test morning
Dependencies: ISR_Mini-explorer mbed
Fork of roboticLab_withclass_3_July by
MiniExplorerCoimbra.cpp@5:19f24c363418, 2017-07-06 (annotated)
- Committer:
- Ludwigfr
- Date:
- Thu Jul 06 16:51:40 2017 +0000
- Revision:
- 5:19f24c363418
- Parent:
- 3:37345c109dfc
- Child:
- 6:0e8db3a23486
latest 6/7 17h51
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 | 5:19f24c363418 | 6 | MiniExplorerCoimbra::MiniExplorerCoimbra(float defaultXWorld, float defaultYWorld, float defaultThetaWorld, float widthRealMap, float heightRealMap):map(widthRealMap,heightRealMap,18,12),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 | |
geotsam | 1:20f48907c726 | 17 | this->k_linear=10; |
geotsam | 1:20f48907c726 | 18 | this->k_angular=200; |
Ludwigfr | 0:9f7ee7ed13e4 | 19 | this->khro=12; |
Ludwigfr | 0:9f7ee7ed13e4 | 20 | this->ka=30; |
Ludwigfr | 0:9f7ee7ed13e4 | 21 | this->kb=-13; |
Ludwigfr | 0:9f7ee7ed13e4 | 22 | this->kv=200; |
Ludwigfr | 0:9f7ee7ed13e4 | 23 | this->kh=200; |
Ludwigfr | 5:19f24c363418 | 24 | this->kd=5;//previous 5 |
geotsam | 1:20f48907c726 | 25 | this->speed=300; |
Ludwigfr | 0:9f7ee7ed13e4 | 26 | |
Ludwigfr | 3:37345c109dfc | 27 | this->rangeForce=50; |
Ludwigfr | 5:19f24c363418 | 28 | //not too bad values 200 and -40000 |
Ludwigfr | 5:19f24c363418 | 29 | //not too bad values 200 and -20000 |
Ludwigfr | 5:19f24c363418 | 30 | //not too bad values 500 and -20000 |
Ludwigfr | 5:19f24c363418 | 31 | //not too bad values 500 and -25000 rangeForce 50 |
Ludwigfr | 5:19f24c363418 | 32 | this->attractionConstantForce=600; |
Ludwigfr | 5:19f24c363418 | 33 | this->repulsionConstantForce=0; |
Ludwigfr | 5:19f24c363418 | 34 | |
Ludwigfr | 5:19f24c363418 | 35 | this->covariancePositionEstimationK[0][0]=0; |
Ludwigfr | 5:19f24c363418 | 36 | this->covariancePositionEstimationK[0][1]=0; |
Ludwigfr | 5:19f24c363418 | 37 | this->covariancePositionEstimationK[0][2]=0; |
Ludwigfr | 5:19f24c363418 | 38 | |
Ludwigfr | 5:19f24c363418 | 39 | this->covariancePositionEstimationK[1][0]=0; |
Ludwigfr | 5:19f24c363418 | 40 | this->covariancePositionEstimationK[1][1]=0; |
Ludwigfr | 5:19f24c363418 | 41 | this->covariancePositionEstimationK[1][2]=0; |
Ludwigfr | 5:19f24c363418 | 42 | |
Ludwigfr | 5:19f24c363418 | 43 | this->covariancePositionEstimationK[2][0]=0; |
Ludwigfr | 5:19f24c363418 | 44 | this->covariancePositionEstimationK[2][1]=0; |
Ludwigfr | 5:19f24c363418 | 45 | this->covariancePositionEstimationK[2][2]=0; |
Ludwigfr | 5:19f24c363418 | 46 | |
Ludwigfr | 0:9f7ee7ed13e4 | 47 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 48 | |
Ludwigfr | 0:9f7ee7ed13e4 | 49 | void MiniExplorerCoimbra::setXYThetaAndXYThetaWorld(float defaultXWorld, float defaultYWorld, float defaultThetaWorld){ |
Ludwigfr | 0:9f7ee7ed13e4 | 50 | this->xWorld=defaultXWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 51 | this->yWorld=defaultYWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 52 | this->thetaWorld=defaultThetaWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 53 | X=defaultYWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 54 | Y=-defaultXWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 55 | if(defaultThetaWorld < -PI/2) |
Ludwigfr | 0:9f7ee7ed13e4 | 56 | theta=PI/2+PI-defaultThetaWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 57 | else |
Ludwigfr | 0:9f7ee7ed13e4 | 58 | theta=defaultThetaWorld-PI/2; |
Ludwigfr | 0:9f7ee7ed13e4 | 59 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 60 | |
Ludwigfr | 0:9f7ee7ed13e4 | 61 | void MiniExplorerCoimbra::myOdometria(){ |
Ludwigfr | 0:9f7ee7ed13e4 | 62 | Odometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 63 | this->xWorld=-Y; |
Ludwigfr | 0:9f7ee7ed13e4 | 64 | this->yWorld=X; |
Ludwigfr | 0:9f7ee7ed13e4 | 65 | if(theta >PI/2) |
Ludwigfr | 0:9f7ee7ed13e4 | 66 | this->thetaWorld=-PI+(theta-PI/2); |
Ludwigfr | 0:9f7ee7ed13e4 | 67 | else |
Ludwigfr | 0:9f7ee7ed13e4 | 68 | this->thetaWorld=theta+PI/2; |
Ludwigfr | 0:9f7ee7ed13e4 | 69 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 70 | |
geotsam | 1:20f48907c726 | 71 | void MiniExplorerCoimbra::go_to_point(float targetXWorld, float targetYWorld) { |
geotsam | 1:20f48907c726 | 72 | |
geotsam | 1:20f48907c726 | 73 | float angleError; //angle error |
geotsam | 1:20f48907c726 | 74 | float d; //distance from target |
geotsam | 1:20f48907c726 | 75 | float k_linear=10, k_angular=200; |
geotsam | 1:20f48907c726 | 76 | float angularLeft, angularRight, linear, angular; |
geotsam | 1:20f48907c726 | 77 | int speed=300; |
geotsam | 1:20f48907c726 | 78 | |
geotsam | 1:20f48907c726 | 79 | do { |
geotsam | 1:20f48907c726 | 80 | //Updating X,Y and theta with the odometry values |
geotsam | 1:20f48907c726 | 81 | this->myOdometria(); |
geotsam | 1:20f48907c726 | 82 | |
geotsam | 1:20f48907c726 | 83 | //Computing angle error and distance towards the target value |
geotsam | 1:20f48907c726 | 84 | angleError = atan2((targetYWorld-this->yWorld),(targetXWorld-this->xWorld))-this->thetaWorld; |
geotsam | 1:20f48907c726 | 85 | if(angleError>PI) angleError=-(angleError-PI); |
geotsam | 1:20f48907c726 | 86 | else if(angleError<-PI) angleError=-(angleError+PI); |
geotsam | 1:20f48907c726 | 87 | pc.printf("\n\r error=%f",angleError); |
geotsam | 1:20f48907c726 | 88 | |
geotsam | 1:20f48907c726 | 89 | d=this->dist(this->xWorld, this->yWorld, targetXWorld, targetYWorld); |
geotsam | 1:20f48907c726 | 90 | pc.printf("\n\r dist=%f/n", d); |
geotsam | 1:20f48907c726 | 91 | |
geotsam | 1:20f48907c726 | 92 | //Computing linear and angular velocities |
geotsam | 1:20f48907c726 | 93 | linear=k_linear*d; |
geotsam | 1:20f48907c726 | 94 | angular=k_angular*angleError; |
geotsam | 1:20f48907c726 | 95 | angularLeft=(linear-0.5*this->distanceWheels*angular)/this->radiusWheels; |
geotsam | 1:20f48907c726 | 96 | angularRight=(linear+0.5*this->distanceWheels*angular)/this->radiusWheels; |
geotsam | 1:20f48907c726 | 97 | |
geotsam | 1:20f48907c726 | 98 | |
geotsam | 1:20f48907c726 | 99 | //Normalize speed for motors |
geotsam | 1:20f48907c726 | 100 | if(angularLeft>angularRight) { |
geotsam | 1:20f48907c726 | 101 | angularRight=speed*angularRight/angularLeft; |
geotsam | 1:20f48907c726 | 102 | angularLeft=speed; |
geotsam | 1:20f48907c726 | 103 | } else { |
geotsam | 1:20f48907c726 | 104 | angularLeft=speed*angularLeft/angularRight; |
geotsam | 1:20f48907c726 | 105 | angularRight=speed; |
geotsam | 1:20f48907c726 | 106 | } |
geotsam | 1:20f48907c726 | 107 | |
geotsam | 1:20f48907c726 | 108 | pc.printf("\n\r X=%f", this->xWorld); |
geotsam | 1:20f48907c726 | 109 | pc.printf("\n\r Y=%f", this->yWorld); |
geotsam | 1:20f48907c726 | 110 | pc.printf("\n\r theta=%f", this->thetaWorld); |
geotsam | 1:20f48907c726 | 111 | |
geotsam | 1:20f48907c726 | 112 | //Updating motor velocities |
geotsam | 1:20f48907c726 | 113 | if(angularLeft>0){ |
geotsam | 1:20f48907c726 | 114 | leftMotor(1,angularLeft); |
geotsam | 1:20f48907c726 | 115 | } |
geotsam | 1:20f48907c726 | 116 | else{ |
geotsam | 1:20f48907c726 | 117 | leftMotor(0,-angularLeft); |
geotsam | 1:20f48907c726 | 118 | } |
geotsam | 1:20f48907c726 | 119 | |
geotsam | 1:20f48907c726 | 120 | if(angularRight>0){ |
geotsam | 1:20f48907c726 | 121 | rightMotor(1,angularRight); |
geotsam | 1:20f48907c726 | 122 | } |
geotsam | 1:20f48907c726 | 123 | else{ |
geotsam | 1:20f48907c726 | 124 | rightMotor(0,-angularRight); |
geotsam | 1:20f48907c726 | 125 | } |
geotsam | 1:20f48907c726 | 126 | |
Ludwigfr | 5:19f24c363418 | 127 | //wait(0.5); |
geotsam | 1:20f48907c726 | 128 | } while(d>1); |
geotsam | 1:20f48907c726 | 129 | |
geotsam | 1:20f48907c726 | 130 | //Stop at the end |
geotsam | 1:20f48907c726 | 131 | leftMotor(1,0); |
geotsam | 1:20f48907c726 | 132 | rightMotor(1,0); |
geotsam | 1:20f48907c726 | 133 | } |
geotsam | 1:20f48907c726 | 134 | |
