test morning
Dependencies: ISR_Mini-explorer mbed
Fork of roboticLab_withclass_3_July by
MiniExplorerCoimbra.cpp@8:072a76960e27, 2017-07-10 (annotated)
- Committer:
- Ludwigfr
- Date:
- Mon Jul 10 12:49:07 2017 +0000
- Revision:
- 8:072a76960e27
- Parent:
- 6:0e8db3a23486
- Child:
- 9:1cc27f33d3e1
just in case of rollback
Who changed what in which revision?
User | Revision | Line number | New contents of line |
---|---|---|---|
Ludwigfr | 0:9f7ee7ed13e4 | 1 | #include "MiniExplorerCoimbra.hpp" |
Ludwigfr | 0:9f7ee7ed13e4 | 2 | #include "robot.h" |
Ludwigfr | 0:9f7ee7ed13e4 | 3 | |
Ludwigfr | 0:9f7ee7ed13e4 | 4 | #define PI 3.14159 |
Ludwigfr | 0:9f7ee7ed13e4 | 5 | |
Ludwigfr | 8:072a76960e27 | 6 | MiniExplorerCoimbra::MiniExplorerCoimbra(float defaultXWorld, float defaultYWorld, float defaultThetaWorld, float widthRealMap, float heightRealMap):map(widthRealMap,heightRealMap,12,8),sonarLeft(10*PI/36,-4,4),sonarFront(0,0,5),sonarRight(-10*PI/36,4,4){ |
Ludwigfr | 0:9f7ee7ed13e4 | 7 | i2c1.frequency(100000); |
Ludwigfr | 0:9f7ee7ed13e4 | 8 | initRobot(); //Initializing the robot |
Ludwigfr | 0:9f7ee7ed13e4 | 9 | pc.baud(9600); // baud for the pc communication |
Ludwigfr | 0:9f7ee7ed13e4 | 10 | |
Ludwigfr | 0:9f7ee7ed13e4 | 11 | measure_always_on();//TODO check if needed |
Ludwigfr | 0:9f7ee7ed13e4 | 12 | |
Ludwigfr | 0:9f7ee7ed13e4 | 13 | this->setXYThetaAndXYThetaWorld(defaultXWorld,defaultYWorld,defaultThetaWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 14 | this->radiusWheels=3.25; |
Ludwigfr | 0:9f7ee7ed13e4 | 15 | this->distanceWheels=7.2; |
Ludwigfr | 0:9f7ee7ed13e4 | 16 | |
Ludwigfr | 8:072a76960e27 | 17 | //go to point |
geotsam | 1:20f48907c726 | 18 | this->k_linear=10; |
geotsam | 1:20f48907c726 | 19 | this->k_angular=200; |
Ludwigfr | 8:072a76960e27 | 20 | |
Ludwigfr | 8:072a76960e27 | 21 | //go to point with angle |
Ludwigfr | 8:072a76960e27 | 22 | this->khro=0.3; |
Ludwigfr | 8:072a76960e27 | 23 | this->ka=0.8; |
Ludwigfr | 8:072a76960e27 | 24 | this->kb=-0.36; |
Ludwigfr | 8:072a76960e27 | 25 | this->kv=200;//velocity |
Ludwigfr | 8:072a76960e27 | 26 | |
Ludwigfr | 8:072a76960e27 | 27 | //go to line kh > 0, kd > 0 (200,5), dont turn to line enough, (10,10) turn on itself, |
Ludwigfr | 8:072a76960e27 | 28 | this->kh=650; |
Ludwigfr | 8:072a76960e27 | 29 | this->kd=10;//previous 5 |
Ludwigfr | 8:072a76960e27 | 30 | |
geotsam | 1:20f48907c726 | 31 | this->speed=300; |
Ludwigfr | 0:9f7ee7ed13e4 | 32 | |
Ludwigfr | 3:37345c109dfc | 33 | this->rangeForce=50; |
Ludwigfr | 5:19f24c363418 | 34 | //not too bad values 200 and -40000 |
Ludwigfr | 5:19f24c363418 | 35 | //not too bad values 200 and -20000 |
Ludwigfr | 5:19f24c363418 | 36 | //not too bad values 500 and -20000 |
Ludwigfr | 5:19f24c363418 | 37 | //not too bad values 500 and -25000 rangeForce 50 |
Ludwigfr | 8:072a76960e27 | 38 | this->attractionConstantForce=1; |
Ludwigfr | 8:072a76960e27 | 39 | this->repulsionConstantForce=-90;//-90 ok //idea make repulsion less divaded by distance |
Ludwigfr | 5:19f24c363418 | 40 | |
Ludwigfr | 5:19f24c363418 | 41 | this->covariancePositionEstimationK[0][0]=0; |
Ludwigfr | 5:19f24c363418 | 42 | this->covariancePositionEstimationK[0][1]=0; |
Ludwigfr | 5:19f24c363418 | 43 | this->covariancePositionEstimationK[0][2]=0; |
Ludwigfr | 5:19f24c363418 | 44 | |
Ludwigfr | 5:19f24c363418 | 45 | this->covariancePositionEstimationK[1][0]=0; |
Ludwigfr | 5:19f24c363418 | 46 | this->covariancePositionEstimationK[1][1]=0; |
Ludwigfr | 5:19f24c363418 | 47 | this->covariancePositionEstimationK[1][2]=0; |
Ludwigfr | 5:19f24c363418 | 48 | |
Ludwigfr | 5:19f24c363418 | 49 | this->covariancePositionEstimationK[2][0]=0; |
Ludwigfr | 5:19f24c363418 | 50 | this->covariancePositionEstimationK[2][1]=0; |
Ludwigfr | 5:19f24c363418 | 51 | this->covariancePositionEstimationK[2][2]=0; |
Ludwigfr | 5:19f24c363418 | 52 | |
Ludwigfr | 0:9f7ee7ed13e4 | 53 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 54 | |
Ludwigfr | 0:9f7ee7ed13e4 | 55 | void MiniExplorerCoimbra::setXYThetaAndXYThetaWorld(float defaultXWorld, float defaultYWorld, float defaultThetaWorld){ |
Ludwigfr | 0:9f7ee7ed13e4 | 56 | this->xWorld=defaultXWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 57 | this->yWorld=defaultYWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 58 | this->thetaWorld=defaultThetaWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 59 | X=defaultYWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 60 | Y=-defaultXWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 61 | if(defaultThetaWorld < -PI/2) |
Ludwigfr | 0:9f7ee7ed13e4 | 62 | theta=PI/2+PI-defaultThetaWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 63 | else |
Ludwigfr | 0:9f7ee7ed13e4 | 64 | theta=defaultThetaWorld-PI/2; |
Ludwigfr | 0:9f7ee7ed13e4 | 65 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 66 | |
Ludwigfr | 0:9f7ee7ed13e4 | 67 | void MiniExplorerCoimbra::myOdometria(){ |
Ludwigfr | 0:9f7ee7ed13e4 | 68 | Odometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 69 | this->xWorld=-Y; |
Ludwigfr | 0:9f7ee7ed13e4 | 70 | this->yWorld=X; |
Ludwigfr | 0:9f7ee7ed13e4 | 71 | if(theta >PI/2) |
Ludwigfr | 0:9f7ee7ed13e4 | 72 | this->thetaWorld=-PI+(theta-PI/2); |
Ludwigfr | 0:9f7ee7ed13e4 | 73 | else |
Ludwigfr | 0:9f7ee7ed13e4 | 74 | this->thetaWorld=theta+PI/2; |
Ludwigfr | 0:9f7ee7ed13e4 | 75 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 76 | |
geotsam | 1:20f48907c726 | 77 | void MiniExplorerCoimbra::go_to_point(float targetXWorld, float targetYWorld) { |
geotsam | 1:20f48907c726 | 78 | |
geotsam | 1:20f48907c726 | 79 | float angleError; //angle error |
geotsam | 1:20f48907c726 | 80 | float d; //distance from target |
geotsam | 1:20f48907c726 | 81 | float angularLeft, angularRight, linear, angular; |
geotsam | 1:20f48907c726 | 82 | int speed=300; |
geotsam | 1:20f48907c726 | 83 | |
geotsam | 1:20f48907c726 | 84 | do { |
geotsam | 1:20f48907c726 | 85 | //Updating X,Y and theta with the odometry values |
geotsam | 1:20f48907c726 | 86 | this->myOdometria(); |
geotsam | 1:20f48907c726 | 87 | |
geotsam | 1:20f48907c726 | 88 | //Computing angle error and distance towards the target value |
geotsam | 1:20f48907c726 | 89 | angleError = atan2((targetYWorld-this->yWorld),(targetXWorld-this->xWorld))-this->thetaWorld; |
geotsam | 1:20f48907c726 | 90 | if(angleError>PI) angleError=-(angleError-PI); |
geotsam | 1:20f48907c726 | 91 | else if(angleError<-PI) angleError=-(angleError+PI); |
geotsam | 1:20f48907c726 | 92 | pc.printf("\n\r error=%f",angleError); |
geotsam | 1:20f48907c726 | 93 | |
geotsam | 1:20f48907c726 | 94 | d=this->dist(this->xWorld, this->yWorld, targetXWorld, targetYWorld); |
geotsam | 1:20f48907c726 | 95 | pc.printf("\n\r dist=%f/n", d); |
geotsam | 1:20f48907c726 | 96 | |
geotsam | 1:20f48907c726 | 97 | //Computing linear and angular velocities |
geotsam | 1:20f48907c726 | 98 | linear=k_linear*d; |
geotsam | 1:20f48907c726 | 99 | angular=k_angular*angleError; |
geotsam | 1:20f48907c726 | 100 | angularLeft=(linear-0.5*this->distanceWheels*angular)/this->radiusWheels; |
geotsam | 1:20f48907c726 | 101 | angularRight=(linear+0.5*this->distanceWheels*angular)/this->radiusWheels; |
geotsam | 1:20f48907c726 | 102 | |
geotsam | 1:20f48907c726 | 103 | |
geotsam | 1:20f48907c726 | 104 | //Normalize speed for motors |
geotsam | 1:20f48907c726 | 105 | if(angularLeft>angularRight) { |
geotsam | 1:20f48907c726 | 106 | angularRight=speed*angularRight/angularLeft; |
geotsam | 1:20f48907c726 | 107 | angularLeft=speed; |
geotsam | 1:20f48907c726 | 108 | } else { |
geotsam | 1:20f48907c726 | 109 | angularLeft=speed*angularLeft/angularRight; |
geotsam | 1:20f48907c726 | 110 | angularRight=speed; |
geotsam | 1:20f48907c726 | 111 | } |
geotsam | 1:20f48907c726 | 112 | |
geotsam | 1:20f48907c726 | 113 | pc.printf("\n\r X=%f", this->xWorld); |
geotsam | 1:20f48907c726 | 114 | pc.printf("\n\r Y=%f", this->yWorld); |
geotsam | 1:20f48907c726 | 115 | pc.printf("\n\r theta=%f", this->thetaWorld); |
geotsam | 1:20f48907c726 | 116 | |
geotsam | 1:20f48907c726 | 117 | //Updating motor velocities |
geotsam | 1:20f48907c726 | 118 | if(angularLeft>0){ |
geotsam | 1:20f48907c726 | 119 | leftMotor(1,angularLeft); |
geotsam | 1:20f48907c726 | 120 | } |
geotsam | 1:20f48907c726 | 121 | else{ |
geotsam | 1:20f48907c726 | 122 | leftMotor(0,-angularLeft); |
geotsam | 1:20f48907c726 | 123 | } |
geotsam | 1:20f48907c726 | 124 | |
geotsam | 1:20f48907c726 | 125 | if(angularRight>0){ |
geotsam | 1:20f48907c726 | 126 | rightMotor(1,angularRight); |
geotsam | 1:20f48907c726 | 127 | } |
geotsam | 1:20f48907c726 | 128 | else{ |
geotsam | 1:20f48907c726 | 129 | rightMotor(0,-angularRight); |
geotsam | 1:20f48907c726 | 130 | } |
geotsam | 1:20f48907c726 | 131 | |
Ludwigfr | 5:19f24c363418 | 132 | //wait(0.5); |
geotsam | 1:20f48907c726 | 133 | } while(d>1); |
geotsam | 1:20f48907c726 | 134 | |
geotsam | 1:20f48907c726 | 135 | //Stop at the end |
geotsam | 1:20f48907c726 | 136 | leftMotor(1,0); |
geotsam | 1:20f48907c726 | 137 | rightMotor(1,0); |
geotsam | 1:20f48907c726 | 138 | } |
geotsam | 1:20f48907c726 | 139 | |
Ludwigfr | 2:11cd5173aa36 | 140 | void MiniExplorerCoimbra::test_procedure_lab2(int nbIteration){ |
Ludwigfr | 2:11cd5173aa36 | 141 | for(int i=0;i<nbIteration;i++){ |
Ludwigfr | 2:11cd5173aa36 | 142 | this->randomize_and_map(); |
Ludwigfr | 5:19f24c363418 | 143 | //this->print_map_with_robot_position(); |
Ludwigfr | 2:11cd5173aa36 | 144 | } |
Ludwigfr | 3:37345c109dfc | 145 | while(1) |
Ludwigfr | 3:37345c109dfc | 146 | this->print_map_with_robot_position(); |
Ludwigfr | 2:11cd5173aa36 | 147 | } |
Ludwigfr | 2:11cd5173aa36 | 148 | |
Ludwigfr | 0:9f7ee7ed13e4 | 149 | //generate a position randomly and makes the robot go there while updating the map |
Ludwigfr | 0:9f7ee7ed13e4 | 150 | void MiniExplorerCoimbra::randomize_and_map() { |
Ludwigfr | 0:9f7ee7ed13e4 | 151 | //TODO check that it's aurelien's work |
Ludwigfr | 2:11cd5173aa36 | 152 | float movementOnX=rand()%(int)(this->map.widthRealMap); |
Ludwigfr | 2:11cd5173aa36 | 153 | float movementOnY=rand()%(int)(this->map.heightRealMap); |
Ludwigfr | 3:37345c109dfc | 154 | |
Ludwigfr | 3:37345c109dfc | 155 | float targetXWorld = movementOnX; |
Ludwigfr | 3:37345c109dfc | 156 | float targetYWorld = movementOnY; |
Ludwigfr | 3:37345c109dfc | 157 | float targetAngleWorld = 2*((float)(rand()%31416)-15708)/10000.0; |
Ludwigfr | 0:9f7ee7ed13e4 | 158 | |
Ludwigfr | 3:37345c109dfc | 159 | //target between (0,0) and (widthRealMap,heightRealMap) |
Ludwigfr | 0:9f7ee7ed13e4 | 160 | this->go_to_point_with_angle(targetXWorld, targetYWorld, targetAngleWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 161 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 162 | |
Ludwigfr | 2:11cd5173aa36 | 163 | void MiniExplorerCoimbra::test_sonars_and_map(int nbIteration){ |
Ludwigfr | 2:11cd5173aa36 | 164 | float leftMm; |
Ludwigfr | 2:11cd5173aa36 | 165 | float frontMm; |
Ludwigfr | 2:11cd5173aa36 | 166 | float rightMm; |
Ludwigfr | 2:11cd5173aa36 | 167 | this->myOdometria(); |
Ludwigfr | 2:11cd5173aa36 | 168 | this->print_map_with_robot_position(); |
Ludwigfr | 2:11cd5173aa36 | 169 | for(int i=0;i<nbIteration;i++){ |
Ludwigfr | 2:11cd5173aa36 | 170 | leftMm = get_distance_left_sensor(); |
Ludwigfr | 2:11cd5173aa36 | 171 | frontMm = get_distance_front_sensor(); |
Ludwigfr | 2:11cd5173aa36 | 172 | rightMm = get_distance_right_sensor(); |
Ludwigfr | 3:37345c109dfc | 173 | pc.printf("\n\r 1 leftMm= %f",leftMm); |
Ludwigfr | 3:37345c109dfc | 174 | pc.printf("\n\r 1 frontMm= %f",frontMm); |
Ludwigfr | 3:37345c109dfc | 175 | pc.printf("\n\r 1 rightMm= %f",rightMm); |
Ludwigfr | 2:11cd5173aa36 | 176 | this->update_sonar_values(leftMm, frontMm, rightMm); |
Ludwigfr | 2:11cd5173aa36 | 177 | this->print_map_with_robot_position(); |
Ludwigfr | 3:37345c109dfc | 178 | wait(1); |
Ludwigfr | 2:11cd5173aa36 | 179 | } |
Ludwigfr | 2:11cd5173aa36 | 180 | } |
Ludwigfr | 2:11cd5173aa36 | 181 | |
Ludwigfr | 2:11cd5173aa36 | 182 | |
Ludwigfr | 2:11cd5173aa36 | 183 | //generate a position randomly and makes the robot go there while updating the map |
Ludwigfr | 0:9f7ee7ed13e4 | 184 | //move of targetXWorld and targetYWorld ending in a targetAngleWorld |
Ludwigfr | 0:9f7ee7ed13e4 | 185 | void MiniExplorerCoimbra::go_to_point_with_angle(float targetXWorld, float targetYWorld, float targetAngleWorld) { |
Ludwigfr | 0:9f7ee7ed13e4 | 186 | bool keepGoing=true; |
Ludwigfr | 0:9f7ee7ed13e4 | 187 | float leftMm; |
Ludwigfr | 0:9f7ee7ed13e4 | 188 | float frontMm; |
Ludwigfr | 0:9f7ee7ed13e4 | 189 | float rightMm; |
Ludwigfr | 0:9f7ee7ed13e4 | 190 | float dt; |
Ludwigfr | 0:9f7ee7ed13e4 | 191 | Timer t; |
Ludwigfr | 0:9f7ee7ed13e4 | 192 | float distanceToTarget; |
Ludwigfr | 0:9f7ee7ed13e4 | 193 | do { |
Ludwigfr | 0:9f7ee7ed13e4 | 194 | //Timer stuff |
Ludwigfr | 0:9f7ee7ed13e4 | 195 | dt = t.read(); |
Ludwigfr | 0:9f7ee7ed13e4 | 196 | t.reset(); |
Ludwigfr | 0:9f7ee7ed13e4 | 197 | t.start(); |
Ludwigfr | 0:9f7ee7ed13e4 | 198 | |
Ludwigfr | 0:9f7ee7ed13e4 | 199 | //Updating X,Y and theta with the odometry values |
Ludwigfr | 0:9f7ee7ed13e4 | 200 | this->myOdometria(); |
Ludwigfr | 2:11cd5173aa36 | 201 | leftMm = get_distance_left_sensor(); |
Ludwigfr | 2:11cd5173aa36 | 202 | frontMm = get_distance_front_sensor(); |
Ludwigfr | 2:11cd5173aa36 | 203 | rightMm = get_distance_right_sensor(); |
Ludwigfr | 2:11cd5173aa36 | 204 | //if in dangerzone 150 mm |
Ludwigfr | 0:9f7ee7ed13e4 | 205 | if((frontMm < 150 && frontMm > 0)|| (leftMm <150 && leftMm > 0) || (rightMm <150 && rightMm > 0) ){ |
Ludwigfr | 2:11cd5173aa36 | 206 | //stop motors |
Ludwigfr | 0:9f7ee7ed13e4 | 207 | leftMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 208 | rightMotor(1,0); |
Ludwigfr | 2:11cd5173aa36 | 209 | //update the map |
Ludwigfr | 0:9f7ee7ed13e4 | 210 | this->update_sonar_values(leftMm, frontMm, rightMm); |
Ludwigfr | 0:9f7ee7ed13e4 | 211 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 212 | keepGoing=false; |
Ludwigfr | 2:11cd5173aa36 | 213 | this->do_half_flip(); |
Ludwigfr | 0:9f7ee7ed13e4 | 214 | }else{ |
Ludwigfr | 0:9f7ee7ed13e4 | 215 | //if not in danger zone continue as usual |
Ludwigfr | 0:9f7ee7ed13e4 | 216 | this->update_sonar_values(leftMm, frontMm, rightMm); |
Ludwigfr | 2:11cd5173aa36 | 217 | //Updating motor velocities |
Ludwigfr | 0:9f7ee7ed13e4 | 218 | distanceToTarget=this->update_angular_speed_wheels_go_to_point_with_angle(targetXWorld,targetYWorld,targetAngleWorld,dt); |
Ludwigfr | 5:19f24c363418 | 219 | //wait(0.2); |
Ludwigfr | 0:9f7ee7ed13e4 | 220 | //Timer stuff |
Ludwigfr | 0:9f7ee7ed13e4 | 221 | t.stop(); |
Ludwigfr | 2:11cd5173aa36 | 222 | pc.printf("\n\rdist to target= %f",distanceToTarget); |
Ludwigfr | 0:9f7ee7ed13e4 | 223 | } |
Ludwigfr | 3:37345c109dfc | 224 | } while((distanceToTarget>2 || (abs(targetAngleWorld-this->thetaWorld)>PI/3)) && keepGoing); |