Ludwigfr | 2:11cd5173aa36 | 135 | void MiniExplorerCoimbra::test_procedure_lab2(int nbIteration){ |
Ludwigfr | 2:11cd5173aa36 | 136 | for(int i=0;i<nbIteration;i++){ |
Ludwigfr | 2:11cd5173aa36 | 137 | this->randomize_and_map(); |
Ludwigfr | 5:19f24c363418 | 138 | //this->print_map_with_robot_position(); |
Ludwigfr | 2:11cd5173aa36 | 139 | } |
Ludwigfr | 3:37345c109dfc | 140 | while(1) |
Ludwigfr | 3:37345c109dfc | 141 | this->print_map_with_robot_position(); |
Ludwigfr | 2:11cd5173aa36 | 142 | } |
Ludwigfr | 2:11cd5173aa36 | 143 | |
Ludwigfr | 0:9f7ee7ed13e4 | 144 | //generate a position randomly and makes the robot go there while updating the map |
Ludwigfr | 0:9f7ee7ed13e4 | 145 | void MiniExplorerCoimbra::randomize_and_map() { |
Ludwigfr | 0:9f7ee7ed13e4 | 146 | //TODO check that it's aurelien's work |
Ludwigfr | 2:11cd5173aa36 | 147 | float movementOnX=rand()%(int)(this->map.widthRealMap); |
Ludwigfr | 2:11cd5173aa36 | 148 | float movementOnY=rand()%(int)(this->map.heightRealMap); |
Ludwigfr | 3:37345c109dfc | 149 | |
Ludwigfr | 3:37345c109dfc | 150 | float targetXWorld = movementOnX; |
Ludwigfr | 3:37345c109dfc | 151 | float targetYWorld = movementOnY; |
Ludwigfr | 3:37345c109dfc | 152 | float targetAngleWorld = 2*((float)(rand()%31416)-15708)/10000.0; |
Ludwigfr | 0:9f7ee7ed13e4 | 153 | |
Ludwigfr | 3:37345c109dfc | 154 | //target between (0,0) and (widthRealMap,heightRealMap) |
Ludwigfr | 0:9f7ee7ed13e4 | 155 | this->go_to_point_with_angle(targetXWorld, targetYWorld, targetAngleWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 156 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 157 | |
Ludwigfr | 2:11cd5173aa36 | 158 | void MiniExplorerCoimbra::test_sonars_and_map(int nbIteration){ |
Ludwigfr | 2:11cd5173aa36 | 159 | float leftMm; |
Ludwigfr | 2:11cd5173aa36 | 160 | float frontMm; |
Ludwigfr | 2:11cd5173aa36 | 161 | float rightMm; |
Ludwigfr | 2:11cd5173aa36 | 162 | this->myOdometria(); |
Ludwigfr | 2:11cd5173aa36 | 163 | this->print_map_with_robot_position(); |
Ludwigfr | 2:11cd5173aa36 | 164 | for(int i=0;i<nbIteration;i++){ |
Ludwigfr | 2:11cd5173aa36 | 165 | leftMm = get_distance_left_sensor(); |
Ludwigfr | 2:11cd5173aa36 | 166 | frontMm = get_distance_front_sensor(); |
Ludwigfr | 2:11cd5173aa36 | 167 | rightMm = get_distance_right_sensor(); |
Ludwigfr | 3:37345c109dfc | 168 | pc.printf("\n\r 1 leftMm= %f",leftMm); |
Ludwigfr | 3:37345c109dfc | 169 | pc.printf("\n\r 1 frontMm= %f",frontMm); |
Ludwigfr | 3:37345c109dfc | 170 | pc.printf("\n\r 1 rightMm= %f",rightMm); |
Ludwigfr | 2:11cd5173aa36 | 171 | this->update_sonar_values(leftMm, frontMm, rightMm); |
Ludwigfr | 2:11cd5173aa36 | 172 | this->print_map_with_robot_position(); |
Ludwigfr | 3:37345c109dfc | 173 | wait(1); |
Ludwigfr | 2:11cd5173aa36 | 174 | } |
Ludwigfr | 2:11cd5173aa36 | 175 | } |
Ludwigfr | 2:11cd5173aa36 | 176 | |
Ludwigfr | 2:11cd5173aa36 | 177 | |
Ludwigfr | 2:11cd5173aa36 | 178 | //generate a position randomly and makes the robot go there while updating the map |
Ludwigfr | 0:9f7ee7ed13e4 | 179 | //move of targetXWorld and targetYWorld ending in a targetAngleWorld |
Ludwigfr | 0:9f7ee7ed13e4 | 180 | void MiniExplorerCoimbra::go_to_point_with_angle(float targetXWorld, float targetYWorld, float targetAngleWorld) { |
Ludwigfr | 0:9f7ee7ed13e4 | 181 | bool keepGoing=true; |
Ludwigfr | 0:9f7ee7ed13e4 | 182 | float leftMm; |
Ludwigfr | 0:9f7ee7ed13e4 | 183 | float frontMm; |
Ludwigfr | 0:9f7ee7ed13e4 | 184 | float rightMm; |
Ludwigfr | 0:9f7ee7ed13e4 | 185 | float dt; |
Ludwigfr | 0:9f7ee7ed13e4 | 186 | Timer t; |
Ludwigfr | 0:9f7ee7ed13e4 | 187 | float distanceToTarget; |
Ludwigfr | 0:9f7ee7ed13e4 | 188 | do { |
Ludwigfr | 0:9f7ee7ed13e4 | 189 | //Timer stuff |
Ludwigfr | 0:9f7ee7ed13e4 | 190 | dt = t.read(); |
Ludwigfr | 0:9f7ee7ed13e4 | 191 | t.reset(); |
Ludwigfr | 0:9f7ee7ed13e4 | 192 | t.start(); |
Ludwigfr | 0:9f7ee7ed13e4 | 193 | |
Ludwigfr | 0:9f7ee7ed13e4 | 194 | //Updating X,Y and theta with the odometry values |
Ludwigfr | 0:9f7ee7ed13e4 | 195 | this->myOdometria(); |
Ludwigfr | 2:11cd5173aa36 | 196 | leftMm = get_distance_left_sensor(); |
Ludwigfr | 2:11cd5173aa36 | 197 | frontMm = get_distance_front_sensor(); |
Ludwigfr | 2:11cd5173aa36 | 198 | rightMm = get_distance_right_sensor(); |
Ludwigfr | 2:11cd5173aa36 | 199 | //if in dangerzone 150 mm |
Ludwigfr | 0:9f7ee7ed13e4 | 200 | if((frontMm < 150 && frontMm > 0)|| (leftMm <150 && leftMm > 0) || (rightMm <150 && rightMm > 0) ){ |
Ludwigfr | 2:11cd5173aa36 | 201 | //stop motors |
Ludwigfr | 0:9f7ee7ed13e4 | 202 | leftMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 203 | rightMotor(1,0); |
Ludwigfr | 2:11cd5173aa36 | 204 | //update the map |
Ludwigfr | 0:9f7ee7ed13e4 | 205 | this->update_sonar_values(leftMm, frontMm, rightMm); |
Ludwigfr | 0:9f7ee7ed13e4 | 206 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 207 | keepGoing=false; |
Ludwigfr | 2:11cd5173aa36 | 208 | this->do_half_flip(); |
Ludwigfr | 0:9f7ee7ed13e4 | 209 | }else{ |
Ludwigfr | 0:9f7ee7ed13e4 | 210 | //if not in danger zone continue as usual |
Ludwigfr | 0:9f7ee7ed13e4 | 211 | this->update_sonar_values(leftMm, frontMm, rightMm); |
Ludwigfr | 2:11cd5173aa36 | 212 | //Updating motor velocities |
Ludwigfr | 0:9f7ee7ed13e4 | 213 | distanceToTarget=this->update_angular_speed_wheels_go_to_point_with_angle(targetXWorld,targetYWorld,targetAngleWorld,dt); |
Ludwigfr | 5:19f24c363418 | 214 | //wait(0.2); |
Ludwigfr | 0:9f7ee7ed13e4 | 215 | //Timer stuff |
Ludwigfr | 0:9f7ee7ed13e4 | 216 | t.stop(); |
Ludwigfr | 2:11cd5173aa36 | 217 | pc.printf("\n\rdist to target= %f",distanceToTarget); |
Ludwigfr | 0:9f7ee7ed13e4 | 218 | } |
Ludwigfr | 3:37345c109dfc | 219 | } while((distanceToTarget>2 || (abs(targetAngleWorld-this->thetaWorld)>PI/3)) && keepGoing); |
Ludwigfr | 0:9f7ee7ed13e4 | 220 | |
Ludwigfr | 0:9f7ee7ed13e4 | 221 | //Stop at the end |
Ludwigfr | 0:9f7ee7ed13e4 | 222 | leftMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 223 | rightMotor(1,0); |
Ludwigfr | 2:11cd5173aa36 | 224 | pc.printf("\r\nReached Target!"); |
Ludwigfr | 0:9f7ee7ed13e4 | 225 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 226 | |
geotsam | 1:20f48907c726 | 227 | //move of targetXWorld and targetYWorld ending in a targetAngleWorld |
geotsam | 1:20f48907c726 | 228 | void MiniExplorerCoimbra::go_to_point_with_angle_first_lab(float targetXWorld, float targetYWorld, float targetAngleWorld) { |
geotsam | 1:20f48907c726 | 229 | float dt; |
geotsam | 1:20f48907c726 | 230 | Timer t; |
geotsam | 1:20f48907c726 | 231 | float distanceToTarget; |
geotsam | 1:20f48907c726 | 232 | do { |
geotsam | 1:20f48907c726 | 233 | //Timer stuff |
geotsam | 1:20f48907c726 | 234 | dt = t.read(); |
geotsam | 1:20f48907c726 | 235 | t.reset(); |
geotsam | 1:20f48907c726 | 236 | t.start(); |
geotsam | 1:20f48907c726 | 237 | |
geotsam | 1:20f48907c726 | 238 | //Updating X,Y and theta with the odometry values |
geotsam | 1:20f48907c726 | 239 | this->myOdometria(); |
geotsam | 1:20f48907c726 | 240 | |
geotsam | 1:20f48907c726 | 241 | //Updating motor velocities |
geotsam | 1:20f48907c726 | 242 | distanceToTarget=this->update_angular_speed_wheels_go_to_point_with_angle(targetXWorld,targetYWorld,targetAngleWorld,dt); |
geotsam | 1:20f48907c726 | 243 | |
Ludwigfr | 5:19f24c363418 | 244 | //wait(0.2); |
geotsam | 1:20f48907c726 | 245 | //Timer stuff |
geotsam | 1:20f48907c726 | 246 | t.stop(); |
geotsam | 1:20f48907c726 | 247 | pc.printf("\n\rdist to target= %f",distanceToTarget); |
geotsam | 1:20f48907c726 | 248 | |
geotsam | 1:20f48907c726 | 249 | } while(distanceToTarget>1 || (abs(targetAngleWorld-this->thetaWorld)>0.1)); |
geotsam | 1:20f48907c726 | 250 | |
geotsam | 1:20f48907c726 | 251 | //Stop at the end |
geotsam | 1:20f48907c726 | 252 | leftMotor(1,0); |
geotsam | 1:20f48907c726 | 253 | rightMotor(1,0); |
geotsam | 1:20f48907c726 | 254 | pc.printf("\r\nReached Target!"); |