Ludwigfr | 0:9f7ee7ed13e4 | 225 | |
Ludwigfr | 0:9f7ee7ed13e4 | 226 | //Stop at the end |
Ludwigfr | 0:9f7ee7ed13e4 | 227 | leftMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 228 | rightMotor(1,0); |
Ludwigfr | 2:11cd5173aa36 | 229 | pc.printf("\r\nReached Target!"); |
Ludwigfr | 0:9f7ee7ed13e4 | 230 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 231 | |
geotsam | 1:20f48907c726 | 232 | //move of targetXWorld and targetYWorld ending in a targetAngleWorld |
geotsam | 1:20f48907c726 | 233 | void MiniExplorerCoimbra::go_to_point_with_angle_first_lab(float targetXWorld, float targetYWorld, float targetAngleWorld) { |
geotsam | 1:20f48907c726 | 234 | float dt; |
geotsam | 1:20f48907c726 | 235 | Timer t; |
geotsam | 1:20f48907c726 | 236 | float distanceToTarget; |
geotsam | 1:20f48907c726 | 237 | do { |
geotsam | 1:20f48907c726 | 238 | //Timer stuff |
geotsam | 1:20f48907c726 | 239 | dt = t.read(); |
geotsam | 1:20f48907c726 | 240 | t.reset(); |
geotsam | 1:20f48907c726 | 241 | t.start(); |
geotsam | 1:20f48907c726 | 242 | |
geotsam | 1:20f48907c726 | 243 | //Updating X,Y and theta with the odometry values |
geotsam | 1:20f48907c726 | 244 | this->myOdometria(); |
geotsam | 1:20f48907c726 | 245 | |
geotsam | 1:20f48907c726 | 246 | //Updating motor velocities |
geotsam | 1:20f48907c726 | 247 | distanceToTarget=this->update_angular_speed_wheels_go_to_point_with_angle(targetXWorld,targetYWorld,targetAngleWorld,dt); |
geotsam | 1:20f48907c726 | 248 | |
Ludwigfr | 5:19f24c363418 | 249 | //wait(0.2); |
geotsam | 1:20f48907c726 | 250 | //Timer stuff |
geotsam | 1:20f48907c726 | 251 | t.stop(); |
geotsam | 1:20f48907c726 | 252 | pc.printf("\n\rdist to target= %f",distanceToTarget); |
geotsam | 1:20f48907c726 | 253 | |
geotsam | 1:20f48907c726 | 254 | } while(distanceToTarget>1 || (abs(targetAngleWorld-this->thetaWorld)>0.1)); |
geotsam | 1:20f48907c726 | 255 | |
geotsam | 1:20f48907c726 | 256 | //Stop at the end |
geotsam | 1:20f48907c726 | 257 | leftMotor(1,0); |
geotsam | 1:20f48907c726 | 258 | rightMotor(1,0); |
geotsam | 1:20f48907c726 | 259 | pc.printf("\r\nReached Target!"); |
geotsam | 1:20f48907c726 | 260 | } |
geotsam | 1:20f48907c726 | 261 | |
Ludwigfr | 0:9f7ee7ed13e4 | 262 | float MiniExplorerCoimbra::update_angular_speed_wheels_go_to_point_with_angle(float targetXWorld, float targetYWorld, float targetAngleWorld, float dt){ |
Ludwigfr | 0:9f7ee7ed13e4 | 263 | //compute_angles_and_distance |
Ludwigfr | 0:9f7ee7ed13e4 | 264 | //atan2 take the deplacement on x and the deplacement on y as parameters |
Ludwigfr | 0:9f7ee7ed13e4 | 265 | float angleToPoint = atan2((targetYWorld-this->yWorld),(targetXWorld-this->xWorld))-this->thetaWorld; |
geotsam | 1:20f48907c726 | 266 | |
geotsam | 1:20f48907c726 | 267 | if(angleToPoint>PI) angleToPoint=-(angleToPoint-PI); |
Ludwigfr | 3:37345c109dfc | 268 | else if(angleToPoint<-PI) angleToPoint=-(angleToPoint+PI); |
geotsam | 1:20f48907c726 | 269 | |
Ludwigfr | 3:37345c109dfc | 270 | //rho is the distance to the point of arrival |
Ludwigfr | 0:9f7ee7ed13e4 | 271 | float rho = dist(targetXWorld,targetYWorld,this->xWorld,this->yWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 272 | float distanceToTarget = rho; |
Ludwigfr | 0:9f7ee7ed13e4 | 273 | //TODO check that |
Ludwigfr | 0:9f7ee7ed13e4 | 274 | float beta = targetAngleWorld-angleToPoint-this->thetaWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 275 | |
Ludwigfr | 0:9f7ee7ed13e4 | 276 | //Computing angle error and distance towards the target value |
Ludwigfr | 0:9f7ee7ed13e4 | 277 | rho += dt*(-this->khro*cos(angleToPoint)*rho); |
Ludwigfr | 0:9f7ee7ed13e4 | 278 | float temp = angleToPoint; |
Ludwigfr | 0:9f7ee7ed13e4 | 279 | angleToPoint += dt*(this->khro*sin(angleToPoint)-this->ka*angleToPoint-this->kb*beta); |
Ludwigfr | 0:9f7ee7ed13e4 | 280 | beta += dt*(-this->khro*sin(temp)); |
Ludwigfr | 0:9f7ee7ed13e4 | 281 | |
Ludwigfr | 0:9f7ee7ed13e4 | 282 | //Computing linear and angular velocities |
Ludwigfr | 0:9f7ee7ed13e4 | 283 | float linear; |
Ludwigfr | 0:9f7ee7ed13e4 | 284 | float angular; |
Ludwigfr | 0:9f7ee7ed13e4 | 285 | if(angleToPoint>=-1.5708 && angleToPoint<=1.5708){ |
Ludwigfr | 0:9f7ee7ed13e4 | 286 | linear=this->khro*rho; |
Ludwigfr | 0:9f7ee7ed13e4 | 287 | angular=this->ka*angleToPoint+this->kb*beta; |
Ludwigfr | 0:9f7ee7ed13e4 | 288 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 289 | else{ |
Ludwigfr | 0:9f7ee7ed13e4 | 290 | linear=-this->khro*rho; |
Ludwigfr | 0:9f7ee7ed13e4 | 291 | angular=-this->ka*angleToPoint-this->kb*beta; |
Ludwigfr | 0:9f7ee7ed13e4 | 292 | } |
geotsam | 1:20f48907c726 | 293 | |
geotsam | 1:20f48907c726 | 294 | float angularLeft=(linear-0.5*this->distanceWheels*angular)/this->radiusWheels; |
geotsam | 1:20f48907c726 | 295 | float angularRight=(linear+0.5*this->distanceWheels*angular)/this->radiusWheels; |
geotsam | 1:20f48907c726 | 296 | |
geotsam | 1:20f48907c726 | 297 | //Slowing down at the end for more precision |
geotsam | 1:20f48907c726 | 298 | if (distanceToTarget<30) { |
geotsam | 1:20f48907c726 | 299 | this->speed = distanceToTarget*10; |
geotsam | 1:20f48907c726 | 300 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 301 | |
Ludwigfr | 0:9f7ee7ed13e4 | 302 | //Normalize speed for motors |
geotsam | 1:20f48907c726 | 303 | if(angularLeft>angularRight) { |
geotsam | 1:20f48907c726 | 304 | angularRight=this->speed*angularRight/angularLeft; |
geotsam | 1:20f48907c726 | 305 | angularLeft=this->speed; |
Ludwigfr | 0:9f7ee7ed13e4 | 306 | } else { |
geotsam | 1:20f48907c726 | 307 | angularLeft=this->speed*angularLeft/angularRight; |
geotsam | 1:20f48907c726 | 308 | angularRight=this->speed; |
Ludwigfr | 0:9f7ee7ed13e4 | 309 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 310 | |
Ludwigfr | 0:9f7ee7ed13e4 | 311 | //compute_linear_angular_velocities |
geotsam | 1:20f48907c726 | 312 | leftMotor(1,angularLeft); |
geotsam | 1:20f48907c726 | 313 | rightMotor(1,angularRight); |
Ludwigfr | 0:9f7ee7ed13e4 | 314 | |
Ludwigfr | 0:9f7ee7ed13e4 | 315 | return distanceToTarget; |
Ludwigfr | 0:9f7ee7ed13e4 | 316 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 317 | |
Ludwigfr | 0:9f7ee7ed13e4 | 318 | void MiniExplorerCoimbra::update_sonar_values(float leftMm,float frontMm,float rightMm){ |
Ludwigfr | 0:9f7ee7ed13e4 | 319 | float xWorldCell; |
Ludwigfr | 0:9f7ee7ed13e4 | 320 | float yWorldCell; |
Ludwigfr | 3:37345c109dfc | 321 | float probaLeft; |
Ludwigfr | 3:37345c109dfc | 322 | float probaFront; |
Ludwigfr | 3:37345c109dfc | 323 | float probaRight; |
Ludwigfr | 3:37345c109dfc | 324 | float leftCm=leftMm/10; |
Ludwigfr | 3:37345c109dfc | 325 | float frontCm=frontMm/10; |
Ludwigfr | 3:37345c109dfc | 326 | float rightCm=rightMm/10; |
Ludwigfr | 0:9f7ee7ed13e4 | 327 | for(int i=0;i<this->map.nbCellWidth;i++){ |
Ludwigfr | 0:9f7ee7ed13e4 | 328 | for(int j=0;j<this->map.nbCellHeight;j++){ |
Ludwigfr | 0:9f7ee7ed13e4 | 329 | xWorldCell=this->map.cell_width_coordinate_to_world(i); |
Ludwigfr | 0:9f7ee7ed13e4 | 330 | yWorldCell=this->map.cell_height_coordinate_to_world(j); |
Ludwigfr | 3:37345c109dfc | 331 | |
Ludwigfr | 3:37345c109dfc | 332 | probaLeft=this->sonarLeft.compute_probability_t(leftCm,xWorldCell,yWorldCell,this->xWorld,this->yWorld,this->thetaWorld); |
Ludwigfr | 3:37345c109dfc | 333 | probaFront=this->sonarFront.compute_probability_t(frontCm,xWorldCell,yWorldCell,this->xWorld,this->yWorld,this->thetaWorld); |
Ludwigfr | 3:37345c109dfc | 334 | probaRight=this->sonarRight.compute_probability_t(rightCm,xWorldCell,yWorldCell,this->xWorld,this->yWorld,this->thetaWorld); |
Ludwigfr | 3:37345c109dfc | 335 | |
Ludwigfr | 3:37345c109dfc | 336 | /* |
Ludwigfr | 3:37345c109dfc | 337 | pc.printf("\n\r leftCm= %f",leftCm); |
Ludwigfr | 3:37345c109dfc | 338 | pc.printf("\n\r frontCm= %f",frontCm); |
Ludwigfr | 3:37345c109dfc | 339 | pc.printf("\n\r rightCm= %f",rightCm); |
Ludwigfr | 3:37345c109dfc | 340 | */ |
Ludwigfr | 3:37345c109dfc | 341 | /* |
Ludwigfr | 3:37345c109dfc | 342 | pc.printf("\n\r probaLeft= %f",probaLeft); |
Ludwigfr | 3:37345c109dfc | 343 | pc.printf("\n\r probaFront= %f",probaFront); |
Ludwigfr | 3:37345c109dfc | 344 | pc.printf("\n\r probaRight= %f",probaRight); |
Ludwigfr | 3:37345c109dfc | 345 | if(probaLeft> 1 || probaLeft < 0 || probaFront> 1 || probaFront < 0 ||probaRight> 1 || probaRight < 0)){ |
Ludwigfr | 3:37345c109dfc | 346 | pwm_buzzer.pulsewidth_us(250); |
Ludwigfr | 3:37345c109dfc | 347 | wait_ms(50); |
Ludwigfr | 3:37345c109dfc | 348 | pwm_buzzer.pulsewidth_us(0); |
Ludwigfr | 3:37345c109dfc | 349 | wait(20); |
Ludwigfr | 3:37345c109dfc | 350 | pwm_buzzer.pulsewidth_us(250); |
Ludwigfr | 3:37345c109dfc | 351 | wait_ms(50); |
Ludwigfr | 3:37345c109dfc | 352 | pwm_buzzer.pulsewidth_us(0); |
Ludwigfr | 3:37345c109dfc | 353 | } |
Ludwigfr | 3:37345c109dfc | 354 | */ |
Ludwigfr | 3:37345c109dfc | 355 | this->map.update_cell_value(i,j,probaLeft); |
Ludwigfr | 3:37345c109dfc | 356 | this->map.update_cell_value(i,j,probaFront); |
Ludwigfr | 3:37345c109dfc | 357 | this->map.update_cell_value(i,j,probaRight); |
Ludwigfr | 0:9f7ee7ed13e4 | 358 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 359 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 360 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 361 | |
Ludwigfr | 0:9f7ee7ed13e4 | 362 | void MiniExplorerCoimbra::do_half_flip(){ |
Ludwigfr | 0:9f7ee7ed13e4 | 363 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 364 | float theta_plus_h_pi=theta+PI/2;//theta is between -PI and PI |
Ludwigfr | 0:9f7ee7ed13e4 | 365 | if(theta_plus_h_pi > PI) |
Ludwigfr | 0:9f7ee7ed13e4 | 366 | theta_plus_h_pi=-(2*PI-theta_plus_h_pi); |
Ludwigfr | 0:9f7ee7ed13e4 | 367 | leftMotor(0,100); |
Ludwigfr | 0:9f7ee7ed13e4 | 368 | rightMotor(1,100); |
Ludwigfr | 0:9f7ee7ed13e4 | 369 | while(abs(theta_plus_h_pi-theta)>0.05){ |
Ludwigfr | 0:9f7ee7ed13e4 | 370 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 371 | // pc.printf("\n\r diff=%f", abs(theta_plus_pi-theta)); |
Ludwigfr | 0:9f7ee7ed13e4 | 372 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 373 | leftMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 374 | rightMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 375 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 376 | |
Ludwigfr | 0:9f7ee7ed13e4 | 377 | //Distance computation function |
Ludwigfr | 0:9f7ee7ed13e4 | 378 | float MiniExplorerCoimbra::dist(float x1, float y1, float x2, float y2){ |
Ludwigfr | 0:9f7ee7ed13e4 | 379 | return sqrt(pow(y2-y1,2) + pow(x2-x1,2)); |
Ludwigfr | 0:9f7ee7ed13e4 | 380 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 381 | |
Ludwigfr | 0:9f7ee7ed13e4 | 382 | //use virtual force field |
Ludwigfr | 0:9f7ee7ed13e4 | 383 | void MiniExplorerCoimbra::try_to_reach_target(float targetXWorld,float targetYWorld){ |
Ludwigfr | 0:9f7ee7ed13e4 | 384 | //atan2 gives the angle beetween PI and -PI |
Ludwigfr | 0:9f7ee7ed13e4 | 385 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 386 | /* |
Ludwigfr | 0:9f7ee7ed13e4 | 387 | float deplacementOnXWorld=targetXWorld-this->xWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 388 | float deplacementOnYWorld=targetYWorld-this->yWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 389 | */ |
Ludwigfr | 3:37345c109dfc | 390 | //float angleToTarget=atan2(targetYWorld-this->yWorld,targetXWorld-this->xWorld); |
Ludwigfr | 3:37345c109dfc | 391 | //pc.printf("\n angleToTarget=%f",angleToTarget); |
Ludwigfr | 3:37345c109dfc | 392 | //turn_to_target(angleToTarget); |
Ludwigfr | 3:37345c109dfc | 393 | //TODO IDEA check if maybe set a low max range |
Ludwigfr | 3:37345c109dfc | 394 | //this->sonarLeft.setMaxRange(30); |
Ludwigfr | 3:37345c109dfc | 395 | //this->sonarFront.setMaxRange(30); |
Ludwigfr | 3:37345c109dfc | 396 | //this->sonarRight.setMaxRange(30); |
Ludwigfr | 0:9f7ee7ed13e4 | 397 | bool reached=false; |
Ludwigfr | 0:9f7ee7ed13e4 | 398 | int print=0; |
Ludwigfr | 3:37345c109dfc | 399 | int printLimit=1000; |
Ludwigfr | 0:9f7ee7ed13e4 | 400 | while (!reached) { |
Ludwigfr | 0:9f7ee7ed13e4 | 401 | this->vff(&reached,targetXWorld,targetYWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 402 | //test_got_to_line(&reached); |
Ludwigfr | 3:37345c109dfc | 403 | if(print==printLimit){ |
Ludwigfr | 0:9f7ee7ed13e4 | 404 | leftMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 405 | rightMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 406 | this->print_map_with_robot_position_and_target(targetXWorld,targetYWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 407 | print=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 408 | }else |
Ludwigfr | 0:9f7ee7ed13e4 | 409 | print+=1; |
Ludwigfr | 0:9f7ee7ed13e4 | 410 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 411 | //Stop at the end |
Ludwigfr | 0:9f7ee7ed13e4 | 412 | leftMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 413 | rightMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 414 | pc.printf("\r\n target reached"); |
Ludwigfr | 5:19f24c363418 | 415 | //wait(3);// |
Ludwigfr | 0:9f7ee7ed13e4 | 416 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 417 | |
Ludwigfr | 0:9f7ee7ed13e4 | 418 | void MiniExplorerCoimbra::vff(bool* reached, float targetXWorld, float targetYWorld){ |
Ludwigfr | 0:9f7ee7ed13e4 | 419 | float line_a; |
Ludwigfr | 0:9f7ee7ed13e4 | 420 | float line_b; |
Ludwigfr | 0:9f7ee7ed13e4 | 421 | float line_c; |
Ludwigfr | 0:9f7ee7ed13e4 | 422 | //Updating X,Y and theta with the odometry values |
Ludwigfr | 0:9f7ee7ed13e4 | 423 | float forceXWorld=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 424 | float forceYWorld=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 425 | //we update the odometrie |
Ludwigfr | 0:9f7ee7ed13e4 | 426 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 427 | //we check the sensors |
Ludwigfr | 0:9f7ee7ed13e4 | 428 | float leftMm = get_distance_left_sensor(); |
Ludwigfr | 0:9f7ee7ed13e4 | 429 | float frontMm = get_distance_front_sensor(); |
Ludwigfr | 0:9f7ee7ed13e4 | 430 | float rightMm = get_distance_right_sensor(); |
Ludwigfr | 0:9f7ee7ed13e4 | 431 | //update the probabilities values |
Ludwigfr | 0:9f7ee7ed13e4 | 432 | this->update_sonar_values(leftMm, frontMm, rightMm); |
Ludwigfr | 8:072a76960e27 | 433 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 434 | //we compute the force on X and Y |
Ludwigfr | 0:9f7ee7ed13e4 | 435 | this->compute_forceX_and_forceY(&forceXWorld, &forceYWorld,targetXWorld,targetYWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 436 | //we compute a new ine |
Ludwigfr | 0:9f7ee7ed13e4 | 437 | this->calculate_line(forceXWorld, forceYWorld, &line_a,&line_b,&line_c); |
Ludwigfr | 0:9f7ee7ed13e4 | 438 | //Updating motor velocities |
Ludwigfr | 8:072a76960e27 | 439 | //pc.printf("\r\nX=%f;Y=%f",xWorld,yWorld); |
Ludwigfr | 8:072a76960e27 | 440 | //pc.printf("\r\n%f*x+%f*y+%f=0",line_a,line_b,line_c); |
Ludwigfr | 0:9f7ee7ed13e4 | 441 | this->go_to_line(line_a,line_b,line_c,targetXWorld,targetYWorld); |