geotsam | 1:20f48907c726 | 255 | } |
geotsam | 1:20f48907c726 | 256 | |
Ludwigfr | 0:9f7ee7ed13e4 | 257 | float MiniExplorerCoimbra::update_angular_speed_wheels_go_to_point_with_angle(float targetXWorld, float targetYWorld, float targetAngleWorld, float dt){ |
Ludwigfr | 0:9f7ee7ed13e4 | 258 | //compute_angles_and_distance |
Ludwigfr | 0:9f7ee7ed13e4 | 259 | //atan2 take the deplacement on x and the deplacement on y as parameters |
Ludwigfr | 0:9f7ee7ed13e4 | 260 | float angleToPoint = atan2((targetYWorld-this->yWorld),(targetXWorld-this->xWorld))-this->thetaWorld; |
geotsam | 1:20f48907c726 | 261 | |
geotsam | 1:20f48907c726 | 262 | if(angleToPoint>PI) angleToPoint=-(angleToPoint-PI); |
Ludwigfr | 3:37345c109dfc | 263 | else if(angleToPoint<-PI) angleToPoint=-(angleToPoint+PI); |
geotsam | 1:20f48907c726 | 264 | |
Ludwigfr | 3:37345c109dfc | 265 | //rho is the distance to the point of arrival |
Ludwigfr | 0:9f7ee7ed13e4 | 266 | float rho = dist(targetXWorld,targetYWorld,this->xWorld,this->yWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 267 | float distanceToTarget = rho; |
Ludwigfr | 0:9f7ee7ed13e4 | 268 | //TODO check that |
Ludwigfr | 0:9f7ee7ed13e4 | 269 | float beta = targetAngleWorld-angleToPoint-this->thetaWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 270 | |
Ludwigfr | 0:9f7ee7ed13e4 | 271 | //Computing angle error and distance towards the target value |
Ludwigfr | 0:9f7ee7ed13e4 | 272 | rho += dt*(-this->khro*cos(angleToPoint)*rho); |
Ludwigfr | 0:9f7ee7ed13e4 | 273 | float temp = angleToPoint; |
Ludwigfr | 0:9f7ee7ed13e4 | 274 | angleToPoint += dt*(this->khro*sin(angleToPoint)-this->ka*angleToPoint-this->kb*beta); |
Ludwigfr | 0:9f7ee7ed13e4 | 275 | beta += dt*(-this->khro*sin(temp)); |
Ludwigfr | 0:9f7ee7ed13e4 | 276 | |
Ludwigfr | 0:9f7ee7ed13e4 | 277 | //Computing linear and angular velocities |
Ludwigfr | 0:9f7ee7ed13e4 | 278 | float linear; |
Ludwigfr | 0:9f7ee7ed13e4 | 279 | float angular; |
Ludwigfr | 0:9f7ee7ed13e4 | 280 | if(angleToPoint>=-1.5708 && angleToPoint<=1.5708){ |
Ludwigfr | 0:9f7ee7ed13e4 | 281 | linear=this->khro*rho; |
Ludwigfr | 0:9f7ee7ed13e4 | 282 | angular=this->ka*angleToPoint+this->kb*beta; |
Ludwigfr | 0:9f7ee7ed13e4 | 283 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 284 | else{ |
Ludwigfr | 0:9f7ee7ed13e4 | 285 | linear=-this->khro*rho; |
Ludwigfr | 0:9f7ee7ed13e4 | 286 | angular=-this->ka*angleToPoint-this->kb*beta; |
Ludwigfr | 0:9f7ee7ed13e4 | 287 | } |
geotsam | 1:20f48907c726 | 288 | |
geotsam | 1:20f48907c726 | 289 | float angularLeft=(linear-0.5*this->distanceWheels*angular)/this->radiusWheels; |
geotsam | 1:20f48907c726 | 290 | float angularRight=(linear+0.5*this->distanceWheels*angular)/this->radiusWheels; |
geotsam | 1:20f48907c726 | 291 | |
geotsam | 1:20f48907c726 | 292 | //Slowing down at the end for more precision |
geotsam | 1:20f48907c726 | 293 | if (distanceToTarget<30) { |
geotsam | 1:20f48907c726 | 294 | this->speed = distanceToTarget*10; |
geotsam | 1:20f48907c726 | 295 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 296 | |
Ludwigfr | 0:9f7ee7ed13e4 | 297 | //Normalize speed for motors |
geotsam | 1:20f48907c726 | 298 | if(angularLeft>angularRight) { |
geotsam | 1:20f48907c726 | 299 | angularRight=this->speed*angularRight/angularLeft; |
geotsam | 1:20f48907c726 | 300 | angularLeft=this->speed; |
Ludwigfr | 0:9f7ee7ed13e4 | 301 | } else { |
geotsam | 1:20f48907c726 | 302 | angularLeft=this->speed*angularLeft/angularRight; |
geotsam | 1:20f48907c726 | 303 | angularRight=this->speed; |
Ludwigfr | 0:9f7ee7ed13e4 | 304 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 305 | |
Ludwigfr | 0:9f7ee7ed13e4 | 306 | //compute_linear_angular_velocities |
geotsam | 1:20f48907c726 | 307 | leftMotor(1,angularLeft); |
geotsam | 1:20f48907c726 | 308 | rightMotor(1,angularRight); |
Ludwigfr | 0:9f7ee7ed13e4 | 309 | |
Ludwigfr | 0:9f7ee7ed13e4 | 310 | return distanceToTarget; |
Ludwigfr | 0:9f7ee7ed13e4 | 311 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 312 | |
Ludwigfr | 0:9f7ee7ed13e4 | 313 | void MiniExplorerCoimbra::update_sonar_values(float leftMm,float frontMm,float rightMm){ |
Ludwigfr | 0:9f7ee7ed13e4 | 314 | float xWorldCell; |
Ludwigfr | 0:9f7ee7ed13e4 | 315 | float yWorldCell; |
Ludwigfr | 3:37345c109dfc | 316 | float probaLeft; |
Ludwigfr | 3:37345c109dfc | 317 | float probaFront; |
Ludwigfr | 3:37345c109dfc | 318 | float probaRight; |
Ludwigfr | 3:37345c109dfc | 319 | float leftCm=leftMm/10; |
Ludwigfr | 3:37345c109dfc | 320 | float frontCm=frontMm/10; |
Ludwigfr | 3:37345c109dfc | 321 | float rightCm=rightMm/10; |
Ludwigfr | 0:9f7ee7ed13e4 | 322 | for(int i=0;i<this->map.nbCellWidth;i++){ |
Ludwigfr | 0:9f7ee7ed13e4 | 323 | for(int j=0;j<this->map.nbCellHeight;j++){ |
Ludwigfr | 0:9f7ee7ed13e4 | 324 | xWorldCell=this->map.cell_width_coordinate_to_world(i); |
Ludwigfr | 0:9f7ee7ed13e4 | 325 | yWorldCell=this->map.cell_height_coordinate_to_world(j); |
Ludwigfr | 3:37345c109dfc | 326 | |
Ludwigfr | 3:37345c109dfc | 327 | probaLeft=this->sonarLeft.compute_probability_t(leftCm,xWorldCell,yWorldCell,this->xWorld,this->yWorld,this->thetaWorld); |
Ludwigfr | 3:37345c109dfc | 328 | probaFront=this->sonarFront.compute_probability_t(frontCm,xWorldCell,yWorldCell,this->xWorld,this->yWorld,this->thetaWorld); |
Ludwigfr | 3:37345c109dfc | 329 | probaRight=this->sonarRight.compute_probability_t(rightCm,xWorldCell,yWorldCell,this->xWorld,this->yWorld,this->thetaWorld); |
Ludwigfr | 3:37345c109dfc | 330 | |
Ludwigfr | 3:37345c109dfc | 331 | /* |
Ludwigfr | 3:37345c109dfc | 332 | pc.printf("\n\r leftCm= %f",leftCm); |
Ludwigfr | 3:37345c109dfc | 333 | pc.printf("\n\r frontCm= %f",frontCm); |
Ludwigfr | 3:37345c109dfc | 334 | pc.printf("\n\r rightCm= %f",rightCm); |
Ludwigfr | 3:37345c109dfc | 335 | */ |
Ludwigfr | 3:37345c109dfc | 336 | /* |
Ludwigfr | 3:37345c109dfc | 337 | pc.printf("\n\r probaLeft= %f",probaLeft); |
Ludwigfr | 3:37345c109dfc | 338 | pc.printf("\n\r probaFront= %f",probaFront); |
Ludwigfr | 3:37345c109dfc | 339 | pc.printf("\n\r probaRight= %f",probaRight); |
Ludwigfr | 3:37345c109dfc | 340 | if(probaLeft> 1 || probaLeft < 0 || probaFront> 1 || probaFront < 0 ||probaRight> 1 || probaRight < 0)){ |
Ludwigfr | 3:37345c109dfc | 341 | pwm_buzzer.pulsewidth_us(250); |
Ludwigfr | 3:37345c109dfc | 342 | wait_ms(50); |
Ludwigfr | 3:37345c109dfc | 343 | pwm_buzzer.pulsewidth_us(0); |
Ludwigfr | 3:37345c109dfc | 344 | wait(20); |
Ludwigfr | 3:37345c109dfc | 345 | pwm_buzzer.pulsewidth_us(250); |
Ludwigfr | 3:37345c109dfc | 346 | wait_ms(50); |
Ludwigfr | 3:37345c109dfc | 347 | pwm_buzzer.pulsewidth_us(0); |
Ludwigfr | 3:37345c109dfc | 348 | } |
Ludwigfr | 3:37345c109dfc | 349 | */ |
Ludwigfr | 3:37345c109dfc | 350 | this->map.update_cell_value(i,j,probaLeft); |
Ludwigfr | 3:37345c109dfc | 351 | this->map.update_cell_value(i,j,probaFront); |
Ludwigfr | 3:37345c109dfc | 352 | this->map.update_cell_value(i,j,probaRight); |
Ludwigfr | 0:9f7ee7ed13e4 | 353 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 354 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 355 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 356 | |
Ludwigfr | 0:9f7ee7ed13e4 | 357 | void MiniExplorerCoimbra::do_half_flip(){ |
Ludwigfr | 0:9f7ee7ed13e4 | 358 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 359 | float theta_plus_h_pi=theta+PI/2;//theta is between -PI and PI |
Ludwigfr | 0:9f7ee7ed13e4 | 360 | if(theta_plus_h_pi > PI) |
Ludwigfr | 0:9f7ee7ed13e4 | 361 | theta_plus_h_pi=-(2*PI-theta_plus_h_pi); |
Ludwigfr | 0:9f7ee7ed13e4 | 362 | leftMotor(0,100); |
Ludwigfr | 0:9f7ee7ed13e4 | 363 | rightMotor(1,100); |
Ludwigfr | 0:9f7ee7ed13e4 | 364 | while(abs(theta_plus_h_pi-theta)>0.05){ |
Ludwigfr | 0:9f7ee7ed13e4 | 365 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 366 | // pc.printf("\n\r diff=%f", abs(theta_plus_pi-theta)); |
Ludwigfr | 0:9f7ee7ed13e4 | 367 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 368 | leftMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 369 | rightMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 370 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 371 | |
Ludwigfr | 0:9f7ee7ed13e4 | 372 | //Distance computation function |
Ludwigfr | 0:9f7ee7ed13e4 | 373 | float MiniExplorerCoimbra::dist(float x1, float y1, float x2, float y2){ |
Ludwigfr | 0:9f7ee7ed13e4 | 374 | return sqrt(pow(y2-y1,2) + pow(x2-x1,2)); |
Ludwigfr | 0:9f7ee7ed13e4 | 375 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 376 | |
Ludwigfr | 0:9f7ee7ed13e4 | 377 | //use virtual force field |
Ludwigfr | 0:9f7ee7ed13e4 | 378 | void MiniExplorerCoimbra::try_to_reach_target(float targetXWorld,float targetYWorld){ |
Ludwigfr | 0:9f7ee7ed13e4 | 379 | //atan2 gives the angle beetween PI and -PI |
Ludwigfr | 0:9f7ee7ed13e4 | 380 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 381 | /* |
Ludwigfr | 0:9f7ee7ed13e4 | 382 | float deplacementOnXWorld=targetXWorld-this->xWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 383 | float deplacementOnYWorld=targetYWorld-this->yWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 384 | */ |
Ludwigfr | 3:37345c109dfc | 385 | //float angleToTarget=atan2(targetYWorld-this->yWorld,targetXWorld-this->xWorld); |
Ludwigfr | 3:37345c109dfc | 386 | //pc.printf("\n angleToTarget=%f",angleToTarget); |
Ludwigfr | 3:37345c109dfc | 387 | //turn_to_target(angleToTarget); |
Ludwigfr | 3:37345c109dfc | 388 | //TODO IDEA check if maybe set a low max range |
Ludwigfr | 3:37345c109dfc | 389 | //this->sonarLeft.setMaxRange(30); |
Ludwigfr | 3:37345c109dfc | 390 | //this->sonarFront.setMaxRange(30); |
Ludwigfr | 3:37345c109dfc | 391 | //this->sonarRight.setMaxRange(30); |
Ludwigfr | 0:9f7ee7ed13e4 | 392 | bool reached=false; |
Ludwigfr | 0:9f7ee7ed13e4 | 393 | int print=0; |
Ludwigfr | 3:37345c109dfc | 394 | int printLimit=1000; |
Ludwigfr | 0:9f7ee7ed13e4 | 395 | while (!reached) { |
Ludwigfr | 0:9f7ee7ed13e4 | 396 | this->vff(&reached,targetXWorld,targetYWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 397 | //test_got_to_line(&reached); |
Ludwigfr | 3:37345c109dfc | 398 | if(print==printLimit){ |
Ludwigfr | 0:9f7ee7ed13e4 | 399 | leftMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 400 | rightMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 401 | this->print_map_with_robot_position_and_target(targetXWorld,targetYWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 402 | print=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 403 | }else |
Ludwigfr | 0:9f7ee7ed13e4 | 404 | print+=1; |
Ludwigfr | 0:9f7ee7ed13e4 | 405 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 406 | //Stop at the end |
Ludwigfr | 0:9f7ee7ed13e4 | 407 | leftMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 408 | rightMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 409 | pc.printf("\r\n target reached"); |
Ludwigfr | 5:19f24c363418 | 410 | //wait(3);// |
Ludwigfr | 0:9f7ee7ed13e4 | 411 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 412 | |
Ludwigfr | 0:9f7ee7ed13e4 | 413 | void MiniExplorerCoimbra::vff(bool* reached, float targetXWorld, float targetYWorld){ |
Ludwigfr | 0:9f7ee7ed13e4 | 414 | float line_a; |
Ludwigfr | 0:9f7ee7ed13e4 | 415 | float line_b; |
Ludwigfr | 0:9f7ee7ed13e4 | 416 | float line_c; |
Ludwigfr | 0:9f7ee7ed13e4 | 417 | //Updating X,Y and theta with the odometry values |
Ludwigfr | 0:9f7ee7ed13e4 | 418 | float forceXWorld=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 419 | float forceYWorld=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 420 | //we update the odometrie |
Ludwigfr | 0:9f7ee7ed13e4 | 421 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 422 | //we check the sensors |
Ludwigfr | 0:9f7ee7ed13e4 | 423 | float leftMm = get_distance_left_sensor(); |
Ludwigfr | 0:9f7ee7ed13e4 | 424 | float frontMm = get_distance_front_sensor(); |
Ludwigfr | 0:9f7ee7ed13e4 | 425 | float rightMm = get_distance_right_sensor(); |
Ludwigfr | 0:9f7ee7ed13e4 | 426 | //update the probabilities values |
Ludwigfr | 0:9f7ee7ed13e4 | 427 | this->update_sonar_values(leftMm, frontMm, rightMm); |
Ludwigfr | 0:9f7ee7ed13e4 | 428 | //we compute the force on X and Y |
Ludwigfr | 0:9f7ee7ed13e4 | 429 | this->compute_forceX_and_forceY(&forceXWorld, &forceYWorld,targetXWorld,targetYWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 430 | //we compute a new ine |
Ludwigfr | 0:9f7ee7ed13e4 | 431 | this->calculate_line(forceXWorld, forceYWorld, &line_a,&line_b,&line_c); |
Ludwigfr | 0:9f7ee7ed13e4 | 432 | //Updating motor velocities |
Ludwigfr | 0:9f7ee7ed13e4 | 433 | this->go_to_line(line_a,line_b,line_c,targetXWorld,targetYWorld); |
Ludwigfr | 3:37345c109dfc | 434 | |
Ludwigfr | 0:9f7ee7ed13e4 | 435 | //wait(0.1); |
Ludwigfr | 0:9f7ee7ed13e4 | 436 | this->myOdometria(); |
Ludwigfr | 3:37345c109dfc | 437 | if(dist(this->xWorld,this->yWorld,targetXWorld,targetYWorld)<3) |
Ludwigfr | 0:9f7ee7ed13e4 | 438 | *reached=true; |
Ludwigfr | 0:9f7ee7ed13e4 | 439 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 440 | |
Ludwigfr | 0:9f7ee7ed13e4 | 441 | /*angleToTarget is obtained through atan2 so it s: |
Ludwigfr | 0:9f7ee7ed13e4 | 442 | < 0 if the angle is bettween PI and 2pi on a trigo circle |
Ludwigfr | 0:9f7ee7ed13e4 | 443 | > 0 if it is between 0 and PI |
Ludwigfr | 0:9f7ee7ed13e4 | 444 | */ |
Ludwigfr | 0:9f7ee7ed13e4 | 445 | void MiniExplorerCoimbra::turn_to_target(float angleToTarget){ |
Ludwigfr | 0:9f7ee7ed13e4 | 446 | this->myOdometria(); |
Ludwigfr | 3:37345c109dfc | 447 | if(angleToTarget!=0){ |
Ludwigfr | 3:37345c109dfc | 448 | if(angleToTarget>0){ |
Ludwigfr | 3:37345c109dfc | 449 | leftMotor(0,1); |
Ludwigfr | 3:37345c109dfc | 450 | rightMotor(1,1); |
Ludwigfr | 3:37345c109dfc | 451 | }else{ |
Ludwigfr | 3:37345c109dfc | 452 | leftMotor(1,1); |
Ludwigfr | 3:37345c109dfc | 453 | rightMotor(0,1); |
Ludwigfr | 3:37345c109dfc | 454 | } |
Ludwigfr | 3:37345c109dfc | 455 | while(abs(angleToTarget-this->thetaWorld)>0.05) |
Ludwigfr | 3:37345c109dfc | 456 | this->myOdometria(); |
Ludwigfr | 0:9f7ee7ed13e4 | 457 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 458 | leftMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 459 | rightMotor(1,0); |
Ludwigfr | 0:9f7ee7ed13e4 | 460 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 461 | |
Ludwigfr | 0:9f7ee7ed13e4 | 462 | |
Ludwigfr | 0:9f7ee7ed13e4 | 463 | void MiniExplorerCoimbra::print_map_with_robot_position_and_target(float targetXWorld, float targetYWorld) { |
Ludwigfr | 0:9f7ee7ed13e4 | 464 | float currProba; |
Ludwigfr | 0:9f7ee7ed13e4 | 465 | |
Ludwigfr | 0:9f7ee7ed13e4 | 466 | float heightIndiceInOrthonormal; |
Ludwigfr | 0:9f7ee7ed13e4 | 467 | float widthIndiceInOrthonormal; |
Ludwigfr | 0:9f7ee7ed13e4 | 468 | |
Ludwigfr | 0:9f7ee7ed13e4 | 469 | float widthMalus=-(3*this->map.sizeCellWidth/2); |
Ludwigfr | 0:9f7ee7ed13e4 | 470 | float widthBonus=this->map.sizeCellWidth/2; |
Ludwigfr | 0:9f7ee7ed13e4 | 471 | |
Ludwigfr | 0:9f7ee7ed13e4 | 472 | float heightMalus=-(3*this->map.sizeCellHeight/2); |
Ludwigfr | 0:9f7ee7ed13e4 | 473 | float heightBonus=this->map.sizeCellHeight/2; |
Ludwigfr | 0:9f7ee7ed13e4 | 474 | |
Ludwigfr | 0:9f7ee7ed13e4 | 475 | pc.printf("\n\r"); |
Ludwigfr | 0:9f7ee7ed13e4 | 476 | for (int y = this->map.nbCellHeight -1; y>-1; y--) { |
Ludwigfr | 0:9f7ee7ed13e4 | 477 | for (int x= 0; x<this->map.nbCellWidth; x++) { |
Ludwigfr | 0:9f7ee7ed13e4 | 478 | heightIndiceInOrthonormal=this->map.cell_height_coordinate_to_world(y); |
Ludwigfr | 0:9f7ee7ed13e4 | 479 | widthIndiceInOrthonormal=this->map.cell_width_coordinate_to_world(x); |