Ludwigfr | 3:37345c109dfc | 442 | |
Ludwigfr | 0:9f7ee7ed13e4 | 443 | //wait(0.1); |
Ludwigfr | 0:9f7ee7ed13e4 | 444 | this->myOdometria(); |
Ludwigfr | 3:37345c109dfc | 445 | if(dist(this->xWorld,this->yWorld,targetXWorld,targetYWorld)<3) |
Ludwigfr | 0:9f7ee7ed13e4 | 446 | *reached=true; |
Ludwigfr | 0:9f7ee7ed13e4 | 447 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 448 | |
Ludwigfr | 0:9f7ee7ed13e4 | 449 | /*angleToTarget is obtained through atan2 so it s: |
Ludwigfr | 0:9f7ee7ed13e4 | 450 | < 0 if the angle is bettween PI and 2pi on a trigo circle |
Ludwigfr | 0:9f7ee7ed13e4 | 451 | > 0 if it is between 0 and PI |
Ludwigfr | 0:9f7ee7ed13e4 | 452 | */ |
Ludwigfr | 0:9f7ee7ed13e4 | 453 | void MiniExplorerCoimbra::turn_to_target(float angleToTarget){ |
Ludwigfr | 0:9f7ee7ed13e4 | 454 | this->myOdometria(); |
Ludwigfr | 3:37345c109dfc | 455 | if(angleToTarget!=0){ |
Ludwigfr | 3:37345c109dfc | 456 | if(angleToTarget>0){ |
Ludwigfr | 3:37345c109dfc | 457 | leftMotor(0,1); |
Ludwigfr | 3:37345c109dfc | 458 | rightMotor(1,1); |
Ludwigfr | 3:37345c109dfc | 459 | }else{ |
Ludwigfr | 3:37345c109dfc | 460 | leftMotor(1,1); |
Ludwigfr | 3:37345c109dfc | 461 | rightMotor(0,1); |
Ludwigfr | 3:37345c109dfc | 462 | } |
Ludwigfr | 3:37345c109dfc | 463 | while(abs(angleToTarget-this->thetaWorld)>0.05) |
Ludwigfr | 3:37345c109dfc | 464 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 465 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 466 | leftMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 467 | rightMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 468 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 469 | |
Ludwigfr | 0:9f7ee7ed13e4 | 470 | |
Ludwigfr | 0:9f7ee7ed13e4 | 471 | void MiniExplorerCoimbra::print_map_with_robot_position_and_target(float targetXWorld, float targetYWorld) { |
Ludwigfr | 0:9f7ee7ed13e4 | 472 | float currProba; |
Ludwigfr | 0:9f7ee7ed13e4 | 473 | |
Ludwigfr | 0:9f7ee7ed13e4 | 474 | float heightIndiceInOrthonormal; |
Ludwigfr | 0:9f7ee7ed13e4 | 475 | float widthIndiceInOrthonormal; |
Ludwigfr | 0:9f7ee7ed13e4 | 476 | |
Ludwigfr | 0:9f7ee7ed13e4 | 477 | float widthMalus=-(3*this->map.sizeCellWidth/2); |
Ludwigfr | 0:9f7ee7ed13e4 | 478 | float widthBonus=this->map.sizeCellWidth/2; |
Ludwigfr | 0:9f7ee7ed13e4 | 479 | |
Ludwigfr | 0:9f7ee7ed13e4 | 480 | float heightMalus=-(3*this->map.sizeCellHeight/2); |
Ludwigfr | 0:9f7ee7ed13e4 | 481 | float heightBonus=this->map.sizeCellHeight/2; |
Ludwigfr | 0:9f7ee7ed13e4 | 482 | |
Ludwigfr | 0:9f7ee7ed13e4 | 483 | pc.printf("\n\r"); |
Ludwigfr | 0:9f7ee7ed13e4 | 484 | for (int y = this->map.nbCellHeight -1; y>-1; y--) { |
Ludwigfr | 0:9f7ee7ed13e4 | 485 | for (int x= 0; x<this->map.nbCellWidth; x++) { |
Ludwigfr | 0:9f7ee7ed13e4 | 486 | heightIndiceInOrthonormal=this->map.cell_height_coordinate_to_world(y); |
Ludwigfr | 0:9f7ee7ed13e4 | 487 | widthIndiceInOrthonormal=this->map.cell_width_coordinate_to_world(x); |
Ludwigfr | 0:9f7ee7ed13e4 | 488 | if(this->yWorld >= (heightIndiceInOrthonormal+heightMalus) && this->yWorld <= (heightIndiceInOrthonormal+heightBonus) && this->xWorld >= (widthIndiceInOrthonormal+widthMalus) && this->xWorld <= (widthIndiceInOrthonormal+widthBonus)) |
Ludwigfr | 0:9f7ee7ed13e4 | 489 | pc.printf(" R "); |
Ludwigfr | 0:9f7ee7ed13e4 | 490 | else{ |
Ludwigfr | 0:9f7ee7ed13e4 | 491 | if(targetYWorld >= (heightIndiceInOrthonormal+heightMalus) && targetYWorld <= (heightIndiceInOrthonormal+heightBonus) && targetXWorld >= (widthIndiceInOrthonormal+widthMalus) && targetXWorld <= (widthIndiceInOrthonormal+widthBonus)) |
Ludwigfr | 0:9f7ee7ed13e4 | 492 | pc.printf(" T "); |
Ludwigfr | 0:9f7ee7ed13e4 | 493 | else{ |
Ludwigfr | 0:9f7ee7ed13e4 | 494 | currProba=this->map.log_to_proba(this->map.cellsLogValues[x][y]); |
Ludwigfr | 5:19f24c363418 | 495 | if ( currProba < 0.5){ |
Ludwigfr | 0:9f7ee7ed13e4 | 496 | pc.printf(" "); |
Ludwigfr | 5:19f24c363418 | 497 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 498 | }else{ |
Ludwigfr | 5:19f24c363418 | 499 | if(currProba==0.5){ |
Ludwigfr | 0:9f7ee7ed13e4 | 500 | pc.printf(" . "); |
Ludwigfr | 5:19f24c363418 | 501 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 502 | }else{ |
Ludwigfr | 0:9f7ee7ed13e4 | 503 | pc.printf(" X "); |
Ludwigfr | 5:19f24c363418 | 504 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 505 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 506 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 507 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 508 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 509 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 510 | pc.printf("\n\r"); |
Ludwigfr | 0:9f7ee7ed13e4 | 511 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 512 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 513 | |
Ludwigfr | 2:11cd5173aa36 | 514 | void MiniExplorerCoimbra::print_map_with_robot_position(){ |
Ludwigfr | 2:11cd5173aa36 | 515 | float currProba; |
Ludwigfr | 2:11cd5173aa36 | 516 | |
Ludwigfr | 2:11cd5173aa36 | 517 | float heightIndiceInOrthonormal; |
Ludwigfr | 2:11cd5173aa36 | 518 | float widthIndiceInOrthonormal; |
Ludwigfr | 2:11cd5173aa36 | 519 | |
Ludwigfr | 2:11cd5173aa36 | 520 | float widthMalus=-(3*this->map.sizeCellWidth/2); |
Ludwigfr | 2:11cd5173aa36 | 521 | float widthBonus=this->map.sizeCellWidth/2; |
Ludwigfr | 2:11cd5173aa36 | 522 | |
Ludwigfr | 2:11cd5173aa36 | 523 | float heightMalus=-(3*this->map.sizeCellHeight/2); |
Ludwigfr | 2:11cd5173aa36 | 524 | float heightBonus=this->map.sizeCellHeight/2; |
Ludwigfr | 2:11cd5173aa36 | 525 | |
Ludwigfr | 2:11cd5173aa36 | 526 | pc.printf("\n\r"); |
Ludwigfr | 2:11cd5173aa36 | 527 | for (int y = this->map.nbCellHeight -1; y>-1; y--) { |
Ludwigfr | 2:11cd5173aa36 | 528 | for (int x= 0; x<this->map.nbCellWidth; x++) { |
Ludwigfr | 2:11cd5173aa36 | 529 | heightIndiceInOrthonormal=this->map.cell_height_coordinate_to_world(y); |
Ludwigfr | 2:11cd5173aa36 | 530 | widthIndiceInOrthonormal=this->map.cell_width_coordinate_to_world(x); |
Ludwigfr | 5:19f24c363418 | 531 | if(this->yWorld >= (heightIndiceInOrthonormal+heightMalus) && this->yWorld <= (heightIndiceInOrthonormal+heightBonus) && this->xWorld >= (widthIndiceInOrthonormal+widthMalus) && this->xWorld <= (widthIndiceInOrthonormal+widthBonus)){ |
Ludwigfr | 2:11cd5173aa36 | 532 | pc.printf(" R "); |
Ludwigfr | 5:19f24c363418 | 533 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 534 | }else{ |
Ludwigfr | 2:11cd5173aa36 | 535 | currProba=this->map.log_to_proba(this->map.cellsLogValues[x][y]); |
Ludwigfr | 5:19f24c363418 | 536 | if ( currProba < 0.5){ |
Ludwigfr | 2:11cd5173aa36 | 537 | pc.printf(" "); |
Ludwigfr | 5:19f24c363418 | 538 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 539 | }else{ |
Ludwigfr | 5:19f24c363418 | 540 | if(currProba==0.5){ |
Ludwigfr | 2:11cd5173aa36 | 541 | pc.printf(" . "); |
Ludwigfr | 5:19f24c363418 | 542 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 543 | }else{ |
Ludwigfr | 5:19f24c363418 | 544 | pc.printf(" X "); |
Ludwigfr | 5:19f24c363418 | 545 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 546 | } |
Ludwigfr | 2:11cd5173aa36 | 547 | } |
Ludwigfr | 2:11cd5173aa36 | 548 | } |
Ludwigfr | 2:11cd5173aa36 | 549 | } |
Ludwigfr | 2:11cd5173aa36 | 550 | pc.printf("\n\r"); |
Ludwigfr | 2:11cd5173aa36 | 551 | } |
Ludwigfr | 2:11cd5173aa36 | 552 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 553 | |
Ludwigfr | 0:9f7ee7ed13e4 | 554 | //robotX and robotY are from this->myOdometria(), calculate line_a, line_b and line_c |
Ludwigfr | 0:9f7ee7ed13e4 | 555 | void MiniExplorerCoimbra::calculate_line(float forceX, float forceY, float *line_a, float *line_b, float *line_c){ |
Ludwigfr | 0:9f7ee7ed13e4 | 556 | /* |
Ludwigfr | 0:9f7ee7ed13e4 | 557 | in the teachers maths it is |
Ludwigfr | 0:9f7ee7ed13e4 | 558 | |
Ludwigfr | 0:9f7ee7ed13e4 | 559 | *line_a=forceY; |
Ludwigfr | 0:9f7ee7ed13e4 | 560 | *line_b=-forceX; |
Ludwigfr | 0:9f7ee7ed13e4 | 561 | |
Ludwigfr | 0:9f7ee7ed13e4 | 562 | because a*x+b*y+c=0 |
Ludwigfr | 0:9f7ee7ed13e4 | 563 | a impact the vertical and b the horizontal |
Ludwigfr | 0:9f7ee7ed13e4 | 564 | and he has to put them like this because |
Ludwigfr | 0:9f7ee7ed13e4 | 565 | Robot space: World space: |
Ludwigfr | 0:9f7ee7ed13e4 | 566 | ^ ^ |
Ludwigfr | 0:9f7ee7ed13e4 | 567 | |x |y |
Ludwigfr | 0:9f7ee7ed13e4 | 568 | <- R O -> |
Ludwigfr | 0:9f7ee7ed13e4 | 569 | y x |
Ludwigfr | 0:9f7ee7ed13e4 | 570 | but since our forceX, forceY are already computed in the orthonormal space I m not sure we need to |
Ludwigfr | 0:9f7ee7ed13e4 | 571 | */ |
Ludwigfr | 3:37345c109dfc | 572 | //*line_a=forceX; |
Ludwigfr | 3:37345c109dfc | 573 | //*line_b=forceY; |
Ludwigfr | 3:37345c109dfc | 574 | |
Ludwigfr | 3:37345c109dfc | 575 | *line_a=forceY; |
Ludwigfr | 3:37345c109dfc | 576 | *line_b=-forceX; |
Ludwigfr | 0:9f7ee7ed13e4 | 577 | //because the line computed always pass by the robot center we dont need lince_c |
Ludwigfr | 8:072a76960e27 | 578 | *line_c=forceX*this->yWorld+forceY*this->xWorld; |
Ludwigfr | 8:072a76960e27 | 579 | //*line_c=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 580 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 581 | //compute the force on X and Y |
Ludwigfr | 0:9f7ee7ed13e4 | 582 | void MiniExplorerCoimbra::compute_forceX_and_forceY(float* forceXWorld, float* forceYWorld, float targetXWorld, float targetYWorld){ |
Ludwigfr | 0:9f7ee7ed13e4 | 583 | float forceRepulsionComputedX=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 584 | float forceRepulsionComputedY=0; |
Ludwigfr | 8:072a76960e27 | 585 | this->print_map_with_robot_position(); |
Ludwigfr | 8:072a76960e27 | 586 | for(int i=0;i<this->map.nbCellWidth;i++){ //for each cell of the map we compute a force of repulsion |
Ludwigfr | 0:9f7ee7ed13e4 | 587 | for(int j=0;j<this->map.nbCellHeight;j++){ |
Ludwigfr | 0:9f7ee7ed13e4 | 588 | this->update_force(i,j,&forceRepulsionComputedX,&forceRepulsionComputedY); |
Ludwigfr | 0:9f7ee7ed13e4 | 589 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 590 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 591 | //update with attraction force |
Ludwigfr | 3:37345c109dfc | 592 | *forceXWorld=forceRepulsionComputedX; |
Ludwigfr | 3:37345c109dfc | 593 | *forceYWorld=forceRepulsionComputedY; |
Ludwigfr | 8:072a76960e27 | 594 | //this->print_map_with_robot_position(); |
Ludwigfr | 8:072a76960e27 | 595 | //pc.printf("\r\nForce X repul:%f",*forceXWorld); |
Ludwigfr | 8:072a76960e27 | 596 | //pc.printf("\r\nForce Y repul:%f",*forceYWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 597 | float distanceTargetRobot=sqrt(pow(targetXWorld-this->xWorld,2)+pow(targetYWorld-this->yWorld,2)); |
Ludwigfr | 0:9f7ee7ed13e4 | 598 | if(distanceTargetRobot != 0){ |
Ludwigfr | 8:072a76960e27 | 599 | *forceXWorld+=this->attractionConstantForce*(targetXWorld-this->xWorld)/distanceTargetRobot; |
Ludwigfr | 8:072a76960e27 | 600 | *forceYWorld+=this->attractionConstantForce*(targetYWorld-this->yWorld)/distanceTargetRobot; |
Ludwigfr | 3:37345c109dfc | 601 | }else{ |
Ludwigfr | 8:072a76960e27 | 602 | *forceXWorld+=this->attractionConstantForce*(targetXWorld-this->xWorld)/0.001; |
Ludwigfr | 8:072a76960e27 | 603 | *forceYWorld+=this->attractionConstantForce*(targetYWorld-this->yWorld)/0.001; |
Ludwigfr | 0:9f7ee7ed13e4 | 604 | } |
Ludwigfr | 8:072a76960e27 | 605 | //pc.printf("\r\nForce X after attract:%f",*forceXWorld); |
Ludwigfr | 8:072a76960e27 | 606 | //pc.printf("\r\nForce Y after attract:%f",*forceYWorld); |
Ludwigfr | 3:37345c109dfc | 607 | |
Ludwigfr | 0:9f7ee7ed13e4 | 608 | float amplitude=sqrt(pow(*forceXWorld,2)+pow(*forceYWorld,2)); |
Ludwigfr | 0:9f7ee7ed13e4 | 609 | if(amplitude!=0){//avoid division by 0 if forceX and forceY == 0 |
Ludwigfr | 0:9f7ee7ed13e4 | 610 | *forceXWorld=*forceXWorld/amplitude; |
Ludwigfr | 0:9f7ee7ed13e4 | 611 | *forceYWorld=*forceYWorld/amplitude; |
Ludwigfr | 3:37345c109dfc | 612 | }else{ |
Ludwigfr | 8:072a76960e27 | 613 | *forceXWorld=*forceXWorld/0.001; |
Ludwigfr | 8:072a76960e27 | 614 | *forceYWorld=*forceYWorld/0.001; |
Ludwigfr | 0:9f7ee7ed13e4 | 615 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 616 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 617 | |
Ludwigfr | 2:11cd5173aa36 | 618 | //for vff |
Ludwigfr | 0:9f7ee7ed13e4 | 619 | void MiniExplorerCoimbra::go_to_line(float line_a, float line_b, float line_c,float targetXWorld, float targetYWorld){ |
Ludwigfr | 0:9f7ee7ed13e4 | 620 | float lineAngle; |
Ludwigfr | 0:9f7ee7ed13e4 | 621 | float angleError; |
Ludwigfr | 0:9f7ee7ed13e4 | 622 | float linear; |
Ludwigfr | 0:9f7ee7ed13e4 | 623 | float angular; |
Ludwigfr | 2:11cd5173aa36 | 624 | float d; |
Ludwigfr | 0:9f7ee7ed13e4 | 625 | |
Ludwigfr | 0:9f7ee7ed13e4 | 626 | //line angle is beetween pi/2 and -pi/2 |
Ludwigfr | 2:11cd5173aa36 | 627 | |
Ludwigfr | 2:11cd5173aa36 | 628 | if(line_b!=0){ |
Ludwigfr | 2:11cd5173aa36 | 629 | lineAngle=atan(line_a/-line_b); |
Ludwigfr | 0:9f7ee7ed13e4 | 630 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 631 | else{ |
Ludwigfr | 3:37345c109dfc | 632 | lineAngle=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 633 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 634 | |
Ludwigfr | 2:11cd5173aa36 | 635 | this->myOdometria(); |
Ludwigfr | 2:11cd5173aa36 | 636 | //Computing angle error |
Ludwigfr | 2:11cd5173aa36 | 637 | angleError = lineAngle-this->thetaWorld;//TODO that I m not sure |
Ludwigfr | 2:11cd5173aa36 | 638 | if(angleError>PI) |
Ludwigfr | 2:11cd5173aa36 | 639 | angleError=-(angleError-PI); |
Ludwigfr | 2:11cd5173aa36 | 640 | else |
Ludwigfr | 2:11cd5173aa36 | 641 | if(angleError<-PI) |
Ludwigfr | 2:11cd5173aa36 | 642 | angleError=-(angleError+PI); |
Ludwigfr | 2:11cd5173aa36 | 643 | |
Ludwigfr | 3:37345c109dfc | 644 | //d=this->distFromLine(this->xWorld, this->yWorld, line_a, line_b, line_c);//this could be 0 |
Ludwigfr | 3:37345c109dfc | 645 | d=0; |
Ludwigfr | 8:072a76960e27 | 646 | //pc.printf("\r\ndistance from line:%f",d); |
Ludwigfr | 0:9f7ee7ed13e4 | 647 | //Calculating velocities |
Ludwigfr | 2:11cd5173aa36 | 648 | linear= this->kv*(3.14); |
Ludwigfr | 2:11cd5173aa36 | 649 | angular=-this->kd*d + this->kh*angleError; |
Ludwigfr | 0:9f7ee7ed13e4 | 650 | |
Ludwigfr | 0:9f7ee7ed13e4 | 651 | float angularLeft=(linear-0.5*this->distanceWheels*angular)/this->radiusWheels; |
Ludwigfr | 2:11cd5173aa36 | 652 | float angularRight=(linear+0.5*this->distanceWheels*angular)/this->radiusWheels; |
Ludwigfr | 0:9f7ee7ed13e4 | 653 | |
Ludwigfr | 2:11cd5173aa36 | 654 | //Normalize speed for motors |
Ludwigfr | 0:9f7ee7ed13e4 | 655 | if(abs(angularLeft)>abs(angularRight)) { |
Ludwigfr | 0:9f7ee7ed13e4 | 656 | angularRight=this->speed*abs(angularRight/angularLeft)*this->sign1(angularRight); |
Ludwigfr | 0:9f7ee7ed13e4 | 657 | angularLeft=this->speed*this->sign1(angularLeft); |
Ludwigfr | 0:9f7ee7ed13e4 | 658 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 659 | else { |
Ludwigfr | 0:9f7ee7ed13e4 | 660 | angularLeft=this->speed*abs(angularLeft/angularRight)*this->sign1(angularLeft); |
Ludwigfr | 0:9f7ee7ed13e4 | 661 | angularRight=this->speed*this->sign1(angularRight); |
Ludwigfr | 0:9f7ee7ed13e4 | 662 | } |
Ludwigfr | 2:11cd5173aa36 | 663 | |
Ludwigfr | 2:11cd5173aa36 | 664 | pc.printf("\r\nd = %f", d); |
Ludwigfr | 3:37345c109dfc | 665 | pc.printf("\r\nerror = %f, lineAngle=%f, robotAngle=%f\n", angleError,lineAngle,this->thetaWorld); |
Ludwigfr | 2:11cd5173aa36 | 666 | |
Ludwigfr | 0:9f7ee7ed13e4 | 667 | leftMotor(this->sign2(angularLeft),abs(angularLeft)); |
Ludwigfr | 0:9f7ee7ed13e4 | 668 | rightMotor(this->sign2(angularRight),abs(angularRight)); |
Ludwigfr | 0:9f7ee7ed13e4 | 669 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 670 | |
geotsam | 1:20f48907c726 | 671 | void MiniExplorerCoimbra::go_to_line_first_lab(float line_a, float line_b, float line_c){ |
geotsam | 1:20f48907c726 | 672 | float lineAngle; |
geotsam | 1:20f48907c726 | 673 | float angleError; |
geotsam | 1:20f48907c726 | 674 | float linear; |
geotsam | 1:20f48907c726 | 675 | float angular; |
geotsam | 1:20f48907c726 | 676 | float d; |
geotsam | 1:20f48907c726 | 677 | |
geotsam | 1:20f48907c726 | 678 | //line angle is beetween pi/2 and -pi/2 |
geotsam | 1:20f48907c726 | 679 | |
geotsam | 1:20f48907c726 | 680 | if(line_b!=0){ |
geotsam | 1:20f48907c726 | 681 | lineAngle=atan(line_a/-line_b); |
geotsam | 1:20f48907c726 | 682 | } |
geotsam | 1:20f48907c726 | 683 | else{ |
geotsam | 1:20f48907c726 | 684 | lineAngle=1.5708; |
geotsam | 1:20f48907c726 | 685 | } |
geotsam | 1:20f48907c726 | 686 | |
geotsam | 1:20f48907c726 | 687 | do{ |
geotsam | 1:20f48907c726 | 688 | this->myOdometria(); |
geotsam | 1:20f48907c726 | 689 | //Computing angle error |
Ludwigfr | 3:37345c109dfc | 690 | pc.printf("\r\nline angle = %f", lineAngle); |
Ludwigfr | 3:37345c109dfc | 691 | pc.printf("\r\nthetaWorld = %f", thetaWorld); |
geotsam | 1:20f48907c726 | 692 | angleError = lineAngle-this->thetaWorld;//TODO that I m not sure |
geotsam | 1:20f48907c726 | 693 | |
Ludwigfr | 3:37345c109dfc | 694 | if(angleError>PI) |
Ludwigfr | 3:37345c109dfc | 695 | angleError=-(angleError-PI); |
Ludwigfr | 3:37345c109dfc | 696 | else |
Ludwigfr | 3:37345c109dfc | 697 | if(angleError<-PI) |
Ludwigfr | 3:37345c109dfc | 698 | angleError=-(angleError+PI); |
geotsam | 1:20f48907c726 | 699 | |
Ludwigfr | 3:37345c109dfc | 700 | pc.printf("\r\nangleError = %f\n", angleError); |
geotsam | 1:20f48907c726 | 701 | d=this->distFromLine(xWorld, yWorld, line_a, line_b, line_c); |
Ludwigfr | 3:37345c109dfc | 702 | pc.printf("\r\ndistance to line = %f", d); |
geotsam | 1:20f48907c726 | 703 | |
geotsam | 1:20f48907c726 | 704 | //Calculating velocities |
geotsam | 1:20f48907c726 | 705 | linear= this->kv*(3.14); |
geotsam | 1:20f48907c726 | 706 | angular=-this->kd*d + this->kh*angleError; |
geotsam | 1:20f48907c726 | 707 | |
geotsam | 1:20f48907c726 | 708 | float angularLeft=(linear-0.5*this->distanceWheels*angular)/this->radiusWheels; |
geotsam | 1:20f48907c726 | 709 | float angularRight=(linear+0.5*this->distanceWheels*angular)/this->radiusWheels; |
geotsam | 1:20f48907c726 | 710 | |
geotsam | 1:20f48907c726 | 711 | //Normalize speed for motors |
geotsam | 1:20f48907c726 | 712 | if(abs(angularLeft)>abs(angularRight)) { |
geotsam | 1:20f48907c726 | 713 | angularRight=this->speed*abs(angularRight/angularLeft)*this->sign1(angularRight); |
geotsam | 1:20f48907c726 | 714 | angularLeft=this->speed*this->sign1(angularLeft); |
geotsam | 1:20f48907c726 | 715 | } |
geotsam | 1:20f48907c726 | 716 | else { |
geotsam | 1:20f48907c726 | 717 | angularLeft=this->speed*abs(angularLeft/angularRight)*this->sign1(angularLeft); |
geotsam | 1:20f48907c726 | 718 | angularRight=this->speed*this->sign1(angularRight); |
geotsam | 1:20f48907c726 | 719 | } |
geotsam | 1:20f48907c726 | 720 | |
geotsam | 1:20f48907c726 | 721 | leftMotor(this->sign2(angularLeft),abs(angularLeft)); |
geotsam | 1:20f48907c726 | 722 | rightMotor(this->sign2(angularRight),abs(angularRight)); |
geotsam | 1:20f48907c726 | 723 | }while(1); |
geotsam | 1:20f48907c726 | 724 | } |
geotsam | 1:20f48907c726 | 725 | |
Ludwigfr | 0:9f7ee7ed13e4 | 726 | void MiniExplorerCoimbra::update_force(int widthIndice, int heightIndice, float* forceRepulsionComputedX, float* forceRepulsionComputedY ){ |
Ludwigfr | 0:9f7ee7ed13e4 | 727 | //get the coordonate of the map and the robot in the ortonormal space |
Ludwigfr | 0:9f7ee7ed13e4 | 728 | float xWorldCell=this->map.cell_width_coordinate_to_world(widthIndice); |
Ludwigfr | 0:9f7ee7ed13e4 | 729 | float yWorldCell=this->map.cell_height_coordinate_to_world(heightIndice); |
Ludwigfr | 0:9f7ee7ed13e4 | 730 | //compute the distance beetween the cell and the robot |
Ludwigfr | 0:9f7ee7ed13e4 | 731 | float distanceCellToRobot=sqrt(pow(xWorldCell-this->xWorld,2)+pow(yWorldCell-this->yWorld,2)); |
Ludwigfr | 3:37345c109dfc | 732 | float probaCell; |
Ludwigfr | 0:9f7ee7ed13e4 | 733 | //check if the cell is in range |
Ludwigfr | 8:072a76960e27 | 734 | //float anglePointToRobot=atan2(yWorldCell-this->yWorld,xWorldCell-this->xWorld);//like world system |
Ludwigfr | 3:37345c109dfc | 735 | float temp1; |
Ludwigfr | 3:37345c109dfc | 736 | float temp2; |
Ludwigfr | 0:9f7ee7ed13e4 | 737 | if(distanceCellToRobot <= this->rangeForce) { |
Ludwigfr | 3:37345c109dfc | 738 | probaCell=this->map.get_proba_cell(widthIndice,heightIndice); |
Ludwigfr | 8:072a76960e27 | 739 | //pc.printf("\r\nupdate force proba:%f",probaCell); |
Ludwigfr | 3:37345c109dfc | 740 | temp1=this->repulsionConstantForce*probaCell/pow(distanceCellToRobot,2); |
Ludwigfr | 3:37345c109dfc | 741 | temp2=(xWorldCell-this->xWorld)/distanceCellToRobot; |
Ludwigfr | 3:37345c109dfc | 742 | *forceRepulsionComputedX+=temp1*temp2; |
Ludwigfr | 3:37345c109dfc | 743 | temp2=(yWorldCell-this->yWorld)/distanceCellToRobot; |
Ludwigfr | 3:37345c109dfc | 744 | *forceRepulsionComputedY+=temp1*temp2; |
Ludwigfr | 0:9f7ee7ed13e4 | 745 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 746 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 747 | |
Ludwigfr | 0:9f7ee7ed13e4 | 748 | //return 1 if positiv, -1 if negativ |
Ludwigfr | 0:9f7ee7ed13e4 | 749 | float MiniExplorerCoimbra::sign1(float value){ |
Ludwigfr | 0:9f7ee7ed13e4 | 750 | if(value>=0) |
Ludwigfr | 0:9f7ee7ed13e4 | 751 | return 1; |
Ludwigfr | 0:9f7ee7ed13e4 | 752 | else |
Ludwigfr | 0:9f7ee7ed13e4 | 753 | return -1; |
Ludwigfr | 0:9f7ee7ed13e4 | 754 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 755 | |
Ludwigfr | 0:9f7ee7ed13e4 | 756 | //return 1 if positiv, 0 if negativ |
Ludwigfr | 0:9f7ee7ed13e4 | 757 | int MiniExplorerCoimbra::sign2(float value){ |
Ludwigfr | 0:9f7ee7ed13e4 | 758 | if(value>=0) |
Ludwigfr | 0:9f7ee7ed13e4 | 759 | return 1; |
Ludwigfr | 0:9f7ee7ed13e4 | 760 | else |
Ludwigfr | 0:9f7ee7ed13e4 | 761 | return 0; |
Ludwigfr | 0:9f7ee7ed13e4 | 762 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 763 | |
geotsam | 1:20f48907c726 | 764 | float MiniExplorerCoimbra::distFromLine(float robot_x, float robot_y, float line_a, float line_b, float line_c){ |
geotsam | 1:20f48907c726 | 765 | return abs((line_a*robot_x+line_b*robot_y+line_c)/sqrt(line_a*line_a+line_b*line_b)); |
geotsam | 1:20f48907c726 | 766 | } |
geotsam | 1:20f48907c726 | 767 | |
Ludwigfr | 5:19f24c363418 | 768 | //4th LAB |
Ludwigfr | 5:19f24c363418 | 769 | //starting position lower left |
Ludwigfr | 5:19f24c363418 | 770 | |
Ludwigfr | 5:19f24c363418 | 771 | void MiniExplorerCoimbra::test_procedure_lab_4(float sizeX, float sizeY, int nbRectangle){ |
Ludwigfr | 5:19f24c363418 | 772 | |
Ludwigfr | 5:19f24c363418 | 773 | this->map.fill_map_with_kalman_knowledge(); |
Ludwigfr | 5:19f24c363418 | 774 | |
Ludwigfr | 5:19f24c363418 | 775 | this->go_to_point_with_angle_kalman(this->xWorld+sizeX,this->yWorld,this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 776 | |
Ludwigfr | 5:19f24c363418 | 777 | /* |
Ludwigfr | 5:19f24c363418 | 778 | for(int j=0;j<nbRectangle;j++){ |
Ludwigfr | 5:19f24c363418 | 779 | //right |
Ludwigfr | 5:19f24c363418 | 780 | this->go_to_point_with_angle_kalman(this->xWorld+sizeX,this->yWorld,this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 781 | this->go_turn_kalman(this->xWorld,this->yWorld,this->thetaWorld+PI/2); |
Ludwigfr | 5:19f24c363418 | 782 | |
Ludwigfr | 5:19f24c363418 | 783 | this->print_map_with_robot_position(); |
Ludwigfr | 5:19f24c363418 | 784 | pc.printf("\n\rX= %f",this->xWorld); |
Ludwigfr | 5:19f24c363418 | 785 | pc.printf("\n\rY= %f",this->yWorld); |
Ludwigfr | 5:19f24c363418 | 786 | pc.printf("\n\rtheta= %f",this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 787 | |
Ludwigfr | 5:19f24c363418 | 788 | //up |
Ludwigfr | 5:19f24c363418 | 789 | this->go_to_point_with_angle_kalman(this->xWorld+sizeX,this->yWorld+sizeY,this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 790 | this->go_turn_kalman(this->xWorld,this->yWorld,this->thetaWorld+PI/2); |
Ludwigfr | 5:19f24c363418 | 791 | |
Ludwigfr | 5:19f24c363418 | 792 | this->print_map_with_robot_position(); |
Ludwigfr | 5:19f24c363418 | 793 | pc.printf("\n\rX= %f",this->xWorld); |
Ludwigfr | 5:19f24c363418 | 794 | pc.printf("\n\rY= %f",this->yWorld); |
Ludwigfr | 5:19f24c363418 | 795 | pc.printf("\n\rtheta= %f",this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 796 | //left |
Ludwigfr | 5:19f24c363418 | 797 | this->go_to_point_with_angle_kalman(this->xWorld-sizeX,this->yWorld,this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 798 | this->go_turn_kalman(this->xWorld,this->yWorld,this->thetaWorld+PI/2); |
Ludwigfr | 5:19f24c363418 | 799 | |
Ludwigfr | 5:19f24c363418 | 800 | this->print_map_with_robot_position(); |
Ludwigfr | 5:19f24c363418 | 801 | pc.printf("\n\rX= %f",this->xWorld); |
Ludwigfr | 5:19f24c363418 | 802 | pc.printf("\n\rY= %f",this->yWorld); |
Ludwigfr | 5:19f24c363418 | 803 | pc.printf("\n\rtheta= %f",this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 804 | //down |
Ludwigfr | 5:19f24c363418 | 805 | this->go_to_point_with_angle_kalman(this->xWorld,this->yWorld-sizeY,this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 806 | this->go_turn_kalman(this->xWorld,this->yWorld,this->thetaWorld+PI/2); |
Ludwigfr | 5:19f24c363418 | 807 | |
Ludwigfr | 5:19f24c363418 | 808 | this->print_map_with_robot_position(); |
Ludwigfr | 5:19f24c363418 | 809 | pc.printf("\n\rX= %f",this->xWorld); |
Ludwigfr | 5:19f24c363418 | 810 | pc.printf("\n\rY= %f",this->yWorld); |
Ludwigfr | 5:19f24c363418 | 811 | pc.printf("\n\rtheta= %f",this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 812 | |
Ludwigfr | 5:19f24c363418 | 813 | } |
Ludwigfr | 5:19f24c363418 | 814 | */ |
Ludwigfr | 5:19f24c363418 | 815 | } |
Ludwigfr | 5:19f24c363418 | 816 | |
Ludwigfr | 5:19f24c363418 | 817 | //move of targetXWorld and targetYWorld ending in a targetAngleWorld |
Ludwigfr | 5:19f24c363418 | 818 | void MiniExplorerCoimbra::go_turn_kalman(float targetXWorld, float targetYWorld, float targetAngleWorld) { |
Ludwigfr | 5:19f24c363418 | 819 | //make sure the target is correct |
Ludwigfr | 5:19f24c363418 | 820 | if(targetAngleWorld > PI) |
Ludwigfr | 5:19f24c363418 | 821 | targetAngleWorld=-2*PI+targetAngleWorld; |
Ludwigfr | 5:19f24c363418 | 822 | if(targetAngleWorld < -PI) |
Ludwigfr | 5:19f24c363418 | 823 | targetAngleWorld=2*PI+targetAngleWorld; |
Ludwigfr | 5:19f24c363418 | 824 | |
Ludwigfr | 5:19f24c363418 | 825 | float distanceToTarget=100; |
Ludwigfr | 5:19f24c363418 | 826 | do { |
Ludwigfr | 5:19f24c363418 | 827 | leftMotor(1,50); |
Ludwigfr | 5:19f24c363418 | 828 | rightMotor(0,50); |
Ludwigfr | 6:0e8db3a23486 | 829 | this->OdometriaKalmanFilter(); |
Ludwigfr | 5:19f24c363418 | 830 | |
Ludwigfr | 5:19f24c363418 | 831 | float distanceToTarget=this->dist(this->xWorld, this->yWorld, targetXWorld, targetYWorld); |
Ludwigfr | 5:19f24c363418 | 832 | //pc.printf("\n\rdist to target= %f",distanceToTarget); |
Ludwigfr | 5:19f24c363418 | 833 | |
Ludwigfr | 5:19f24c363418 | 834 | } while(distanceToTarget>1 || (abs(targetAngleWorld-this->thetaWorld)>0.1)); |
Ludwigfr | 5:19f24c363418 | 835 | |
Ludwigfr | 5:19f24c363418 | 836 | //Stop at the end |
Ludwigfr | 5:19f24c363418 | 837 | leftMotor(1,0); |
Ludwigfr | 5:19f24c363418 | 838 | rightMotor(1,0); |
Ludwigfr | 5:19f24c363418 | 839 | pc.printf("\r\nReached Target!"); |
Ludwigfr | 5:19f24c363418 | 840 | } |
Ludwigfr | 5:19f24c363418 | 841 | |
Ludwigfr | 5:19f24c363418 | 842 | //move of targetXWorld and targetYWorld ending in a targetAngleWorld |
Ludwigfr | 5:19f24c363418 | 843 | void MiniExplorerCoimbra::go_straight_kalman(float targetXWorld, float targetYWorld, float targetAngleWorld) { |
Ludwigfr | 5:19f24c363418 | 844 | //make sure the target is correct |
Ludwigfr | 5:19f24c363418 | 845 | if(targetAngleWorld > PI) |
Ludwigfr | 5:19f24c363418 | 846 | targetAngleWorld=-2*PI+targetAngleWorld; |
Ludwigfr | 5:19f24c363418 | 847 | if(targetAngleWorld < -PI) |
Ludwigfr | 5:19f24c363418 | 848 | targetAngleWorld=2*PI+targetAngleWorld; |
Ludwigfr | 5:19f24c363418 | 849 | |
Ludwigfr | 5:19f24c363418 | 850 | float distanceToTarget=100;; |
Ludwigfr | 5:19f24c363418 | 851 | |
Ludwigfr | 5:19f24c363418 | 852 | do { |
Ludwigfr | 5:19f24c363418 | 853 | leftMotor(1,400); |
Ludwigfr | 5:19f24c363418 | 854 | rightMotor(1,400); |
Ludwigfr | 6:0e8db3a23486 | 855 | this->OdometriaKalmanFilter(); |
Ludwigfr | 5:19f24c363418 | 856 | |
Ludwigfr | 5:19f24c363418 | 857 | float distanceToTarget=this->dist(this->xWorld, this->yWorld, targetXWorld, targetYWorld); |
Ludwigfr | 5:19f24c363418 | 858 | pc.printf("\n\rdist to target= %f",distanceToTarget); |
Ludwigfr | 5:19f24c363418 | 859 | |
Ludwigfr | 5:19f24c363418 | 860 | } while(distanceToTarget>1 || (abs(targetAngleWorld-this->thetaWorld)>0.1)); |
Ludwigfr | 5:19f24c363418 | 861 | |
Ludwigfr | 5:19f24c363418 | 862 | //Stop at the end |
Ludwigfr | 5:19f24c363418 | 863 | leftMotor(1,0); |
Ludwigfr | 5:19f24c363418 | 864 | rightMotor(1,0); |
Ludwigfr | 5:19f24c363418 | 865 | pc.printf("\r\nReached Target!"); |
Ludwigfr | 5:19f24c363418 | 866 | } |
Ludwigfr | 5:19f24c363418 | 867 | |
Ludwigfr | 5:19f24c363418 | 868 | //move of targetXWorld and targetYWorld ending in a targetAngleWorld |
Ludwigfr | 5:19f24c363418 | 869 | void MiniExplorerCoimbra::go_to_point_with_angle_kalman(float targetXWorld, float targetYWorld, float targetAngleWorld) { |
Ludwigfr | 5:19f24c363418 | 870 | float dt; |
Ludwigfr | 5:19f24c363418 | 871 | Timer t; |
Ludwigfr | 5:19f24c363418 | 872 | float distanceToTarget; |
Ludwigfr | 5:19f24c363418 | 873 | //make sure the target is correct |
Ludwigfr | 5:19f24c363418 | 874 | if(targetAngleWorld > PI) |
Ludwigfr | 5:19f24c363418 | 875 | targetAngleWorld=-2*PI+targetAngleWorld; |
Ludwigfr | 5:19f24c363418 | 876 | if(targetAngleWorld < -PI) |