Ludwigfr | 0:9f7ee7ed13e4 | 480 | if(this->yWorld >= (heightIndiceInOrthonormal+heightMalus) && this->yWorld <= (heightIndiceInOrthonormal+heightBonus) && this->xWorld >= (widthIndiceInOrthonormal+widthMalus) && this->xWorld <= (widthIndiceInOrthonormal+widthBonus)) |
Ludwigfr | 0:9f7ee7ed13e4 | 481 | pc.printf(" R "); |
Ludwigfr | 0:9f7ee7ed13e4 | 482 | else{ |
Ludwigfr | 0:9f7ee7ed13e4 | 483 | if(targetYWorld >= (heightIndiceInOrthonormal+heightMalus) && targetYWorld <= (heightIndiceInOrthonormal+heightBonus) && targetXWorld >= (widthIndiceInOrthonormal+widthMalus) && targetXWorld <= (widthIndiceInOrthonormal+widthBonus)) |
Ludwigfr | 0:9f7ee7ed13e4 | 484 | pc.printf(" T "); |
Ludwigfr | 0:9f7ee7ed13e4 | 485 | else{ |
Ludwigfr | 0:9f7ee7ed13e4 | 486 | currProba=this->map.log_to_proba(this->map.cellsLogValues[x][y]); |
Ludwigfr | 5:19f24c363418 | 487 | if ( currProba < 0.5){ |
Ludwigfr | 0:9f7ee7ed13e4 | 488 | pc.printf(" "); |
Ludwigfr | 5:19f24c363418 | 489 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 490 | }else{ |
Ludwigfr | 5:19f24c363418 | 491 | if(currProba==0.5){ |
Ludwigfr | 0:9f7ee7ed13e4 | 492 | pc.printf(" . "); |
Ludwigfr | 5:19f24c363418 | 493 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 494 | }else{ |
Ludwigfr | 0:9f7ee7ed13e4 | 495 | pc.printf(" X "); |
Ludwigfr | 5:19f24c363418 | 496 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 497 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 498 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 499 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 500 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 501 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 502 | pc.printf("\n\r"); |
Ludwigfr | 0:9f7ee7ed13e4 | 503 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 504 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 505 | |
Ludwigfr | 2:11cd5173aa36 | 506 | void MiniExplorerCoimbra::print_map_with_robot_position(){ |
Ludwigfr | 2:11cd5173aa36 | 507 | float currProba; |
Ludwigfr | 2:11cd5173aa36 | 508 | |
Ludwigfr | 2:11cd5173aa36 | 509 | float heightIndiceInOrthonormal; |
Ludwigfr | 2:11cd5173aa36 | 510 | float widthIndiceInOrthonormal; |
Ludwigfr | 2:11cd5173aa36 | 511 | |
Ludwigfr | 2:11cd5173aa36 | 512 | float widthMalus=-(3*this->map.sizeCellWidth/2); |
Ludwigfr | 2:11cd5173aa36 | 513 | float widthBonus=this->map.sizeCellWidth/2; |
Ludwigfr | 2:11cd5173aa36 | 514 | |
Ludwigfr | 2:11cd5173aa36 | 515 | float heightMalus=-(3*this->map.sizeCellHeight/2); |
Ludwigfr | 2:11cd5173aa36 | 516 | float heightBonus=this->map.sizeCellHeight/2; |
Ludwigfr | 2:11cd5173aa36 | 517 | |
Ludwigfr | 2:11cd5173aa36 | 518 | pc.printf("\n\r"); |
Ludwigfr | 2:11cd5173aa36 | 519 | for (int y = this->map.nbCellHeight -1; y>-1; y--) { |
Ludwigfr | 2:11cd5173aa36 | 520 | for (int x= 0; x<this->map.nbCellWidth; x++) { |
Ludwigfr | 2:11cd5173aa36 | 521 | heightIndiceInOrthonormal=this->map.cell_height_coordinate_to_world(y); |
Ludwigfr | 2:11cd5173aa36 | 522 | widthIndiceInOrthonormal=this->map.cell_width_coordinate_to_world(x); |
Ludwigfr | 5:19f24c363418 | 523 | if(this->yWorld >= (heightIndiceInOrthonormal+heightMalus) && this->yWorld <= (heightIndiceInOrthonormal+heightBonus) && this->xWorld >= (widthIndiceInOrthonormal+widthMalus) && this->xWorld <= (widthIndiceInOrthonormal+widthBonus)){ |
Ludwigfr | 2:11cd5173aa36 | 524 | pc.printf(" R "); |
Ludwigfr | 5:19f24c363418 | 525 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 526 | }else{ |
Ludwigfr | 2:11cd5173aa36 | 527 | currProba=this->map.log_to_proba(this->map.cellsLogValues[x][y]); |
Ludwigfr | 5:19f24c363418 | 528 | if ( currProba < 0.5){ |
Ludwigfr | 2:11cd5173aa36 | 529 | pc.printf(" "); |
Ludwigfr | 5:19f24c363418 | 530 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 531 | }else{ |
Ludwigfr | 5:19f24c363418 | 532 | if(currProba==0.5){ |
Ludwigfr | 2:11cd5173aa36 | 533 | pc.printf(" . "); |
Ludwigfr | 5:19f24c363418 | 534 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 535 | }else{ |
Ludwigfr | 5:19f24c363418 | 536 | pc.printf(" X "); |
Ludwigfr | 5:19f24c363418 | 537 | //pc.printf("%f",currProba); |
Ludwigfr | 5:19f24c363418 | 538 | } |
Ludwigfr | 2:11cd5173aa36 | 539 | } |
Ludwigfr | 2:11cd5173aa36 | 540 | } |
Ludwigfr | 2:11cd5173aa36 | 541 | } |
Ludwigfr | 2:11cd5173aa36 | 542 | pc.printf("\n\r"); |
Ludwigfr | 2:11cd5173aa36 | 543 | } |
Ludwigfr | 2:11cd5173aa36 | 544 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 545 | |
Ludwigfr | 0:9f7ee7ed13e4 | 546 | //robotX and robotY are from this->myOdometria(), calculate line_a, line_b and line_c |
Ludwigfr | 0:9f7ee7ed13e4 | 547 | void MiniExplorerCoimbra::calculate_line(float forceX, float forceY, float *line_a, float *line_b, float *line_c){ |
Ludwigfr | 0:9f7ee7ed13e4 | 548 | /* |
Ludwigfr | 0:9f7ee7ed13e4 | 549 | in the teachers maths it is |
Ludwigfr | 0:9f7ee7ed13e4 | 550 | |
Ludwigfr | 0:9f7ee7ed13e4 | 551 | *line_a=forceY; |
Ludwigfr | 0:9f7ee7ed13e4 | 552 | *line_b=-forceX; |
Ludwigfr | 0:9f7ee7ed13e4 | 553 | |
Ludwigfr | 0:9f7ee7ed13e4 | 554 | because a*x+b*y+c=0 |
Ludwigfr | 0:9f7ee7ed13e4 | 555 | a impact the vertical and b the horizontal |
Ludwigfr | 0:9f7ee7ed13e4 | 556 | and he has to put them like this because |
Ludwigfr | 0:9f7ee7ed13e4 | 557 | Robot space: World space: |
Ludwigfr | 0:9f7ee7ed13e4 | 558 | ^ ^ |
Ludwigfr | 0:9f7ee7ed13e4 | 559 | |x |y |
Ludwigfr | 0:9f7ee7ed13e4 | 560 | <- R O -> |
Ludwigfr | 0:9f7ee7ed13e4 | 561 | y x |
Ludwigfr | 0:9f7ee7ed13e4 | 562 | but since our forceX, forceY are already computed in the orthonormal space I m not sure we need to |
Ludwigfr | 0:9f7ee7ed13e4 | 563 | */ |
Ludwigfr | 3:37345c109dfc | 564 | //*line_a=forceX; |
Ludwigfr | 3:37345c109dfc | 565 | //*line_b=forceY; |
Ludwigfr | 3:37345c109dfc | 566 | |
Ludwigfr | 3:37345c109dfc | 567 | *line_a=forceY; |
Ludwigfr | 3:37345c109dfc | 568 | *line_b=-forceX; |
Ludwigfr | 0:9f7ee7ed13e4 | 569 | //because the line computed always pass by the robot center we dont need lince_c |
Ludwigfr | 0:9f7ee7ed13e4 | 570 | //*line_c=forceX*this->yWorld+forceY*this->xWorld; |
Ludwigfr | 0:9f7ee7ed13e4 | 571 | *line_c=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 572 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 573 | //compute the force on X and Y |
Ludwigfr | 0:9f7ee7ed13e4 | 574 | void MiniExplorerCoimbra::compute_forceX_and_forceY(float* forceXWorld, float* forceYWorld, float targetXWorld, float targetYWorld){ |
Ludwigfr | 0:9f7ee7ed13e4 | 575 | float forceRepulsionComputedX=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 576 | float forceRepulsionComputedY=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 577 | for(int i=0;i<this->map.nbCellWidth;i++){ //for each cell of the map we compute a force of repulsion |
Ludwigfr | 0:9f7ee7ed13e4 | 578 | for(int j=0;j<this->map.nbCellHeight;j++){ |
Ludwigfr | 0:9f7ee7ed13e4 | 579 | this->update_force(i,j,&forceRepulsionComputedX,&forceRepulsionComputedY); |
Ludwigfr | 0:9f7ee7ed13e4 | 580 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 581 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 582 | //update with attraction force |
Ludwigfr | 3:37345c109dfc | 583 | *forceXWorld=forceRepulsionComputedX; |
Ludwigfr | 3:37345c109dfc | 584 | *forceYWorld=forceRepulsionComputedY; |
Ludwigfr | 3:37345c109dfc | 585 | this->print_map_with_robot_position(); |
Ludwigfr | 5:19f24c363418 | 586 | pc.printf("\r\nForce X repul:%f",*forceXWorld); |
Ludwigfr | 5:19f24c363418 | 587 | pc.printf("\r\nForce Y repul:%f",*forceYWorld); |
Ludwigfr | 0:9f7ee7ed13e4 | 588 | float distanceTargetRobot=sqrt(pow(targetXWorld-this->xWorld,2)+pow(targetYWorld-this->yWorld,2)); |
Ludwigfr | 0:9f7ee7ed13e4 | 589 | if(distanceTargetRobot != 0){ |
Ludwigfr | 3:37345c109dfc | 590 | *forceXWorld+=-this->attractionConstantForce*(targetXWorld-this->xWorld)/distanceTargetRobot; |