Ludwigfr | 5:19f24c363418 | 877 | targetAngleWorld=2*PI+targetAngleWorld; |
Ludwigfr | 5:19f24c363418 | 878 | |
Ludwigfr | 5:19f24c363418 | 879 | do { |
Ludwigfr | 5:19f24c363418 | 880 | //Timer stuff |
Ludwigfr | 5:19f24c363418 | 881 | dt = t.read(); |
Ludwigfr | 5:19f24c363418 | 882 | t.reset(); |
Ludwigfr | 5:19f24c363418 | 883 | t.start(); |
Ludwigfr | 5:19f24c363418 | 884 | |
Ludwigfr | 5:19f24c363418 | 885 | //Updating X,Y and theta with the odometry values |
Ludwigfr | 6:0e8db3a23486 | 886 | this->OdometriaKalmanFilter(); |
Ludwigfr | 5:19f24c363418 | 887 | |
Ludwigfr | 5:19f24c363418 | 888 | //Updating motor velocities |
Ludwigfr | 5:19f24c363418 | 889 | distanceToTarget=this->update_angular_speed_wheels_go_to_point_with_angle(targetXWorld,targetYWorld,targetAngleWorld,dt); |
Ludwigfr | 5:19f24c363418 | 890 | |
Ludwigfr | 5:19f24c363418 | 891 | //wait(0.2); |
Ludwigfr | 5:19f24c363418 | 892 | //Timer stuff |
Ludwigfr | 5:19f24c363418 | 893 | t.stop(); |
Ludwigfr | 5:19f24c363418 | 894 | pc.printf("\n\rdist to target= %f",distanceToTarget); |
Ludwigfr | 5:19f24c363418 | 895 | |
Ludwigfr | 6:0e8db3a23486 | 896 | } while(distanceToTarget>10 || (abs(targetAngleWorld-this->thetaWorld)>0.1)); |
Ludwigfr | 5:19f24c363418 | 897 | |
Ludwigfr | 5:19f24c363418 | 898 | //Stop at the end |
Ludwigfr | 5:19f24c363418 | 899 | leftMotor(1,0); |
Ludwigfr | 5:19f24c363418 | 900 | rightMotor(1,0); |
Ludwigfr | 5:19f24c363418 | 901 | pc.printf("\r\nReached Target!"); |
Ludwigfr | 5:19f24c363418 | 902 | } |
Ludwigfr | 5:19f24c363418 | 903 | |
Ludwigfr | 6:0e8db3a23486 | 904 | void MiniExplorerCoimbra::test_prediction_sonar(){ |
Ludwigfr | 6:0e8db3a23486 | 905 | this->map.fill_map_with_kalman_knowledge(); |
Ludwigfr | 6:0e8db3a23486 | 906 | |
Ludwigfr | 5:19f24c363418 | 907 | //=====KINEMATICS=========================== |
Ludwigfr | 5:19f24c363418 | 908 | float R_cm; |
Ludwigfr | 5:19f24c363418 | 909 | float L_cm; |
Ludwigfr | 5:19f24c363418 | 910 | |
Ludwigfr | 5:19f24c363418 | 911 | //fill R_cm and L_cm with how much is wheel has moved (custom robot.h) |
Ludwigfr | 5:19f24c363418 | 912 | OdometriaKalman(&R_cm, &L_cm); |
Ludwigfr | 5:19f24c363418 | 913 | |
Ludwigfr | 6:0e8db3a23486 | 914 | float encoderRightFailureRate=0.95; |
Ludwigfr | 6:0e8db3a23486 | 915 | float encoderLeftFailureRate=1; |
Ludwigfr | 5:19f24c363418 | 916 | |
Ludwigfr | 5:19f24c363418 | 917 | R_cm=R_cm*encoderRightFailureRate; |
Ludwigfr | 5:19f24c363418 | 918 | L_cm=L_cm*encoderLeftFailureRate; |
Ludwigfr | 5:19f24c363418 | 919 | |
Ludwigfr | 5:19f24c363418 | 920 | float distanceMoved=(R_cm+L_cm)/2; |
Ludwigfr | 5:19f24c363418 | 921 | float angleMoved=(R_cm-L_cm)/this->distanceWheels; |
Ludwigfr | 5:19f24c363418 | 922 | |
Ludwigfr | 5:19f24c363418 | 923 | float distanceMovedX=distanceMoved*cos(this->thetaWorld+angleMoved/2); |
Ludwigfr | 5:19f24c363418 | 924 | float distanceMovedY=distanceMoved*sin(this->thetaWorld+angleMoved/2); |
Ludwigfr | 5:19f24c363418 | 925 | |
Ludwigfr | 5:19f24c363418 | 926 | //try with world coordinate system |
Ludwigfr | 5:19f24c363418 | 927 | |
Ludwigfr | 5:19f24c363418 | 928 | float xEstimatedK=this->xWorld+distanceMovedX; |
Ludwigfr | 5:19f24c363418 | 929 | float yEstimatedK=this->yWorld+distanceMovedY; |
Ludwigfr | 5:19f24c363418 | 930 | float thetaWorldEstimatedK = this->thetaWorld+angleMoved; |
Ludwigfr | 5:19f24c363418 | 931 | |
Ludwigfr | 6:0e8db3a23486 | 932 | pc.printf("\r\n X=%f, Y=%f, theta=%f",xEstimatedK,yEstimatedK,thetaWorldEstimatedK); |
Ludwigfr | 6:0e8db3a23486 | 933 | |
Ludwigfr | 6:0e8db3a23486 | 934 | //try with robot coordinate system |
Ludwigfr | 6:0e8db3a23486 | 935 | /* |
Ludwigfr | 6:0e8db3a23486 | 936 | float xEstimatedK=X; |
Ludwigfr | 6:0e8db3a23486 | 937 | float yEstimatedK=Y; |
Ludwigfr | 6:0e8db3a23486 | 938 | float thetaWorldEstimatedK = theta; |
Ludwigfr | 6:0e8db3a23486 | 939 | */ |
Ludwigfr | 6:0e8db3a23486 | 940 | //=====ERROR_MODEL=========================== |
Ludwigfr | 6:0e8db3a23486 | 941 | |
Ludwigfr | 6:0e8db3a23486 | 942 | //FP Matrix |
Ludwigfr | 6:0e8db3a23486 | 943 | float Fp[3][3]={{1,0,0},{0,1,0},{0,0,1}}; |
Ludwigfr | 6:0e8db3a23486 | 944 | |
Ludwigfr | 6:0e8db3a23486 | 945 | Fp[0][2]=-1*distanceMoved*sin(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 6:0e8db3a23486 | 946 | Fp[1][2]=distanceMoved*cos(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 6:0e8db3a23486 | 947 | |
Ludwigfr | 6:0e8db3a23486 | 948 | //Frl matrix |
Ludwigfr | 6:0e8db3a23486 | 949 | float Frl[3][2]={{0,0},{0,0},{(1/this->distanceWheels),-(1/this->distanceWheels)}}; |
Ludwigfr | 6:0e8db3a23486 | 950 | |
Ludwigfr | 6:0e8db3a23486 | 951 | Frl[0][0]=0.5*cos(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 6:0e8db3a23486 | 952 | Frl[0][1]=0.5*cos(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 6:0e8db3a23486 | 953 | Frl[1][0]=0.5*sin(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 6:0e8db3a23486 | 954 | Frl[1][1]=0.5*sin(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 6:0e8db3a23486 | 955 | |
Ludwigfr | 6:0e8db3a23486 | 956 | //error constants... |
Ludwigfr | 6:0e8db3a23486 | 957 | float kr=1; |
Ludwigfr | 6:0e8db3a23486 | 958 | float kl=1; |
Ludwigfr | 6:0e8db3a23486 | 959 | float covar[2][2]={{kr*abs(R_cm), 0}, {0, kl*abs(L_cm)}}; |
Ludwigfr | 6:0e8db3a23486 | 960 | |
Ludwigfr | 6:0e8db3a23486 | 961 | //uncertainty positionx, and theta at |
Ludwigfr | 6:0e8db3a23486 | 962 | //1,1,1 |
Ludwigfr | 6:0e8db3a23486 | 963 | float Q[3][3]={{1,0,0}, {0,2,0}, {0,0,0.01}}; |
Ludwigfr | 6:0e8db3a23486 | 964 | |
Ludwigfr | 6:0e8db3a23486 | 965 | covariancePositionEstimationK[0][0]=covar[0][0]*pow(Frl[0][0],2)+covar[1][1]*pow(Frl[0][1],2)+covariancePositionEstimationK[0][0]+Q[0][0]+covariancePositionEstimationK[2][0]*Fp[0][2]+Fp[0][2]*(covariancePositionEstimationK[0][2]+covariancePositionEstimationK[2][2]*Fp[0][2]); |
Ludwigfr | 6:0e8db3a23486 | 966 | covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1]+covariancePositionEstimationK[2][1]*Fp[0][2]+Fp[1][2]*(covariancePositionEstimationK[0][2]+covariancePositionEstimationK[2][2]*Fp[0][2])+covar[0][0]*Frl[0][0]*Frl[1][0]+covar[1][1]*Frl[0][1]; |
Ludwigfr | 6:0e8db3a23486 | 967 | covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2]+covariancePositionEstimationK[2][2]*Fp[0][2]+covar[0][0]*Frl[0][0]*Frl[2][0]+covar[1][1]*Frl[0][1]*Frl[2][1]; |
Ludwigfr | 6:0e8db3a23486 | 968 | covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0]+covariancePositionEstimationK[2][0]*Fp[1][2]+Fp[0][2]*(covariancePositionEstimationK[1][2]+covariancePositionEstimationK[2][2]*Fp[1][2])+covar[0][0]*Frl[0][0]*Frl[1][0]+covar[1][1]*Frl[0][1]*Frl[1][1]; |
Ludwigfr | 6:0e8db3a23486 | 969 | covariancePositionEstimationK[1][1]=covar[0][0]*pow(Frl[1][0],2)+covar[1][1]*pow(Frl[1][1],2)+covariancePositionEstimationK[1][1]+Q[1][1]+covariancePositionEstimationK[2][1]*Fp[1][2]+Fp[1][2]*(covariancePositionEstimationK[1][2]+covariancePositionEstimationK[2][2]*Fp[1][2]); |
Ludwigfr | 6:0e8db3a23486 | 970 | covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2]+covariancePositionEstimationK[2][2]*Fp[1][2]+covar[0][0]*Frl[1][0]*Frl[2][0]+covar[1][1]*Frl[1][1]*Frl[2][1]; |
Ludwigfr | 6:0e8db3a23486 | 971 | covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0]+covariancePositionEstimationK[2][2]*Fp[0][2]+covar[0][0]*Frl[0][0]*Frl[2][0]+covar[1][1]*Frl[0][1]*Frl[2][1]; |
Ludwigfr | 6:0e8db3a23486 | 972 | covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1]+covariancePositionEstimationK[2][2]*Fp[1][2]+covar[0][0]*Frl[1][0]*Frl[2][0]+covar[1][1]*Frl[1][1]*Frl[2][1]; |
Ludwigfr | 6:0e8db3a23486 | 973 | covariancePositionEstimationK[2][2]=covar[0][0]*pow(Frl[2][1],2)+covar[1][1]*pow(Frl[2][1],2)+covariancePositionEstimationK[2][2]+Q[2][2]; |
Ludwigfr | 6:0e8db3a23486 | 974 | |
Ludwigfr | 6:0e8db3a23486 | 975 | |
Ludwigfr | 6:0e8db3a23486 | 976 | //=====OBSERVATION===== |
Ludwigfr | 6:0e8db3a23486 | 977 | //get the estimated measure we should have according to our knowledge of the map and the previously computed localisation |
Ludwigfr | 6:0e8db3a23486 | 978 | |
Ludwigfr | 6:0e8db3a23486 | 979 | float leftCmEstimated=this->sonarLeft.maxRange; |
Ludwigfr | 6:0e8db3a23486 | 980 | float frontCmEstimated=this->sonarFront.maxRange; |
Ludwigfr | 6:0e8db3a23486 | 981 | float rightCmEstimated=this->sonarRight.maxRange; |
Ludwigfr | 6:0e8db3a23486 | 982 | float xWorldCell; |
Ludwigfr | 6:0e8db3a23486 | 983 | float yWorldCell; |
Ludwigfr | 6:0e8db3a23486 | 984 | float currDistance; |
Ludwigfr | 6:0e8db3a23486 | 985 | float xClosestCellLeft; |
Ludwigfr | 6:0e8db3a23486 | 986 | float yClosestCellLeft; |
Ludwigfr | 6:0e8db3a23486 | 987 | float xClosestCellFront; |
Ludwigfr | 6:0e8db3a23486 | 988 | float yClosestCellFront; |
Ludwigfr | 6:0e8db3a23486 | 989 | float xClosestCellRight; |
Ludwigfr | 6:0e8db3a23486 | 990 | float yClosestCellRight; |
Ludwigfr | 6:0e8db3a23486 | 991 | //get theorical distance to sonar |
Ludwigfr | 6:0e8db3a23486 | 992 | for(int i=0;i<this->map.nbCellWidth;i++){ |
Ludwigfr | 6:0e8db3a23486 | 993 | for(int j=0;j<this->map.nbCellHeight;j++){ |
Ludwigfr | 6:0e8db3a23486 | 994 | //check if occupied, if not discard |
Ludwigfr | 6:0e8db3a23486 | 995 | if(this->map.get_proba_cell(i,j)>0.5){ |
Ludwigfr | 6:0e8db3a23486 | 996 | //check if in sonar range |
Ludwigfr | 6:0e8db3a23486 | 997 | xWorldCell=this->map.cell_width_coordinate_to_world(i); |
Ludwigfr | 6:0e8db3a23486 | 998 | yWorldCell=this->map.cell_height_coordinate_to_world(j); |
Ludwigfr | 6:0e8db3a23486 | 999 | //check left |
Ludwigfr | 6:0e8db3a23486 | 1000 | currDistance=this->sonarLeft.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK); |
Ludwigfr | 6:0e8db3a23486 | 1001 | if((currDistance < this->sonarLeft.maxRange) && currDistance > -1){ |
Ludwigfr | 6:0e8db3a23486 | 1002 | //check if distance is lower than previous, update lowest if so |
Ludwigfr | 6:0e8db3a23486 | 1003 | if(currDistance < leftCmEstimated){ |
Ludwigfr | 6:0e8db3a23486 | 1004 | leftCmEstimated= currDistance; |
Ludwigfr | 6:0e8db3a23486 | 1005 | xClosestCellLeft=xWorldCell; |
Ludwigfr | 6:0e8db3a23486 | 1006 | yClosestCellLeft=yWorldCell; |
Ludwigfr | 6:0e8db3a23486 | 1007 | } |
Ludwigfr | 6:0e8db3a23486 | 1008 | } |
Ludwigfr | 6:0e8db3a23486 | 1009 | //check front |
Ludwigfr | 6:0e8db3a23486 | 1010 | currDistance=this->sonarFront.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK); |
Ludwigfr | 6:0e8db3a23486 | 1011 | if((currDistance < this->sonarFront.maxRange) && currDistance > -1){ |
Ludwigfr | 6:0e8db3a23486 | 1012 | //check if distance is lower than previous, update lowest if so |
Ludwigfr | 6:0e8db3a23486 | 1013 | if(currDistance < frontCmEstimated){ |
Ludwigfr | 6:0e8db3a23486 | 1014 | frontCmEstimated= currDistance; |
Ludwigfr | 6:0e8db3a23486 | 1015 | xClosestCellFront=xWorldCell; |
Ludwigfr | 6:0e8db3a23486 | 1016 | yClosestCellFront=yWorldCell; |
Ludwigfr | 6:0e8db3a23486 | 1017 | } |
Ludwigfr | 6:0e8db3a23486 | 1018 | } |
Ludwigfr | 6:0e8db3a23486 | 1019 | //check right |
Ludwigfr | 6:0e8db3a23486 | 1020 | currDistance=this->sonarRight.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK); |
Ludwigfr | 6:0e8db3a23486 | 1021 | if((currDistance < this->sonarRight.maxRange) && currDistance > -1){ |
Ludwigfr | 6:0e8db3a23486 | 1022 | //check if distance is lower than previous, update lowest if so |
Ludwigfr | 6:0e8db3a23486 | 1023 | if(currDistance < rightCmEstimated){ |
Ludwigfr | 6:0e8db3a23486 | 1024 | rightCmEstimated= currDistance; |
Ludwigfr | 6:0e8db3a23486 | 1025 | xClosestCellRight=xWorldCell; |
Ludwigfr | 6:0e8db3a23486 | 1026 | yClosestCellRight=yWorldCell; |
Ludwigfr | 6:0e8db3a23486 | 1027 | } |
Ludwigfr | 6:0e8db3a23486 | 1028 | } |
Ludwigfr | 6:0e8db3a23486 | 1029 | } |
Ludwigfr | 6:0e8db3a23486 | 1030 | } |
Ludwigfr | 6:0e8db3a23486 | 1031 | } |
Ludwigfr | 6:0e8db3a23486 | 1032 | |
Ludwigfr | 6:0e8db3a23486 | 1033 | //check measurements from sonars, see if they passed the validation gate |
Ludwigfr | 6:0e8db3a23486 | 1034 | float leftCm = get_distance_left_sensor()/10; |
Ludwigfr | 6:0e8db3a23486 | 1035 | float frontCm = get_distance_front_sensor()/10; |
Ludwigfr | 6:0e8db3a23486 | 1036 | float rightCm = get_distance_right_sensor()/10; |
Ludwigfr | 6:0e8db3a23486 | 1037 | |
Ludwigfr | 6:0e8db3a23486 | 1038 | pc.printf("\r\n1: Lcm=%f, FCm=%f, RCm=%f",leftCm,frontCm,rightCm); |
Ludwigfr | 6:0e8db3a23486 | 1039 | |
Ludwigfr | 6:0e8db3a23486 | 1040 | //if superior to sonar range, put the value to sonar max range + 1 |
Ludwigfr | 6:0e8db3a23486 | 1041 | if(leftCm > this->sonarLeft.maxRange) |
Ludwigfr | 6:0e8db3a23486 | 1042 | leftCm=this->sonarLeft.maxRange; |
Ludwigfr | 6:0e8db3a23486 | 1043 | if(frontCm > this->sonarFront.maxRange) |
Ludwigfr | 6:0e8db3a23486 | 1044 | frontCm=this->sonarFront.maxRange; |
Ludwigfr | 6:0e8db3a23486 | 1045 | if(rightCm > this->sonarRight.maxRange) |
Ludwigfr | 6:0e8db3a23486 | 1046 | rightCm=this->sonarRight.maxRange; |
Ludwigfr | 6:0e8db3a23486 | 1047 | |
Ludwigfr | 6:0e8db3a23486 | 1048 | //======INNOVATION======== |
Ludwigfr | 6:0e8db3a23486 | 1049 | //get the innoncation: the value of the difference between the actual measure and what we anticipated |
Ludwigfr | 6:0e8db3a23486 | 1050 | float innovationLeft=leftCm-leftCmEstimated; |
Ludwigfr | 6:0e8db3a23486 | 1051 | float innovationFront=frontCm-frontCmEstimated; |
Ludwigfr | 6:0e8db3a23486 | 1052 | float innovationRight=rightCm-rightCmEstimated; |
Ludwigfr | 6:0e8db3a23486 | 1053 | |
Ludwigfr | 6:0e8db3a23486 | 1054 | //compute jacobian of observation |
Ludwigfr | 6:0e8db3a23486 | 1055 | float jacobianOfObservationLeft[1][3]; |
Ludwigfr | 6:0e8db3a23486 | 1056 | float jacobianOfObservationRight[1][3]; |
Ludwigfr | 6:0e8db3a23486 | 1057 | float jacobianOfObservationFront[1][3]; |
Ludwigfr | 6:0e8db3a23486 | 1058 | float xSonarLeft=xEstimatedK+this->sonarLeft.distanceX; |
Ludwigfr | 6:0e8db3a23486 | 1059 | float ySonarLeft=yEstimatedK+this->sonarLeft.distanceY; |
Ludwigfr | 6:0e8db3a23486 | 1060 | //left |
Ludwigfr | 6:0e8db3a23486 | 1061 | jacobianOfObservationLeft[0][0]=(xSonarLeft-xClosestCellLeft)/leftCmEstimated; |
Ludwigfr | 6:0e8db3a23486 | 1062 | jacobianOfObservationLeft[0][1]=(ySonarLeft-yClosestCellLeft)/leftCmEstimated; |
Ludwigfr | 6:0e8db3a23486 | 1063 | jacobianOfObservationLeft[0][2]=(xClosestCellLeft-xSonarLeft)*(xSonarLeft*sin(thetaWorldEstimatedK)+ySonarLeft*cos(thetaWorldEstimatedK))+(yClosestCellLeft-ySonarLeft)*(-xSonarLeft*cos(thetaWorldEstimatedK)+ySonarLeft*sin(thetaWorldEstimatedK)); |
Ludwigfr | 6:0e8db3a23486 | 1064 | //front |
Ludwigfr | 6:0e8db3a23486 | 1065 | float xSonarFront=xEstimatedK+this->sonarFront.distanceX; |
Ludwigfr | 6:0e8db3a23486 | 1066 | float ySonarFront=yEstimatedK+this->sonarFront.distanceY; |
Ludwigfr | 6:0e8db3a23486 | 1067 | jacobianOfObservationFront[0][0]=(xSonarFront-xClosestCellFront)/frontCmEstimated; |
Ludwigfr | 6:0e8db3a23486 | 1068 | jacobianOfObservationFront[0][1]=(ySonarFront-yClosestCellFront)/frontCmEstimated; |
Ludwigfr | 6:0e8db3a23486 | 1069 | jacobianOfObservationFront[0][2]=(xClosestCellFront-xSonarFront)*(xSonarFront*sin(thetaWorldEstimatedK)+ySonarFront*cos(thetaWorldEstimatedK))+(yClosestCellFront-ySonarFront)*(-xSonarFront*cos(thetaWorldEstimatedK)+ySonarFront*sin(thetaWorldEstimatedK)); |
Ludwigfr | 6:0e8db3a23486 | 1070 | //right |
Ludwigfr | 6:0e8db3a23486 | 1071 | float xSonarRight=xEstimatedK+this->sonarRight.distanceX; |
Ludwigfr | 6:0e8db3a23486 | 1072 | float ySonarRight=yEstimatedK+this->sonarRight.distanceY; |
Ludwigfr | 6:0e8db3a23486 | 1073 | jacobianOfObservationRight[0][0]=(xSonarRight-xClosestCellRight)/rightCmEstimated; |
Ludwigfr | 6:0e8db3a23486 | 1074 | jacobianOfObservationRight[0][1]=(ySonarRight-yClosestCellRight)/rightCmEstimated; |
Ludwigfr | 6:0e8db3a23486 | 1075 | jacobianOfObservationRight[0][2]=(xClosestCellRight-xSonarRight)*(xSonarRight*sin(thetaWorldEstimatedK)+ySonarRight*cos(thetaWorldEstimatedK))+(yClosestCellRight-ySonarRight)*(-xSonarRight*cos(thetaWorldEstimatedK)+ySonarRight*sin(thetaWorldEstimatedK)); |
Ludwigfr | 6:0e8db3a23486 | 1076 | |
Ludwigfr | 6:0e8db3a23486 | 1077 | //error constants 0,0,0 sonars perfect; must be found by experimenting; gives mean and standanrt deviation |
Ludwigfr | 6:0e8db3a23486 | 1078 | //let's assume |
Ludwigfr | 6:0e8db3a23486 | 1079 | //in centimeters |
Ludwigfr | 6:0e8db3a23486 | 1080 | float R_front=4; |
Ludwigfr | 6:0e8db3a23486 | 1081 | float R_left=4; |
Ludwigfr | 6:0e8db3a23486 | 1082 | float R_right=4; |
Ludwigfr | 6:0e8db3a23486 | 1083 | |
Ludwigfr | 6:0e8db3a23486 | 1084 | //R-> 4 in diagonal |
Ludwigfr | 6:0e8db3a23486 | 1085 | |
Ludwigfr | 6:0e8db3a23486 | 1086 | //S for each sonar (concatenated covariance matrix of innovation) |
Ludwigfr | 6:0e8db3a23486 | 1087 | float innovationCovarianceFront=R_front+ jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]); |
Ludwigfr | 8:072a76960e27 | 1088 | float innovationCovarianceLeft=R_left + jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]); |
Ludwigfr | 6:0e8db3a23486 | 1089 | float innovationCovarianceRight=R_right+ jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]); |
Ludwigfr | 6:0e8db3a23486 | 1090 | |
Ludwigfr | 6:0e8db3a23486 | 1091 | //check if it pass the validation gate |
Ludwigfr | 6:0e8db3a23486 | 1092 | float gateScoreLeft=innovationLeft*innovationLeft/innovationCovarianceLeft; |
Ludwigfr | 6:0e8db3a23486 | 1093 | float gateScoreFront=innovationFront*innovationFront/innovationCovarianceFront; |
Ludwigfr | 6:0e8db3a23486 | 1094 | float gateScoreRight=innovationRight*innovationRight/innovationCovarianceRight; |
Ludwigfr | 6:0e8db3a23486 | 1095 | int leftPassed=0; |
Ludwigfr | 6:0e8db3a23486 | 1096 | int frontPassed=0; |
Ludwigfr | 6:0e8db3a23486 | 1097 | int rightPassed=0; |
Ludwigfr | 6:0e8db3a23486 | 1098 | |
Ludwigfr | 6:0e8db3a23486 | 1099 | //5cm -> 25 |
Ludwigfr | 6:0e8db3a23486 | 1100 | int errorsquare=25;//constant error |
Ludwigfr | 6:0e8db3a23486 | 1101 | |
Ludwigfr | 6:0e8db3a23486 | 1102 | if(gateScoreLeft <= errorsquare) |
Ludwigfr | 6:0e8db3a23486 | 1103 | leftPassed=1; |
Ludwigfr | 6:0e8db3a23486 | 1104 | if(gateScoreFront <= errorsquare) |
Ludwigfr | 6:0e8db3a23486 | 1105 | frontPassed=10; |
Ludwigfr | 6:0e8db3a23486 | 1106 | if(gateScoreRight <= errorsquare) |
Ludwigfr | 6:0e8db3a23486 | 1107 | rightPassed=100; |
Ludwigfr | 6:0e8db3a23486 | 1108 | //for those who passed |
Ludwigfr | 6:0e8db3a23486 | 1109 | //compute composite innovation |
Ludwigfr | 6:0e8db3a23486 | 1110 | int nbPassed=leftPassed+frontPassed+rightPassed; |
Ludwigfr | 6:0e8db3a23486 | 1111 | |
Ludwigfr | 6:0e8db3a23486 | 1112 | |
Ludwigfr | 6:0e8db3a23486 | 1113 | this->print_map_with_robot_position(); |
Ludwigfr | 8:072a76960e27 | 1114 | pc.printf("\r\n E_LCm=%f, E_FCm=%f, E_RCm=%f",leftCmEstimated,frontCmEstimated,rightCmEstimated); |
Ludwigfr | 6:0e8db3a23486 | 1115 | pc.printf("\r\n Lcm=%f, FCm=%f, RCm=%f",leftCm,frontCm,rightCm); |
Ludwigfr | 6:0e8db3a23486 | 1116 | pc.printf("\r\n IL=%f, IF=%f, IR=%f",innovationLeft,innovationFront,innovationRight); |
Ludwigfr | 6:0e8db3a23486 | 1117 | pc.printf("\r\n ICL=%f, ICF=%f, ICR=%f",innovationCovarianceLeft,innovationCovarianceFront,innovationCovarianceRight); |
Ludwigfr | 6:0e8db3a23486 | 1118 | pc.printf("\r\n Gate score L=%f, F=%f, R=%f",gateScoreLeft,gateScoreFront,gateScoreRight); |
Ludwigfr | 6:0e8db3a23486 | 1119 | pc.printf("\r\n nbPassed=%f",nbPassed); |
Ludwigfr | 6:0e8db3a23486 | 1120 | wait(2); |
Ludwigfr | 6:0e8db3a23486 | 1121 | |
Ludwigfr | 6:0e8db3a23486 | 1122 | } |
Ludwigfr | 6:0e8db3a23486 | 1123 | |
Ludwigfr | 6:0e8db3a23486 | 1124 | void MiniExplorerCoimbra::OdometriaKalmanFilter(){ |
Ludwigfr | 6:0e8db3a23486 | 1125 | //=====KINEMATICS=========================== |
Ludwigfr | 6:0e8db3a23486 | 1126 | float R_cm; |
Ludwigfr | 6:0e8db3a23486 | 1127 | float L_cm; |
Ludwigfr | 6:0e8db3a23486 | 1128 | |
Ludwigfr | 6:0e8db3a23486 | 1129 | //fill R_cm and L_cm with how much is wheel has moved (custom robot.h) |
Ludwigfr | 6:0e8db3a23486 | 1130 | OdometriaKalman(&R_cm, &L_cm); |
Ludwigfr | 6:0e8db3a23486 | 1131 | |
Ludwigfr | 6:0e8db3a23486 | 1132 | float encoderRightFailureRate=0.95; |
Ludwigfr | 6:0e8db3a23486 | 1133 | float encoderLeftFailureRate=1; |
Ludwigfr | 6:0e8db3a23486 | 1134 | |
Ludwigfr | 6:0e8db3a23486 | 1135 | R_cm=R_cm*encoderRightFailureRate; |
Ludwigfr | 6:0e8db3a23486 | 1136 | L_cm=L_cm*encoderLeftFailureRate; |
Ludwigfr | 6:0e8db3a23486 | 1137 | |
Ludwigfr | 6:0e8db3a23486 | 1138 | float distanceMoved=(R_cm+L_cm)/2; |
Ludwigfr | 6:0e8db3a23486 | 1139 | float angleMoved=(R_cm-L_cm)/this->distanceWheels; |
Ludwigfr | 6:0e8db3a23486 | 1140 | |
Ludwigfr | 6:0e8db3a23486 | 1141 | float distanceMovedX=distanceMoved*cos(this->thetaWorld+angleMoved/2); |
Ludwigfr | 6:0e8db3a23486 | 1142 | float distanceMovedY=distanceMoved*sin(this->thetaWorld+angleMoved/2); |
Ludwigfr | 6:0e8db3a23486 | 1143 | |
Ludwigfr | 6:0e8db3a23486 | 1144 | //try with world coordinate system |
Ludwigfr | 6:0e8db3a23486 | 1145 | |
Ludwigfr | 6:0e8db3a23486 | 1146 | float xEstimatedK=this->xWorld+distanceMovedX; |
Ludwigfr | 6:0e8db3a23486 | 1147 | float yEstimatedK=this->yWorld+distanceMovedY; |
Ludwigfr | 6:0e8db3a23486 | 1148 | float thetaWorldEstimatedK = this->thetaWorld+angleMoved; |
Ludwigfr | 6:0e8db3a23486 | 1149 | |
Ludwigfr | 6:0e8db3a23486 | 1150 | pc.printf("\r\n X=%f, Y=%f, theta=%f",xEstimatedK,yEstimatedK,thetaWorldEstimatedK); |
Ludwigfr | 6:0e8db3a23486 | 1151 | |
Ludwigfr | 5:19f24c363418 | 1152 | //try with robot coordinate system |
Ludwigfr | 5:19f24c363418 | 1153 | /* |
Ludwigfr | 5:19f24c363418 | 1154 | float xEstimatedK=X; |
Ludwigfr | 5:19f24c363418 | 1155 | float yEstimatedK=Y; |
Ludwigfr | 5:19f24c363418 | 1156 | float thetaWorldEstimatedK = theta; |
Ludwigfr | 5:19f24c363418 | 1157 | */ |
Ludwigfr | 5:19f24c363418 | 1158 | //=====ERROR_MODEL=========================== |
Ludwigfr | 5:19f24c363418 | 1159 | |
Ludwigfr | 5:19f24c363418 | 1160 | //FP Matrix |
Ludwigfr | 5:19f24c363418 | 1161 | float Fp[3][3]={{1,0,0},{0,1,0},{0,0,1}}; |
Ludwigfr | 5:19f24c363418 | 1162 | |
Ludwigfr | 5:19f24c363418 | 1163 | Fp[0][2]=-1*distanceMoved*sin(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 5:19f24c363418 | 1164 | Fp[1][2]=distanceMoved*cos(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 5:19f24c363418 | 1165 | |
Ludwigfr | 5:19f24c363418 | 1166 | //Frl matrix |
Ludwigfr | 5:19f24c363418 | 1167 | float Frl[3][2]={{0,0},{0,0},{(1/this->distanceWheels),-(1/this->distanceWheels)}}; |
Ludwigfr | 5:19f24c363418 | 1168 | |
Ludwigfr | 5:19f24c363418 | 1169 | Frl[0][0]=0.5*cos(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 5:19f24c363418 | 1170 | Frl[0][1]=0.5*cos(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 5:19f24c363418 | 1171 | Frl[1][0]=0.5*sin(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 5:19f24c363418 | 1172 | Frl[1][1]=0.5*sin(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 5:19f24c363418 | 1173 | |
Ludwigfr | 5:19f24c363418 | 1174 | //error constants... |
Ludwigfr | 5:19f24c363418 | 1175 | float kr=1; |
Ludwigfr | 5:19f24c363418 | 1176 | float kl=1; |
Ludwigfr | 5:19f24c363418 | 1177 | float covar[2][2]={{kr*abs(R_cm), 0}, {0, kl*abs(L_cm)}}; |
Ludwigfr | 5:19f24c363418 | 1178 | |
Ludwigfr | 5:19f24c363418 | 1179 | //uncertainty positionx, and theta at |
Ludwigfr | 5:19f24c363418 | 1180 | //1,1,1 |
Ludwigfr | 5:19f24c363418 | 1181 | float Q[3][3]={{1,0,0}, {0,2,0}, {0,0,0.01}}; |
Ludwigfr | 5:19f24c363418 | 1182 | |
Ludwigfr | 5:19f24c363418 | 1183 | covariancePositionEstimationK[0][0]=covar[0][0]*pow(Frl[0][0],2)+covar[1][1]*pow(Frl[0][1],2)+covariancePositionEstimationK[0][0]+Q[0][0]+covariancePositionEstimationK[2][0]*Fp[0][2]+Fp[0][2]*(covariancePositionEstimationK[0][2]+covariancePositionEstimationK[2][2]*Fp[0][2]); |
Ludwigfr | 5:19f24c363418 | 1184 | covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1]+covariancePositionEstimationK[2][1]*Fp[0][2]+Fp[1][2]*(covariancePositionEstimationK[0][2]+covariancePositionEstimationK[2][2]*Fp[0][2])+covar[0][0]*Frl[0][0]*Frl[1][0]+covar[1][1]*Frl[0][1]; |
Ludwigfr | 5:19f24c363418 | 1185 | covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2]+covariancePositionEstimationK[2][2]*Fp[0][2]+covar[0][0]*Frl[0][0]*Frl[2][0]+covar[1][1]*Frl[0][1]*Frl[2][1]; |
Ludwigfr | 5:19f24c363418 | 1186 | covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0]+covariancePositionEstimationK[2][0]*Fp[1][2]+Fp[0][2]*(covariancePositionEstimationK[1][2]+covariancePositionEstimationK[2][2]*Fp[1][2])+covar[0][0]*Frl[0][0]*Frl[1][0]+covar[1][1]*Frl[0][1]*Frl[1][1]; |
Ludwigfr | 5:19f24c363418 | 1187 | covariancePositionEstimationK[1][1]=covar[0][0]*pow(Frl[1][0],2)+covar[1][1]*pow(Frl[1][1],2)+covariancePositionEstimationK[1][1]+Q[1][1]+covariancePositionEstimationK[2][1]*Fp[1][2]+Fp[1][2]*(covariancePositionEstimationK[1][2]+covariancePositionEstimationK[2][2]*Fp[1][2]); |
Ludwigfr | 5:19f24c363418 | 1188 | covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2]+covariancePositionEstimationK[2][2]*Fp[1][2]+covar[0][0]*Frl[1][0]*Frl[2][0]+covar[1][1]*Frl[1][1]*Frl[2][1]; |
Ludwigfr | 5:19f24c363418 | 1189 | covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0]+covariancePositionEstimationK[2][2]*Fp[0][2]+covar[0][0]*Frl[0][0]*Frl[2][0]+covar[1][1]*Frl[0][1]*Frl[2][1]; |
Ludwigfr | 5:19f24c363418 | 1190 | covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1]+covariancePositionEstimationK[2][2]*Fp[1][2]+covar[0][0]*Frl[1][0]*Frl[2][0]+covar[1][1]*Frl[1][1]*Frl[2][1]; |
Ludwigfr | 5:19f24c363418 | 1191 | covariancePositionEstimationK[2][2]=covar[0][0]*pow(Frl[2][1],2)+covar[1][1]*pow(Frl[2][1],2)+covariancePositionEstimationK[2][2]+Q[2][2]; |
Ludwigfr | 5:19f24c363418 | 1192 | |
Ludwigfr | 5:19f24c363418 | 1193 | //=====OBSERVATION===== |
Ludwigfr | 5:19f24c363418 | 1194 | //get the estimated measure we should have according to our knowledge of the map and the previously computed localisation |
Ludwigfr | 5:19f24c363418 | 1195 | |
Ludwigfr | 5:19f24c363418 | 1196 | float leftCmEstimated=this->sonarLeft.maxRange; |
Ludwigfr | 5:19f24c363418 | 1197 | float frontCmEstimated=this->sonarFront.maxRange; |
Ludwigfr | 5:19f24c363418 | 1198 | float rightCmEstimated=this->sonarRight.maxRange; |
Ludwigfr | 5:19f24c363418 | 1199 | float xWorldCell; |
Ludwigfr | 5:19f24c363418 | 1200 | float yWorldCell; |
Ludwigfr | 5:19f24c363418 | 1201 | float currDistance; |
Ludwigfr | 5:19f24c363418 | 1202 | float xClosestCellLeft; |
Ludwigfr | 5:19f24c363418 | 1203 | float yClosestCellLeft; |
Ludwigfr | 5:19f24c363418 | 1204 | float xClosestCellFront; |
Ludwigfr | 5:19f24c363418 | 1205 | float yClosestCellFront; |
Ludwigfr | 5:19f24c363418 | 1206 | float xClosestCellRight; |
Ludwigfr | 5:19f24c363418 | 1207 | float yClosestCellRight; |
Ludwigfr | 5:19f24c363418 | 1208 | //get theorical distance to sonar |
Ludwigfr | 5:19f24c363418 | 1209 | for(int i=0;i<this->map.nbCellWidth;i++){ |
Ludwigfr | 5:19f24c363418 | 1210 | for(int j=0;j<this->map.nbCellHeight;j++){ |
Ludwigfr | 5:19f24c363418 | 1211 | //check if occupied, if not discard |
Ludwigfr | 5:19f24c363418 | 1212 | if(this->map.get_proba_cell(i,j)<0.5){ |
Ludwigfr | 5:19f24c363418 | 1213 | //check if in sonar range |
Ludwigfr | 5:19f24c363418 | 1214 | xWorldCell=this->map.cell_width_coordinate_to_world(i); |
Ludwigfr | 5:19f24c363418 | 1215 | yWorldCell=this->map.cell_height_coordinate_to_world(j); |
Ludwigfr | 5:19f24c363418 | 1216 | //check left |
Ludwigfr | 5:19f24c363418 | 1217 | currDistance=this->sonarLeft.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK); |
Ludwigfr | 6:0e8db3a23486 | 1218 | if((currDistance < this->sonarLeft.maxRange) && currDistance > -1){ |
Ludwigfr | 5:19f24c363418 | 1219 | //check if distance is lower than previous, update lowest if so |
Ludwigfr | 5:19f24c363418 | 1220 | if(currDistance < leftCmEstimated){ |
Ludwigfr | 5:19f24c363418 | 1221 | leftCmEstimated= currDistance; |
Ludwigfr | 5:19f24c363418 | 1222 | xClosestCellLeft=xWorldCell; |
Ludwigfr | 5:19f24c363418 | 1223 | yClosestCellLeft=yWorldCell; |
Ludwigfr | 5:19f24c363418 | 1224 | } |
Ludwigfr | 5:19f24c363418 | 1225 | } |
Ludwigfr | 5:19f24c363418 | 1226 | //check front |
Ludwigfr | 5:19f24c363418 | 1227 | currDistance=this->sonarFront.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK); |
Ludwigfr | 6:0e8db3a23486 | 1228 | if((currDistance < this->sonarFront.maxRange) && currDistance > -1){ |
Ludwigfr | 5:19f24c363418 | 1229 | //check if distance is lower than previous, update lowest if so |
Ludwigfr | 5:19f24c363418 | 1230 | if(currDistance < frontCmEstimated){ |
Ludwigfr | 5:19f24c363418 | 1231 | frontCmEstimated= currDistance; |
Ludwigfr | 5:19f24c363418 | 1232 | xClosestCellFront=xWorldCell; |
Ludwigfr | 5:19f24c363418 | 1233 | yClosestCellFront=yWorldCell; |
Ludwigfr | 5:19f24c363418 | 1234 | } |
Ludwigfr | 5:19f24c363418 | 1235 | } |
Ludwigfr | 5:19f24c363418 | 1236 | //check right |
Ludwigfr | 5:19f24c363418 | 1237 | currDistance=this->sonarRight.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK); |
Ludwigfr | 6:0e8db3a23486 | 1238 | if((currDistance < this->sonarRight.maxRange) && currDistance > -1){ |
Ludwigfr | 5:19f24c363418 | 1239 | //check if distance is lower than previous, update lowest if so |
Ludwigfr | 5:19f24c363418 | 1240 | if(currDistance < rightCmEstimated){ |
Ludwigfr | 5:19f24c363418 | 1241 | rightCmEstimated= currDistance; |
Ludwigfr | 5:19f24c363418 | 1242 | xClosestCellRight=xWorldCell; |
Ludwigfr | 5:19f24c363418 | 1243 | yClosestCellRight=yWorldCell; |
Ludwigfr | 5:19f24c363418 | 1244 | } |
Ludwigfr | 5:19f24c363418 | 1245 | } |
Ludwigfr | 5:19f24c363418 | 1246 | } |
Ludwigfr | 5:19f24c363418 | 1247 | } |
Ludwigfr | 5:19f24c363418 | 1248 | } |
Ludwigfr | 5:19f24c363418 | 1249 | |
Ludwigfr | 5:19f24c363418 | 1250 | //check measurements from sonars, see if they passed the validation gate |