Ludwigfr | 3:37345c109dfc | 591 | *forceYWorld+=-this->attractionConstantForce*(targetYWorld-this->yWorld)/distanceTargetRobot; |
Ludwigfr | 3:37345c109dfc | 592 | }else{ |
Ludwigfr | 3:37345c109dfc | 593 | *forceXWorld+=-this->attractionConstantForce*(targetXWorld-this->xWorld)/0.01; |
Ludwigfr | 3:37345c109dfc | 594 | *forceYWorld+=-this->attractionConstantForce*(targetYWorld-this->yWorld)/0.01; |
Ludwigfr | 0:9f7ee7ed13e4 | 595 | } |
Ludwigfr | 5:19f24c363418 | 596 | pc.printf("\r\nForce X after attract:%f",*forceXWorld); |
Ludwigfr | 5:19f24c363418 | 597 | pc.printf("\r\nForce Y after attract:%f",*forceYWorld); |
Ludwigfr | 3:37345c109dfc | 598 | |
Ludwigfr | 0:9f7ee7ed13e4 | 599 | float amplitude=sqrt(pow(*forceXWorld,2)+pow(*forceYWorld,2)); |
Ludwigfr | 0:9f7ee7ed13e4 | 600 | if(amplitude!=0){//avoid division by 0 if forceX and forceY == 0 |
Ludwigfr | 0:9f7ee7ed13e4 | 601 | *forceXWorld=*forceXWorld/amplitude; |
Ludwigfr | 0:9f7ee7ed13e4 | 602 | *forceYWorld=*forceYWorld/amplitude; |
Ludwigfr | 3:37345c109dfc | 603 | }else{ |
Ludwigfr | 3:37345c109dfc | 604 | *forceXWorld=*forceXWorld/0.01; |
Ludwigfr | 3:37345c109dfc | 605 | *forceYWorld=*forceYWorld/0.01; |
Ludwigfr | 0:9f7ee7ed13e4 | 606 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 607 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 608 | |
Ludwigfr | 2:11cd5173aa36 | 609 | //for vff |
Ludwigfr | 0:9f7ee7ed13e4 | 610 | void MiniExplorerCoimbra::go_to_line(float line_a, float line_b, float line_c,float targetXWorld, float targetYWorld){ |
Ludwigfr | 0:9f7ee7ed13e4 | 611 | float lineAngle; |
Ludwigfr | 0:9f7ee7ed13e4 | 612 | float angleError; |
Ludwigfr | 0:9f7ee7ed13e4 | 613 | float linear; |
Ludwigfr | 0:9f7ee7ed13e4 | 614 | float angular; |
Ludwigfr | 2:11cd5173aa36 | 615 | float d; |
Ludwigfr | 0:9f7ee7ed13e4 | 616 | |
Ludwigfr | 0:9f7ee7ed13e4 | 617 | //line angle is beetween pi/2 and -pi/2 |
Ludwigfr | 2:11cd5173aa36 | 618 | |
Ludwigfr | 2:11cd5173aa36 | 619 | if(line_b!=0){ |
Ludwigfr | 2:11cd5173aa36 | 620 | lineAngle=atan(line_a/-line_b); |
Ludwigfr | 0:9f7ee7ed13e4 | 621 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 622 | else{ |
Ludwigfr | 3:37345c109dfc | 623 | lineAngle=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 624 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 625 | |
Ludwigfr | 2:11cd5173aa36 | 626 | this->myOdometria(); |
Ludwigfr | 2:11cd5173aa36 | 627 | //Computing angle error |
Ludwigfr | 2:11cd5173aa36 | 628 | angleError = lineAngle-this->thetaWorld;//TODO that I m not sure |
Ludwigfr | 2:11cd5173aa36 | 629 | if(angleError>PI) |
Ludwigfr | 2:11cd5173aa36 | 630 | angleError=-(angleError-PI); |
Ludwigfr | 2:11cd5173aa36 | 631 | else |
Ludwigfr | 2:11cd5173aa36 | 632 | if(angleError<-PI) |
Ludwigfr | 2:11cd5173aa36 | 633 | angleError=-(angleError+PI); |
Ludwigfr | 2:11cd5173aa36 | 634 | |
Ludwigfr | 3:37345c109dfc | 635 | //d=this->distFromLine(this->xWorld, this->yWorld, line_a, line_b, line_c);//this could be 0 |
Ludwigfr | 3:37345c109dfc | 636 | d=0; |
Ludwigfr | 0:9f7ee7ed13e4 | 637 | //Calculating velocities |
Ludwigfr | 2:11cd5173aa36 | 638 | linear= this->kv*(3.14); |
Ludwigfr | 2:11cd5173aa36 | 639 | angular=-this->kd*d + this->kh*angleError; |
Ludwigfr | 0:9f7ee7ed13e4 | 640 | |
Ludwigfr | 0:9f7ee7ed13e4 | 641 | float angularLeft=(linear-0.5*this->distanceWheels*angular)/this->radiusWheels; |
Ludwigfr | 2:11cd5173aa36 | 642 | float angularRight=(linear+0.5*this->distanceWheels*angular)/this->radiusWheels; |
Ludwigfr | 0:9f7ee7ed13e4 | 643 | |
Ludwigfr | 2:11cd5173aa36 | 644 | //Normalize speed for motors |
Ludwigfr | 0:9f7ee7ed13e4 | 645 | if(abs(angularLeft)>abs(angularRight)) { |
Ludwigfr | 0:9f7ee7ed13e4 | 646 | angularRight=this->speed*abs(angularRight/angularLeft)*this->sign1(angularRight); |
Ludwigfr | 0:9f7ee7ed13e4 | 647 | angularLeft=this->speed*this->sign1(angularLeft); |
Ludwigfr | 0:9f7ee7ed13e4 | 648 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 649 | else { |
Ludwigfr | 0:9f7ee7ed13e4 | 650 | angularLeft=this->speed*abs(angularLeft/angularRight)*this->sign1(angularLeft); |
Ludwigfr | 0:9f7ee7ed13e4 | 651 | angularRight=this->speed*this->sign1(angularRight); |
Ludwigfr | 0:9f7ee7ed13e4 | 652 | } |
Ludwigfr | 2:11cd5173aa36 | 653 | |
Ludwigfr | 2:11cd5173aa36 | 654 | pc.printf("\r\nd = %f", d); |
Ludwigfr | 3:37345c109dfc | 655 | pc.printf("\r\nerror = %f, lineAngle=%f, robotAngle=%f\n", angleError,lineAngle,this->thetaWorld); |
Ludwigfr | 2:11cd5173aa36 | 656 | |
Ludwigfr | 0:9f7ee7ed13e4 | 657 | leftMotor(this->sign2(angularLeft),abs(angularLeft)); |
Ludwigfr | 0:9f7ee7ed13e4 | 658 | rightMotor(this->sign2(angularRight),abs(angularRight)); |
Ludwigfr | 0:9f7ee7ed13e4 | 659 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 660 | |
geotsam | 1:20f48907c726 | 661 | void MiniExplorerCoimbra::go_to_line_first_lab(float line_a, float line_b, float line_c){ |
geotsam | 1:20f48907c726 | 662 | float lineAngle; |
geotsam | 1:20f48907c726 | 663 | float angleError; |
geotsam | 1:20f48907c726 | 664 | float linear; |
geotsam | 1:20f48907c726 | 665 | float angular; |
geotsam | 1:20f48907c726 | 666 | float d; |
geotsam | 1:20f48907c726 | 667 | |
geotsam | 1:20f48907c726 | 668 | //line angle is beetween pi/2 and -pi/2 |
geotsam | 1:20f48907c726 | 669 | |
geotsam | 1:20f48907c726 | 670 | if(line_b!=0){ |
geotsam | 1:20f48907c726 | 671 | lineAngle=atan(line_a/-line_b); |
geotsam | 1:20f48907c726 | 672 | } |
geotsam | 1:20f48907c726 | 673 | else{ |
geotsam | 1:20f48907c726 | 674 | lineAngle=1.5708; |
geotsam | 1:20f48907c726 | 675 | } |
geotsam | 1:20f48907c726 | 676 | |
geotsam | 1:20f48907c726 | 677 | do{ |
geotsam | 1:20f48907c726 | 678 | this->myOdometria(); |
geotsam | 1:20f48907c726 | 679 | //Computing angle error |
Ludwigfr | 3:37345c109dfc | 680 | pc.printf("\r\nline angle = %f", lineAngle); |
Ludwigfr | 3:37345c109dfc | 681 | pc.printf("\r\nthetaWorld = %f", thetaWorld); |
geotsam | 1:20f48907c726 | 682 | angleError = lineAngle-this->thetaWorld;//TODO that I m not sure |
geotsam | 1:20f48907c726 | 683 | |
Ludwigfr | 3:37345c109dfc | 684 | if(angleError>PI) |
Ludwigfr | 3:37345c109dfc | 685 | angleError=-(angleError-PI); |
Ludwigfr | 3:37345c109dfc | 686 | else |
Ludwigfr | 3:37345c109dfc | 687 | if(angleError<-PI) |
Ludwigfr | 3:37345c109dfc | 688 | angleError=-(angleError+PI); |
geotsam | 1:20f48907c726 | 689 | |
Ludwigfr | 3:37345c109dfc | 690 | pc.printf("\r\nangleError = %f\n", angleError); |
geotsam | 1:20f48907c726 | 691 | d=this->distFromLine(xWorld, yWorld, line_a, line_b, line_c); |
Ludwigfr | 3:37345c109dfc | 692 | pc.printf("\r\ndistance to line = %f", d); |
geotsam | 1:20f48907c726 | 693 | |
geotsam | 1:20f48907c726 | 694 | //Calculating velocities |
geotsam | 1:20f48907c726 | 695 | linear= this->kv*(3.14); |
geotsam | 1:20f48907c726 | 696 | angular=-this->kd*d + this->kh*angleError; |
geotsam | 1:20f48907c726 | 697 | |
geotsam | 1:20f48907c726 | 698 | float angularLeft=(linear-0.5*this->distanceWheels*angular)/this->radiusWheels; |
geotsam | 1:20f48907c726 | 699 | float angularRight=(linear+0.5*this->distanceWheels*angular)/this->radiusWheels; |
geotsam | 1:20f48907c726 | 700 | |
geotsam | 1:20f48907c726 | 701 | //Normalize speed for motors |
geotsam | 1:20f48907c726 | 702 | if(abs(angularLeft)>abs(angularRight)) { |
geotsam | 1:20f48907c726 | 703 | angularRight=this->speed*abs(angularRight/angularLeft)*this->sign1(angularRight); |
geotsam | 1:20f48907c726 | 704 | angularLeft=this->speed*this->sign1(angularLeft); |
geotsam | 1:20f48907c726 | 705 | } |
geotsam | 1:20f48907c726 | 706 | else { |
geotsam | 1:20f48907c726 | 707 | angularLeft=this->speed*abs(angularLeft/angularRight)*this->sign1(angularLeft); |
geotsam | 1:20f48907c726 | 708 | angularRight=this->speed*this->sign1(angularRight); |
geotsam | 1:20f48907c726 | 709 | } |
geotsam | 1:20f48907c726 | 710 | |