Ludwigfr | 5:19f24c363418 | 1251 | float leftCm = get_distance_left_sensor()/10; |
Ludwigfr | 5:19f24c363418 | 1252 | float frontCm = get_distance_front_sensor()/10; |
Ludwigfr | 5:19f24c363418 | 1253 | float rightCm = get_distance_right_sensor()/10; |
Ludwigfr | 5:19f24c363418 | 1254 | //if superior to sonar range, put the value to sonar max range + 1 |
Ludwigfr | 5:19f24c363418 | 1255 | if(leftCm > this->sonarLeft.maxRange) |
Ludwigfr | 5:19f24c363418 | 1256 | leftCm=this->sonarLeft.maxRange; |
Ludwigfr | 5:19f24c363418 | 1257 | if(frontCm > this->sonarFront.maxRange) |
Ludwigfr | 5:19f24c363418 | 1258 | frontCm=this->sonarFront.maxRange; |
Ludwigfr | 5:19f24c363418 | 1259 | if(rightCm > this->sonarRight.maxRange) |
Ludwigfr | 5:19f24c363418 | 1260 | rightCm=this->sonarRight.maxRange; |
Ludwigfr | 5:19f24c363418 | 1261 | |
Ludwigfr | 5:19f24c363418 | 1262 | //======INNOVATION======== |
Ludwigfr | 5:19f24c363418 | 1263 | //get the innoncation: the value of the difference between the actual measure and what we anticipated |
Ludwigfr | 5:19f24c363418 | 1264 | float innovationLeft=leftCm-leftCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1265 | float innovationFront=frontCm-frontCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1266 | float innovationRight=-rightCm-rightCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1267 | //compute jacobian of observation |
Ludwigfr | 5:19f24c363418 | 1268 | float jacobianOfObservationLeft[1][3]; |
Ludwigfr | 5:19f24c363418 | 1269 | float jacobianOfObservationRight[1][3]; |
Ludwigfr | 5:19f24c363418 | 1270 | float jacobianOfObservationFront[1][3]; |
Ludwigfr | 5:19f24c363418 | 1271 | float xSonarLeft=xEstimatedK+this->sonarLeft.distanceX; |
Ludwigfr | 5:19f24c363418 | 1272 | float ySonarLeft=yEstimatedK+this->sonarLeft.distanceY; |
Ludwigfr | 5:19f24c363418 | 1273 | //left |
Ludwigfr | 5:19f24c363418 | 1274 | jacobianOfObservationLeft[0][0]=(xSonarLeft-xClosestCellLeft)/leftCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1275 | jacobianOfObservationLeft[0][1]=(ySonarLeft-yClosestCellLeft)/leftCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1276 | jacobianOfObservationLeft[0][2]=(xClosestCellLeft-xSonarLeft)*(xSonarLeft*sin(thetaWorldEstimatedK)+ySonarLeft*cos(thetaWorldEstimatedK))+(yClosestCellLeft-ySonarLeft)*(-xSonarLeft*cos(thetaWorldEstimatedK)+ySonarLeft*sin(thetaWorldEstimatedK)); |
Ludwigfr | 5:19f24c363418 | 1277 | //front |
Ludwigfr | 5:19f24c363418 | 1278 | float xSonarFront=xEstimatedK+this->sonarFront.distanceX; |
Ludwigfr | 5:19f24c363418 | 1279 | float ySonarFront=yEstimatedK+this->sonarFront.distanceY; |
Ludwigfr | 5:19f24c363418 | 1280 | jacobianOfObservationFront[0][0]=(xSonarFront-xClosestCellFront)/frontCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1281 | jacobianOfObservationFront[0][1]=(ySonarFront-yClosestCellFront)/frontCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1282 | jacobianOfObservationFront[0][2]=(xClosestCellFront-xSonarFront)*(xSonarFront*sin(thetaWorldEstimatedK)+ySonarFront*cos(thetaWorldEstimatedK))+(yClosestCellFront-ySonarFront)*(-xSonarFront*cos(thetaWorldEstimatedK)+ySonarFront*sin(thetaWorldEstimatedK)); |
Ludwigfr | 5:19f24c363418 | 1283 | //right |
Ludwigfr | 5:19f24c363418 | 1284 | float xSonarRight=xEstimatedK+this->sonarRight.distanceX; |
Ludwigfr | 5:19f24c363418 | 1285 | float ySonarRight=yEstimatedK+this->sonarRight.distanceY; |
Ludwigfr | 5:19f24c363418 | 1286 | jacobianOfObservationRight[0][0]=(xSonarRight-xClosestCellRight)/rightCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1287 | jacobianOfObservationRight[0][1]=(ySonarRight-yClosestCellRight)/rightCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1288 | jacobianOfObservationRight[0][2]=(xClosestCellRight-xSonarRight)*(xSonarRight*sin(thetaWorldEstimatedK)+ySonarRight*cos(thetaWorldEstimatedK))+(yClosestCellRight-ySonarRight)*(-xSonarRight*cos(thetaWorldEstimatedK)+ySonarRight*sin(thetaWorldEstimatedK)); |
Ludwigfr | 5:19f24c363418 | 1289 | |
Ludwigfr | 5:19f24c363418 | 1290 | //error constants 0,0,0 sonars perfect; must be found by experimenting; gives mean and standanrt deviation |
Ludwigfr | 5:19f24c363418 | 1291 | //let's assume |
Ludwigfr | 5:19f24c363418 | 1292 | //in centimeters |
Ludwigfr | 5:19f24c363418 | 1293 | float R_front=4; |
Ludwigfr | 5:19f24c363418 | 1294 | float R_left=4; |
Ludwigfr | 5:19f24c363418 | 1295 | float R_right=4; |
Ludwigfr | 5:19f24c363418 | 1296 | |
Ludwigfr | 5:19f24c363418 | 1297 | //R-> 4 in diagonal |
Ludwigfr | 5:19f24c363418 | 1298 | |
Ludwigfr | 5:19f24c363418 | 1299 | //S for each sonar (concatenated covariance matrix of innovation) |
Ludwigfr | 5:19f24c363418 | 1300 | float innovationCovarianceFront=R_front+ jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]); |
Ludwigfr | 5:19f24c363418 | 1301 | float innovationCovarianceLeft=R_left+ jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]); |
Ludwigfr | 5:19f24c363418 | 1302 | float innovationCovarianceRight=R_right+ jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]); |
Ludwigfr | 5:19f24c363418 | 1303 | |
Ludwigfr | 5:19f24c363418 | 1304 | //check if it pass the validation gate |
Ludwigfr | 5:19f24c363418 | 1305 | float gateScoreLeft=innovationLeft*innovationLeft/innovationCovarianceLeft; |
Ludwigfr | 5:19f24c363418 | 1306 | float gateScoreFront=innovationFront*innovationFront/innovationCovarianceFront; |
Ludwigfr | 5:19f24c363418 | 1307 | float gateScoreRight=innovationRight*innovationRight/innovationCovarianceRight; |
Ludwigfr | 5:19f24c363418 | 1308 | int leftPassed=0; |
Ludwigfr | 5:19f24c363418 | 1309 | int frontPassed=0; |
Ludwigfr | 5:19f24c363418 | 1310 | int rightPassed=0; |
Ludwigfr | 5:19f24c363418 | 1311 | |
Ludwigfr | 6:0e8db3a23486 | 1312 | pc.printf("\r\n Gate scre L=%f, F=%f, R=%f",gateScoreLeft,gateScoreFront,gateScoreRight); |
Ludwigfr | 5:19f24c363418 | 1313 | //5cm -> 25 |
Ludwigfr | 5:19f24c363418 | 1314 | int errorsquare=25;//constant error |
Ludwigfr | 5:19f24c363418 | 1315 | |
Ludwigfr | 5:19f24c363418 | 1316 | if(gateScoreLeft <= errorsquare) |
Ludwigfr | 5:19f24c363418 | 1317 | leftPassed=1; |
Ludwigfr | 5:19f24c363418 | 1318 | if(gateScoreFront <= errorsquare) |
Ludwigfr | 5:19f24c363418 | 1319 | frontPassed=10; |
Ludwigfr | 5:19f24c363418 | 1320 | if(gateScoreRight <= errorsquare) |
Ludwigfr | 5:19f24c363418 | 1321 | rightPassed=100; |
Ludwigfr | 5:19f24c363418 | 1322 | //for those who passed |
Ludwigfr | 5:19f24c363418 | 1323 | //compute composite innovation |
Ludwigfr | 5:19f24c363418 | 1324 | int nbPassed=leftPassed+frontPassed+rightPassed; |
Ludwigfr | 5:19f24c363418 | 1325 | |
Ludwigfr | 5:19f24c363418 | 1326 | float xEstimatedKNext=xEstimatedK; |
Ludwigfr | 5:19f24c363418 | 1327 | float yEstimatedKNext=xEstimatedK; |
Ludwigfr | 5:19f24c363418 | 1328 | float thetaWorldEstimatedKNext=thetaWorldEstimatedK; |
Ludwigfr | 5:19f24c363418 | 1329 | |
Ludwigfr | 5:19f24c363418 | 1330 | float compositeInnovationCovariance3x3[3][3]={{0,0,0}, {0,0,0}, {0,0,0}}; |
Ludwigfr | 5:19f24c363418 | 1331 | |
Ludwigfr | 5:19f24c363418 | 1332 | float compositeInnovationCovariance2x2[2][2]={{0,0}, {0,0}}; |
Ludwigfr | 5:19f24c363418 | 1333 | |
Ludwigfr | 5:19f24c363418 | 1334 | float compositeInnovationCovariance1x1=0; |
Ludwigfr | 5:19f24c363418 | 1335 | |
Ludwigfr | 5:19f24c363418 | 1336 | float kalmanGain3X1[3][1]={{0}, {0}, {0}}; |
Ludwigfr | 5:19f24c363418 | 1337 | float kalmanGain3X2[3][2]={{0,0}, {0.0}, {0,0}}; |
Ludwigfr | 5:19f24c363418 | 1338 | float kalmanGain3X3[3][3]={{0,0,0}, {0,0,0}, {0,0,0}}; |
Ludwigfr | 5:19f24c363418 | 1339 | |
Ludwigfr | 5:19f24c363418 | 1340 | //we do not use the composite measurement jacobian (16), we directly use the values from the measurement jacobian (jacobianOfObservation) |
Ludwigfr | 5:19f24c363418 | 1341 | |
Ludwigfr | 5:19f24c363418 | 1342 | switch(nbPassed){ |
Ludwigfr | 5:19f24c363418 | 1343 | case 0://none |
Ludwigfr | 5:19f24c363418 | 1344 | //nothings happens it's okay |
Ludwigfr | 5:19f24c363418 | 1345 | break; |
Ludwigfr | 5:19f24c363418 | 1346 | case 1://left |
Ludwigfr | 5:19f24c363418 | 1347 | compositeInnovationCovariance1x1=R_right + jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]); |
Ludwigfr | 5:19f24c363418 | 1348 | |
Ludwigfr | 5:19f24c363418 | 1349 | kalmanGain3X1[0][0]=(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2])/compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1350 | kalmanGain3X1[1][0]=(covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2])/compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1351 | kalmanGain3X1[2][0]=(covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2])/compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1352 | |
Ludwigfr | 5:19f24c363418 | 1353 | xEstimatedKNext+= kalmanGain3X1[0][0]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1354 | yEstimatedKNext+= kalmanGain3X1[1][0]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1355 | thetaWorldEstimatedKNext+= kalmanGain3X1[2][0]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1356 | |
Ludwigfr | 5:19f24c363418 | 1357 | covariancePositionEstimationK[0][0]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[0][0],2) + covariancePositionEstimationK[0][0]; |
Ludwigfr | 5:19f24c363418 | 1358 | covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1359 | covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1360 | covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1361 | covariancePositionEstimationK[1][1]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[1][0],2) + covariancePositionEstimationK[1][1]; |
Ludwigfr | 5:19f24c363418 | 1362 | covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1363 | covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1364 | covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1365 | covariancePositionEstimationK[2][2]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[2][0],2) + covariancePositionEstimationK[2][2]; |
Ludwigfr | 5:19f24c363418 | 1366 | |
Ludwigfr | 5:19f24c363418 | 1367 | break; |
Ludwigfr | 5:19f24c363418 | 1368 | case 10://front |
Ludwigfr | 5:19f24c363418 | 1369 | |
Ludwigfr | 5:19f24c363418 | 1370 | compositeInnovationCovariance1x1=R_front + jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]); |
Ludwigfr | 5:19f24c363418 | 1371 | |
Ludwigfr | 5:19f24c363418 | 1372 | kalmanGain3X1[0][0]=(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2])/compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1373 | kalmanGain3X1[1][0]=(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2])/compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1374 | kalmanGain3X1[2][0]=(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2])/compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1375 | |
Ludwigfr | 5:19f24c363418 | 1376 | xEstimatedKNext+= kalmanGain3X1[0][0]*innovationFront; |
Ludwigfr | 5:19f24c363418 | 1377 | yEstimatedKNext+= kalmanGain3X1[1][0]*innovationFront; |
Ludwigfr | 5:19f24c363418 | 1378 | thetaWorldEstimatedKNext+= kalmanGain3X1[2][0]*innovationFront; |
Ludwigfr | 5:19f24c363418 | 1379 | |
Ludwigfr | 5:19f24c363418 | 1380 | covariancePositionEstimationK[0][0]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[0][0],2) + covariancePositionEstimationK[0][0]; |
Ludwigfr | 5:19f24c363418 | 1381 | covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1382 | covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1383 | covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1384 | covariancePositionEstimationK[1][1]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[1][0],2) + covariancePositionEstimationK[1][1]; |
Ludwigfr | 5:19f24c363418 | 1385 | covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1386 | covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1387 | covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1388 | covariancePositionEstimationK[2][2]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[2][0],2) + covariancePositionEstimationK[2][2]; |
Ludwigfr | 5:19f24c363418 | 1389 | |
Ludwigfr | 5:19f24c363418 | 1390 | break; |
Ludwigfr | 5:19f24c363418 | 1391 | case 11://left and front |
Ludwigfr | 5:19f24c363418 | 1392 | compositeInnovationCovariance2x2[0][0]=R_front + jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]); |
Ludwigfr | 5:19f24c363418 | 1393 | compositeInnovationCovariance2x2[0][1]=jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]); |
Ludwigfr | 5:19f24c363418 | 1394 | compositeInnovationCovariance2x2[1][0]=jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]); |
Ludwigfr | 5:19f24c363418 | 1395 | compositeInnovationCovariance2x2[1][1]=R_left + jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]); |
Ludwigfr | 5:19f24c363418 | 1396 | |
Ludwigfr | 5:19f24c363418 | 1397 | kalmanGain3X2[0][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1398 | kalmanGain3X2[0][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1399 | kalmanGain3X2[1][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1400 | kalmanGain3X2[1][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1401 | kalmanGain3X2[2][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1402 | kalmanGain3X2[2][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1403 | |
Ludwigfr | 5:19f24c363418 | 1404 | xEstimatedKNext+= kalmanGain3X2[0][0]*innovationFront + kalmanGain3X2[0][1]*innovationLeft; |
Ludwigfr | 5:19f24c363418 | 1405 | yEstimatedKNext+= kalmanGain3X2[1][0]*innovationFront + kalmanGain3X2[1][1]*innovationLeft; |
Ludwigfr | 5:19f24c363418 | 1406 | thetaWorldEstimatedKNext+= kalmanGain3X2[2][0]*innovationFront + kalmanGain3X2[2][1]*innovationLeft; |
Ludwigfr | 5:19f24c363418 | 1407 | |
Ludwigfr | 5:19f24c363418 | 1408 | covariancePositionEstimationK[0][0]=covariancePositionEstimationK[0][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1409 | covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1410 | covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1411 | covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1412 | covariancePositionEstimationK[1][1]=covariancePositionEstimationK[1][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1413 | covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1414 | covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1415 | covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1416 | covariancePositionEstimationK[2][2]=covariancePositionEstimationK[2][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1417 | |
Ludwigfr | 5:19f24c363418 | 1418 | break; |
Ludwigfr | 5:19f24c363418 | 1419 | case 100://right |
Ludwigfr | 5:19f24c363418 | 1420 | |
Ludwigfr | 5:19f24c363418 | 1421 | compositeInnovationCovariance1x1=R_right + jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]); |
Ludwigfr | 5:19f24c363418 | 1422 | |