geotsam | 1:20f48907c726 | 711 | leftMotor(this->sign2(angularLeft),abs(angularLeft)); |
geotsam | 1:20f48907c726 | 712 | rightMotor(this->sign2(angularRight),abs(angularRight)); |
geotsam | 1:20f48907c726 | 713 | }while(1); |
geotsam | 1:20f48907c726 | 714 | } |
geotsam | 1:20f48907c726 | 715 | |
Ludwigfr | 0:9f7ee7ed13e4 | 716 | void MiniExplorerCoimbra::update_force(int widthIndice, int heightIndice, float* forceRepulsionComputedX, float* forceRepulsionComputedY ){ |
Ludwigfr | 0:9f7ee7ed13e4 | 717 | //get the coordonate of the map and the robot in the ortonormal space |
Ludwigfr | 0:9f7ee7ed13e4 | 718 | float xWorldCell=this->map.cell_width_coordinate_to_world(widthIndice); |
Ludwigfr | 0:9f7ee7ed13e4 | 719 | float yWorldCell=this->map.cell_height_coordinate_to_world(heightIndice); |
Ludwigfr | 0:9f7ee7ed13e4 | 720 | //compute the distance beetween the cell and the robot |
Ludwigfr | 0:9f7ee7ed13e4 | 721 | float distanceCellToRobot=sqrt(pow(xWorldCell-this->xWorld,2)+pow(yWorldCell-this->yWorld,2)); |
Ludwigfr | 3:37345c109dfc | 722 | float probaCell; |
Ludwigfr | 0:9f7ee7ed13e4 | 723 | //check if the cell is in range |
Ludwigfr | 3:37345c109dfc | 724 | float anglePointToRobot=atan2(yWorldCell-this->yWorld,xWorldCell-this->xWorld);//like world system |
Ludwigfr | 3:37345c109dfc | 725 | float temp1; |
Ludwigfr | 3:37345c109dfc | 726 | float temp2; |
Ludwigfr | 0:9f7ee7ed13e4 | 727 | if(distanceCellToRobot <= this->rangeForce) { |
Ludwigfr | 3:37345c109dfc | 728 | probaCell=this->map.get_proba_cell(widthIndice,heightIndice); |
Ludwigfr | 3:37345c109dfc | 729 | pc.printf("\r\nupdate force proba:%f",probaCell); |
Ludwigfr | 3:37345c109dfc | 730 | temp1=this->repulsionConstantForce*probaCell/pow(distanceCellToRobot,2); |
Ludwigfr | 3:37345c109dfc | 731 | temp2=(xWorldCell-this->xWorld)/distanceCellToRobot; |
Ludwigfr | 3:37345c109dfc | 732 | *forceRepulsionComputedX+=temp1*temp2; |
Ludwigfr | 3:37345c109dfc | 733 | temp2=(yWorldCell-this->yWorld)/distanceCellToRobot; |
Ludwigfr | 3:37345c109dfc | 734 | *forceRepulsionComputedY+=temp1*temp2; |
Ludwigfr | 0:9f7ee7ed13e4 | 735 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 736 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 737 | |
Ludwigfr | 0:9f7ee7ed13e4 | 738 | //return 1 if positiv, -1 if negativ |
Ludwigfr | 0:9f7ee7ed13e4 | 739 | float MiniExplorerCoimbra::sign1(float value){ |
Ludwigfr | 0:9f7ee7ed13e4 | 740 | if(value>=0) |
Ludwigfr | 0:9f7ee7ed13e4 | 741 | return 1; |
Ludwigfr | 0:9f7ee7ed13e4 | 742 | else |
Ludwigfr | 0:9f7ee7ed13e4 | 743 | return -1; |
Ludwigfr | 0:9f7ee7ed13e4 | 744 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 745 | |
Ludwigfr | 0:9f7ee7ed13e4 | 746 | //return 1 if positiv, 0 if negativ |
Ludwigfr | 0:9f7ee7ed13e4 | 747 | int MiniExplorerCoimbra::sign2(float value){ |
Ludwigfr | 0:9f7ee7ed13e4 | 748 | if(value>=0) |
Ludwigfr | 0:9f7ee7ed13e4 | 749 | return 1; |
Ludwigfr | 0:9f7ee7ed13e4 | 750 | else |
Ludwigfr | 0:9f7ee7ed13e4 | 751 | return 0; |
Ludwigfr | 0:9f7ee7ed13e4 | 752 | } |
Ludwigfr | 0:9f7ee7ed13e4 | 753 | |
geotsam | 1:20f48907c726 | 754 | float MiniExplorerCoimbra::distFromLine(float robot_x, float robot_y, float line_a, float line_b, float line_c){ |
geotsam | 1:20f48907c726 | 755 | return abs((line_a*robot_x+line_b*robot_y+line_c)/sqrt(line_a*line_a+line_b*line_b)); |
geotsam | 1:20f48907c726 | 756 | } |
geotsam | 1:20f48907c726 | 757 | |
Ludwigfr | 5:19f24c363418 | 758 | //4th LAB |
Ludwigfr | 5:19f24c363418 | 759 | //starting position lower left |
Ludwigfr | 5:19f24c363418 | 760 | |
Ludwigfr | 5:19f24c363418 | 761 | void MiniExplorerCoimbra::test_procedure_lab_4(float sizeX, float sizeY, int nbRectangle){ |
Ludwigfr | 5:19f24c363418 | 762 | |
Ludwigfr | 5:19f24c363418 | 763 | this->map.fill_map_with_kalman_knowledge(); |
Ludwigfr | 5:19f24c363418 | 764 | |
Ludwigfr | 5:19f24c363418 | 765 | this->go_to_point_with_angle_kalman(this->xWorld+sizeX,this->yWorld,this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 766 | |
Ludwigfr | 5:19f24c363418 | 767 | /* |
Ludwigfr | 5:19f24c363418 | 768 | for(int j=0;j<nbRectangle;j++){ |
Ludwigfr | 5:19f24c363418 | 769 | //right |
Ludwigfr | 5:19f24c363418 | 770 | this->go_to_point_with_angle_kalman(this->xWorld+sizeX,this->yWorld,this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 771 | this->go_turn_kalman(this->xWorld,this->yWorld,this->thetaWorld+PI/2); |
Ludwigfr | 5:19f24c363418 | 772 | |
Ludwigfr | 5:19f24c363418 | 773 | this->print_map_with_robot_position(); |
Ludwigfr | 5:19f24c363418 | 774 | pc.printf("\n\rX= %f",this->xWorld); |
Ludwigfr | 5:19f24c363418 | 775 | pc.printf("\n\rY= %f",this->yWorld); |
Ludwigfr | 5:19f24c363418 | 776 | pc.printf("\n\rtheta= %f",this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 777 | |
Ludwigfr | 5:19f24c363418 | 778 | //up |
Ludwigfr | 5:19f24c363418 | 779 | this->go_to_point_with_angle_kalman(this->xWorld+sizeX,this->yWorld+sizeY,this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 780 | this->go_turn_kalman(this->xWorld,this->yWorld,this->thetaWorld+PI/2); |
Ludwigfr | 5:19f24c363418 | 781 | |
Ludwigfr | 5:19f24c363418 | 782 | this->print_map_with_robot_position(); |
Ludwigfr | 5:19f24c363418 | 783 | pc.printf("\n\rX= %f",this->xWorld); |
Ludwigfr | 5:19f24c363418 | 784 | pc.printf("\n\rY= %f",this->yWorld); |
Ludwigfr | 5:19f24c363418 | 785 | pc.printf("\n\rtheta= %f",this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 786 | //left |
Ludwigfr | 5:19f24c363418 | 787 | this->go_to_point_with_angle_kalman(this->xWorld-sizeX,this->yWorld,this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 788 | this->go_turn_kalman(this->xWorld,this->yWorld,this->thetaWorld+PI/2); |
Ludwigfr | 5:19f24c363418 | 789 | |
Ludwigfr | 5:19f24c363418 | 790 | this->print_map_with_robot_position(); |
Ludwigfr | 5:19f24c363418 | 791 | pc.printf("\n\rX= %f",this->xWorld); |
Ludwigfr | 5:19f24c363418 | 792 | pc.printf("\n\rY= %f",this->yWorld); |
Ludwigfr | 5:19f24c363418 | 793 | pc.printf("\n\rtheta= %f",this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 794 | //down |
Ludwigfr | 5:19f24c363418 | 795 | this->go_to_point_with_angle_kalman(this->xWorld,this->yWorld-sizeY,this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 796 | this->go_turn_kalman(this->xWorld,this->yWorld,this->thetaWorld+PI/2); |
Ludwigfr | 5:19f24c363418 | 797 | |
Ludwigfr | 5:19f24c363418 | 798 | this->print_map_with_robot_position(); |
Ludwigfr | 5:19f24c363418 | 799 | pc.printf("\n\rX= %f",this->xWorld); |
Ludwigfr | 5:19f24c363418 | 800 | pc.printf("\n\rY= %f",this->yWorld); |
Ludwigfr | 5:19f24c363418 | 801 | pc.printf("\n\rtheta= %f",this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 802 | |
Ludwigfr | 5:19f24c363418 | 803 | } |
Ludwigfr | 5:19f24c363418 | 804 | */ |
Ludwigfr | 5:19f24c363418 | 805 | } |
Ludwigfr | 5:19f24c363418 | 806 | |
Ludwigfr | 5:19f24c363418 | 807 | //move of targetXWorld and targetYWorld ending in a targetAngleWorld |
Ludwigfr | 5:19f24c363418 | 808 | void MiniExplorerCoimbra::go_turn_kalman(float targetXWorld, float targetYWorld, float targetAngleWorld) { |
Ludwigfr | 5:19f24c363418 | 809 | //make sure the target is correct |
Ludwigfr | 5:19f24c363418 | 810 | if(targetAngleWorld > PI) |
Ludwigfr | 5:19f24c363418 | 811 | targetAngleWorld=-2*PI+targetAngleWorld; |
Ludwigfr | 5:19f24c363418 | 812 | if(targetAngleWorld < -PI) |
Ludwigfr | 5:19f24c363418 | 813 | targetAngleWorld=2*PI+targetAngleWorld; |
Ludwigfr | 5:19f24c363418 | 814 | |
Ludwigfr | 5:19f24c363418 | 815 | float distanceToTarget=100; |
Ludwigfr | 5:19f24c363418 | 816 | do { |
Ludwigfr | 5:19f24c363418 | 817 | leftMotor(1,50); |
Ludwigfr | 5:19f24c363418 | 818 | rightMotor(0,50); |
Ludwigfr | 5:19f24c363418 | 819 | this->OdometriaKalmanFilter(1,1); |
Ludwigfr | 5:19f24c363418 | 820 | |