Ludwigfr | 5:19f24c363418 | 1423 | kalmanGain3X1[0][0]=(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2])/compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1424 | kalmanGain3X1[1][0]=(covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2])/compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1425 | kalmanGain3X1[2][0]=(covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2])/compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1426 | |
Ludwigfr | 5:19f24c363418 | 1427 | xEstimatedKNext+= kalmanGain3X1[0][0]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1428 | yEstimatedKNext+= kalmanGain3X1[1][0]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1429 | thetaWorldEstimatedKNext+= kalmanGain3X1[2][0]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1430 | |
Ludwigfr | 5:19f24c363418 | 1431 | covariancePositionEstimationK[0][0]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[0][0],2) + covariancePositionEstimationK[0][0]; |
Ludwigfr | 5:19f24c363418 | 1432 | covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1433 | covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1434 | covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1435 | covariancePositionEstimationK[1][1]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[1][0],2) + covariancePositionEstimationK[1][1]; |
Ludwigfr | 5:19f24c363418 | 1436 | covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1437 | covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1438 | covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1439 | covariancePositionEstimationK[2][2]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[2][0],2) + covariancePositionEstimationK[2][2]; |
Ludwigfr | 5:19f24c363418 | 1440 | |
Ludwigfr | 5:19f24c363418 | 1441 | break; |
Ludwigfr | 5:19f24c363418 | 1442 | case 101://right and left |
Ludwigfr | 5:19f24c363418 | 1443 | compositeInnovationCovariance2x2[0][0]=R_left + jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]); |
Ludwigfr | 5:19f24c363418 | 1444 | compositeInnovationCovariance2x2[0][1]=jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]); |
Ludwigfr | 5:19f24c363418 | 1445 | compositeInnovationCovariance2x2[1][0]=jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]); |
Ludwigfr | 5:19f24c363418 | 1446 | compositeInnovationCovariance2x2[1][1]=R_right + jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]); |
Ludwigfr | 5:19f24c363418 | 1447 | |
Ludwigfr | 5:19f24c363418 | 1448 | kalmanGain3X2[0][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1449 | kalmanGain3X2[0][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1450 | kalmanGain3X2[1][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1451 | kalmanGain3X2[1][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1452 | kalmanGain3X2[2][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1453 | kalmanGain3X2[2][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1454 | |
Ludwigfr | 5:19f24c363418 | 1455 | xEstimatedKNext+= kalmanGain3X2[0][0]*innovationLeft + kalmanGain3X2[0][1]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1456 | yEstimatedKNext+= kalmanGain3X2[1][0]*innovationLeft + kalmanGain3X2[1][1]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1457 | thetaWorldEstimatedKNext+= kalmanGain3X2[2][0]*innovationLeft + kalmanGain3X2[2][1]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1458 | |
Ludwigfr | 5:19f24c363418 | 1459 | covariancePositionEstimationK[0][0]=covariancePositionEstimationK[0][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1460 | covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1461 | covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1462 | covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1463 | covariancePositionEstimationK[1][1]=covariancePositionEstimationK[1][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1464 | covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1465 | covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1466 | covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1467 | covariancePositionEstimationK[2][2]=covariancePositionEstimationK[2][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1468 | |
Ludwigfr | 5:19f24c363418 | 1469 | break; |
Ludwigfr | 5:19f24c363418 | 1470 | case 110://right and front |
Ludwigfr | 5:19f24c363418 | 1471 | |
Ludwigfr | 5:19f24c363418 | 1472 | compositeInnovationCovariance2x2[0][0]=R_front + jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]); |
Ludwigfr | 5:19f24c363418 | 1473 | compositeInnovationCovariance2x2[0][1]=jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]); |
Ludwigfr | 5:19f24c363418 | 1474 | compositeInnovationCovariance2x2[1][0]=jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]); |
Ludwigfr | 5:19f24c363418 | 1475 | compositeInnovationCovariance2x2[1][1]=R_right + jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]); |
Ludwigfr | 5:19f24c363418 | 1476 | |
Ludwigfr | 5:19f24c363418 | 1477 | kalmanGain3X2[0][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1478 | kalmanGain3X2[0][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1479 | kalmanGain3X2[1][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1480 | kalmanGain3X2[1][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1481 | kalmanGain3X2[2][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1482 | kalmanGain3X2[2][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]); |
Ludwigfr | 5:19f24c363418 | 1483 | |
Ludwigfr | 5:19f24c363418 | 1484 | xEstimatedKNext+= kalmanGain3X2[0][0]*innovationFront + kalmanGain3X2[0][1]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1485 | yEstimatedKNext+= kalmanGain3X2[1][0]*innovationFront + kalmanGain3X2[1][1]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1486 | thetaWorldEstimatedKNext+= kalmanGain3X2[2][0]*innovationFront + kalmanGain3X2[2][1]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1487 | |
Ludwigfr | 5:19f24c363418 | 1488 | covariancePositionEstimationK[0][0]=covariancePositionEstimationK[0][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1489 | covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1490 | covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1491 | covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1492 | covariancePositionEstimationK[1][1]=covariancePositionEstimationK[1][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1493 | covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1494 | covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1495 | covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1496 | covariancePositionEstimationK[2][2]=covariancePositionEstimationK[2][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]); |
Ludwigfr | 5:19f24c363418 | 1497 | |
Ludwigfr | 5:19f24c363418 | 1498 | break; |
Ludwigfr | 5:19f24c363418 | 1499 | case 111://right front and left |
Ludwigfr | 5:19f24c363418 | 1500 | //get the compositeInnovationCovariance3x3 |
Ludwigfr | 5:19f24c363418 | 1501 | compositeInnovationCovariance3x3[0][0]=R_front + jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]); |
Ludwigfr | 5:19f24c363418 | 1502 | compositeInnovationCovariance3x3[0][1]=jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]); |
Ludwigfr | 5:19f24c363418 | 1503 | compositeInnovationCovariance3x3[0][2]=jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]); |
Ludwigfr | 5:19f24c363418 | 1504 | compositeInnovationCovariance3x3[1][0]=jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]); |
Ludwigfr | 5:19f24c363418 | 1505 | compositeInnovationCovariance3x3[1][1]=R_left + jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]); |
Ludwigfr | 5:19f24c363418 | 1506 | compositeInnovationCovariance3x3[1][2]=jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]); |
Ludwigfr | 5:19f24c363418 | 1507 | compositeInnovationCovariance3x3[2][0]=jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]); |
Ludwigfr | 5:19f24c363418 | 1508 | compositeInnovationCovariance3x3[2][1]=jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]); |
Ludwigfr | 5:19f24c363418 | 1509 | compositeInnovationCovariance3x3[2][2]=R_right + jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]); |
Ludwigfr | 5:19f24c363418 | 1510 | |
Ludwigfr | 5:19f24c363418 | 1511 | //compute the kalman gain |
Ludwigfr | 5:19f24c363418 | 1512 | kalmanGain3X3[0][0]=(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]); |
Ludwigfr | 5:19f24c363418 | 1513 | kalmanGain3X3[0][1]=-(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]); |
Ludwigfr | 5:19f24c363418 | 1514 | kalmanGain3X3[0][2]=(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] - covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] - covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]); |
Ludwigfr | 5:19f24c363418 | 1515 | kalmanGain3X3[1][0]=(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]); |
Ludwigfr | 5:19f24c363418 | 1516 | kalmanGain3X3[1][1]=-(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]); |
Ludwigfr | 5:19f24c363418 | 1517 | kalmanGain3X3[1][2]=(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] - covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] - covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]); |
Ludwigfr | 5:19f24c363418 | 1518 | kalmanGain3X3[2][0]=(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]); |
Ludwigfr | 5:19f24c363418 | 1519 | kalmanGain3X3[2][1]=-(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]); |
Ludwigfr | 5:19f24c363418 | 1520 | kalmanGain3X3[2][2]=(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] - covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] - covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]); |
Ludwigfr | 5:19f24c363418 | 1521 | |
Ludwigfr | 5:19f24c363418 | 1522 | //update the prediction |
Ludwigfr | 5:19f24c363418 | 1523 | xEstimatedKNext+= kalmanGain3X3[0][0]*innovationFront + kalmanGain3X3[0][1]*innovationLeft + kalmanGain3X3[0][2]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1524 | yEstimatedKNext+= kalmanGain3X3[1][0]*innovationFront + kalmanGain3X3[1][1]*innovationLeft + kalmanGain3X3[1][2]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1525 | thetaWorldEstimatedKNext+= kalmanGain3X3[2][0]*innovationFront + kalmanGain3X3[2][1]*innovationLeft + kalmanGain3X3[2][2]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1526 | |
Ludwigfr | 5:19f24c363418 | 1527 | //update covariancePositionEstimationK |
Ludwigfr | 5:19f24c363418 | 1528 | covariancePositionEstimationK[0][0]=covariancePositionEstimationK[0][0] - kalmanGain3X3[0][0]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[0][1]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[0][2]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][2]); |
Ludwigfr | 5:19f24c363418 | 1529 | covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X3[1][0]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[1][1]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[1][2]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][2]); |
Ludwigfr | 5:19f24c363418 | 1530 | covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X3[2][0]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[2][1]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[2][2]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][2]); |
Ludwigfr | 5:19f24c363418 | 1531 | covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X3[0][0]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[0][1]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[0][2]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][2]); |
Ludwigfr | 5:19f24c363418 | 1532 | covariancePositionEstimationK[1][1]=covariancePositionEstimationK[1][1] - kalmanGain3X3[1][0]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[1][1]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[1][2]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][2]); |
Ludwigfr | 5:19f24c363418 | 1533 | covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X3[2][0]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[2][1]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[2][2]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][2]); |
Ludwigfr | 5:19f24c363418 | 1534 | covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X3[0][0]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[0][1]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[0][2]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][2]); |
Ludwigfr | 5:19f24c363418 | 1535 | covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X3[1][0]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[1][1]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[1][2]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][2]); |
Ludwigfr | 5:19f24c363418 | 1536 | covariancePositionEstimationK[2][2]=covariancePositionEstimationK[2][2] - kalmanGain3X3[2][0]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[2][1]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[2][2]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][2]); |
Ludwigfr | 5:19f24c363418 | 1537 | |
Ludwigfr | 5:19f24c363418 | 1538 | break; |
Ludwigfr | 5:19f24c363418 | 1539 | } |
Ludwigfr | 5:19f24c363418 | 1540 | //big question, in wich coordinate space are those measurements... |
Ludwigfr | 5:19f24c363418 | 1541 | //try with world coordinate system |
Ludwigfr | 5:19f24c363418 | 1542 | |
Ludwigfr | 5:19f24c363418 | 1543 | this->xWorld=xEstimatedKNext; |
Ludwigfr | 5:19f24c363418 | 1544 | this->yWorld=yEstimatedKNext; |
Ludwigfr | 5:19f24c363418 | 1545 | this->thetaWorld=thetaWorldEstimatedKNext; |
Ludwigfr | 5:19f24c363418 | 1546 | |
Ludwigfr | 6:0e8db3a23486 | 1547 | pc.printf("\r\nAfter Kalm X=%f, Y=%f, theta=%f",xEstimatedKNext,yEstimatedKNext,thetaWorldEstimatedKNext); |
Ludwigfr | 5:19f24c363418 | 1548 | //try with robot one |
Ludwigfr | 5:19f24c363418 | 1549 | /* |
Ludwigfr | 5:19f24c363418 | 1550 | X=xEstimatedKNext; |
Ludwigfr | 5:19f24c363418 | 1551 | Y=yEstimatedKNext; |
Ludwigfr | 5:19f24c363418 | 1552 | theta=thetaWorldEstimatedKNext; |
Ludwigfr | 5:19f24c363418 | 1553 | this->xWorld=-Y; |
Ludwigfr | 5:19f24c363418 | 1554 | this->yWorld=X; |
Ludwigfr | 5:19f24c363418 | 1555 | if(theta >PI/2) |
Ludwigfr | 5:19f24c363418 | 1556 | this->thetaWorld=-PI+(theta-PI/2); |
Ludwigfr | 5:19f24c363418 | 1557 | else |
Ludwigfr | 5:19f24c363418 | 1558 | this->thetaWorld=theta+PI/2; |
Ludwigfr | 5:19f24c363418 | 1559 | |
Ludwigfr | 5:19f24c363418 | 1560 | |
Ludwigfr | 5:19f24c363418 | 1561 | this->print_map_with_robot_position(); |
Ludwigfr | 5:19f24c363418 | 1562 | pc.printf("\n\rX= %f",this->xWorld); |
Ludwigfr | 5:19f24c363418 | 1563 | pc.printf("\n\rY= %f",this->yWorld); |
Ludwigfr | 5:19f24c363418 | 1564 | pc.printf("\n\rtheta= %f",this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 1565 | */ |
Ludwigfr | 5:19f24c363418 | 1566 | //update odometrie X Y theta... |
Ludwigfr | 5:19f24c363418 | 1567 | } |
Ludwigfr | 5:19f24c363418 | 1568 | |
Ludwigfr | 5:19f24c363418 | 1569 | |
Ludwigfr | 5:19f24c363418 | 1570 |