Ludwigfr | 5:19f24c363418 | 821 | float distanceToTarget=this->dist(this->xWorld, this->yWorld, targetXWorld, targetYWorld); |
Ludwigfr | 5:19f24c363418 | 822 | //pc.printf("\n\rdist to target= %f",distanceToTarget); |
Ludwigfr | 5:19f24c363418 | 823 | |
Ludwigfr | 5:19f24c363418 | 824 | } while(distanceToTarget>1 || (abs(targetAngleWorld-this->thetaWorld)>0.1)); |
Ludwigfr | 5:19f24c363418 | 825 | |
Ludwigfr | 5:19f24c363418 | 826 | //Stop at the end |
Ludwigfr | 5:19f24c363418 | 827 | leftMotor(1,0); |
Ludwigfr | 5:19f24c363418 | 828 | rightMotor(1,0); |
Ludwigfr | 5:19f24c363418 | 829 | pc.printf("\r\nReached Target!"); |
Ludwigfr | 5:19f24c363418 | 830 | } |
Ludwigfr | 5:19f24c363418 | 831 | |
Ludwigfr | 5:19f24c363418 | 832 | //move of targetXWorld and targetYWorld ending in a targetAngleWorld |
Ludwigfr | 5:19f24c363418 | 833 | void MiniExplorerCoimbra::go_straight_kalman(float targetXWorld, float targetYWorld, float targetAngleWorld) { |
Ludwigfr | 5:19f24c363418 | 834 | //make sure the target is correct |
Ludwigfr | 5:19f24c363418 | 835 | if(targetAngleWorld > PI) |
Ludwigfr | 5:19f24c363418 | 836 | targetAngleWorld=-2*PI+targetAngleWorld; |
Ludwigfr | 5:19f24c363418 | 837 | if(targetAngleWorld < -PI) |
Ludwigfr | 5:19f24c363418 | 838 | targetAngleWorld=2*PI+targetAngleWorld; |
Ludwigfr | 5:19f24c363418 | 839 | |
Ludwigfr | 5:19f24c363418 | 840 | float distanceToTarget=100;; |
Ludwigfr | 5:19f24c363418 | 841 | |
Ludwigfr | 5:19f24c363418 | 842 | do { |
Ludwigfr | 5:19f24c363418 | 843 | leftMotor(1,400); |
Ludwigfr | 5:19f24c363418 | 844 | rightMotor(1,400); |
Ludwigfr | 5:19f24c363418 | 845 | this->OdometriaKalmanFilter(1,1); |
Ludwigfr | 5:19f24c363418 | 846 | |
Ludwigfr | 5:19f24c363418 | 847 | float distanceToTarget=this->dist(this->xWorld, this->yWorld, targetXWorld, targetYWorld); |
Ludwigfr | 5:19f24c363418 | 848 | pc.printf("\n\rdist to target= %f",distanceToTarget); |
Ludwigfr | 5:19f24c363418 | 849 | |
Ludwigfr | 5:19f24c363418 | 850 | } while(distanceToTarget>1 || (abs(targetAngleWorld-this->thetaWorld)>0.1)); |
Ludwigfr | 5:19f24c363418 | 851 | |
Ludwigfr | 5:19f24c363418 | 852 | //Stop at the end |
Ludwigfr | 5:19f24c363418 | 853 | leftMotor(1,0); |
Ludwigfr | 5:19f24c363418 | 854 | rightMotor(1,0); |
Ludwigfr | 5:19f24c363418 | 855 | pc.printf("\r\nReached Target!"); |
Ludwigfr | 5:19f24c363418 | 856 | } |
Ludwigfr | 5:19f24c363418 | 857 | |
Ludwigfr | 5:19f24c363418 | 858 | //move of targetXWorld and targetYWorld ending in a targetAngleWorld |
Ludwigfr | 5:19f24c363418 | 859 | void MiniExplorerCoimbra::go_to_point_with_angle_kalman(float targetXWorld, float targetYWorld, float targetAngleWorld) { |
Ludwigfr | 5:19f24c363418 | 860 | float dt; |
Ludwigfr | 5:19f24c363418 | 861 | Timer t; |
Ludwigfr | 5:19f24c363418 | 862 | float distanceToTarget; |
Ludwigfr | 5:19f24c363418 | 863 | //make sure the target is correct |
Ludwigfr | 5:19f24c363418 | 864 | if(targetAngleWorld > PI) |
Ludwigfr | 5:19f24c363418 | 865 | targetAngleWorld=-2*PI+targetAngleWorld; |
Ludwigfr | 5:19f24c363418 | 866 | if(targetAngleWorld < -PI) |
Ludwigfr | 5:19f24c363418 | 867 | targetAngleWorld=2*PI+targetAngleWorld; |
Ludwigfr | 5:19f24c363418 | 868 | |
Ludwigfr | 5:19f24c363418 | 869 | do { |
Ludwigfr | 5:19f24c363418 | 870 | //Timer stuff |
Ludwigfr | 5:19f24c363418 | 871 | dt = t.read(); |
Ludwigfr | 5:19f24c363418 | 872 | t.reset(); |
Ludwigfr | 5:19f24c363418 | 873 | t.start(); |
Ludwigfr | 5:19f24c363418 | 874 | |
Ludwigfr | 5:19f24c363418 | 875 | //Updating X,Y and theta with the odometry values |
Ludwigfr | 5:19f24c363418 | 876 | this->OdometriaKalmanFilter(1,1); |
Ludwigfr | 5:19f24c363418 | 877 | |
Ludwigfr | 5:19f24c363418 | 878 | //Updating motor velocities |
Ludwigfr | 5:19f24c363418 | 879 | distanceToTarget=this->update_angular_speed_wheels_go_to_point_with_angle(targetXWorld,targetYWorld,targetAngleWorld,dt); |
Ludwigfr | 5:19f24c363418 | 880 | |
Ludwigfr | 5:19f24c363418 | 881 | //wait(0.2); |
Ludwigfr | 5:19f24c363418 | 882 | //Timer stuff |
Ludwigfr | 5:19f24c363418 | 883 | t.stop(); |
Ludwigfr | 5:19f24c363418 | 884 | pc.printf("\n\rdist to target= %f",distanceToTarget); |
Ludwigfr | 5:19f24c363418 | 885 | |
Ludwigfr | 5:19f24c363418 | 886 | } while(distanceToTarget>1 || (abs(targetAngleWorld-this->thetaWorld)>0.1)); |
Ludwigfr | 5:19f24c363418 | 887 | |
Ludwigfr | 5:19f24c363418 | 888 | //Stop at the end |
Ludwigfr | 5:19f24c363418 | 889 | leftMotor(1,0); |
Ludwigfr | 5:19f24c363418 | 890 | rightMotor(1,0); |
Ludwigfr | 5:19f24c363418 | 891 | pc.printf("\r\nReached Target!"); |
Ludwigfr | 5:19f24c363418 | 892 | } |
Ludwigfr | 5:19f24c363418 | 893 | |
Ludwigfr | 5:19f24c363418 | 894 | void MiniExplorerCoimbra::OdometriaKalmanFilter(float encoderRightFailureRate,float encoderLeftFailureRate){ |
Ludwigfr | 5:19f24c363418 | 895 | //=====KINEMATICS=========================== |
Ludwigfr | 5:19f24c363418 | 896 | float R_cm; |
Ludwigfr | 5:19f24c363418 | 897 | float L_cm; |
Ludwigfr | 5:19f24c363418 | 898 | |
Ludwigfr | 5:19f24c363418 | 899 | //fill R_cm and L_cm with how much is wheel has moved (custom robot.h) |
Ludwigfr | 5:19f24c363418 | 900 | OdometriaKalman(&R_cm, &L_cm); |
Ludwigfr | 5:19f24c363418 | 901 | |
Ludwigfr | 5:19f24c363418 | 902 | encoderRightFailureRate=0.95; |
Ludwigfr | 5:19f24c363418 | 903 | encoderLeftFailureRate=1; |
Ludwigfr | 5:19f24c363418 | 904 | |
Ludwigfr | 5:19f24c363418 | 905 | R_cm=R_cm*encoderRightFailureRate; |
Ludwigfr | 5:19f24c363418 | 906 | L_cm=L_cm*encoderLeftFailureRate; |
Ludwigfr | 5:19f24c363418 | 907 | |
Ludwigfr | 5:19f24c363418 | 908 | float distanceMoved=(R_cm+L_cm)/2; |
Ludwigfr | 5:19f24c363418 | 909 | float angleMoved=(R_cm-L_cm)/this->distanceWheels; |
Ludwigfr | 5:19f24c363418 | 910 | |
Ludwigfr | 5:19f24c363418 | 911 | float distanceMovedX=distanceMoved*cos(this->thetaWorld+angleMoved/2); |
Ludwigfr | 5:19f24c363418 | 912 | float distanceMovedY=distanceMoved*sin(this->thetaWorld+angleMoved/2); |
Ludwigfr | 5:19f24c363418 | 913 | |
Ludwigfr | 5:19f24c363418 | 914 | //try with world coordinate system |
Ludwigfr | 5:19f24c363418 | 915 | |
Ludwigfr | 5:19f24c363418 | 916 | float xEstimatedK=this->xWorld+distanceMovedX; |
Ludwigfr | 5:19f24c363418 | 917 | float yEstimatedK=this->yWorld+distanceMovedY; |
Ludwigfr | 5:19f24c363418 | 918 | float thetaWorldEstimatedK = this->thetaWorld+angleMoved; |
Ludwigfr | 5:19f24c363418 | 919 | |
Ludwigfr | 5:19f24c363418 | 920 | //try with robot coordinate system |
Ludwigfr | 5:19f24c363418 | 921 | /* |
Ludwigfr | 5:19f24c363418 | 922 | float xEstimatedK=X; |
Ludwigfr | 5:19f24c363418 | 923 | float yEstimatedK=Y; |
Ludwigfr | 5:19f24c363418 | 924 | float thetaWorldEstimatedK = theta; |
Ludwigfr | 5:19f24c363418 | 925 | */ |
Ludwigfr | 5:19f24c363418 | 926 | //=====ERROR_MODEL=========================== |
Ludwigfr | 5:19f24c363418 | 927 | |
Ludwigfr | 5:19f24c363418 | 928 | //FP Matrix |
Ludwigfr | 5:19f24c363418 | 929 | float Fp[3][3]={{1,0,0},{0,1,0},{0,0,1}}; |
Ludwigfr | 5:19f24c363418 | 930 | |
Ludwigfr | 5:19f24c363418 | 931 | Fp[0][2]=-1*distanceMoved*sin(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 5:19f24c363418 | 932 | Fp[1][2]=distanceMoved*cos(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 5:19f24c363418 | 933 | |
Ludwigfr | 5:19f24c363418 | 934 | //Frl matrix |
Ludwigfr | 5:19f24c363418 | 935 | float Frl[3][2]={{0,0},{0,0},{(1/this->distanceWheels),-(1/this->distanceWheels)}}; |
Ludwigfr | 5:19f24c363418 | 936 | |
Ludwigfr | 5:19f24c363418 | 937 | Frl[0][0]=0.5*cos(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 5:19f24c363418 | 938 | Frl[0][1]=0.5*cos(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 5:19f24c363418 | 939 | Frl[1][0]=0.5*sin(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 5:19f24c363418 | 940 | Frl[1][1]=0.5*sin(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2)); |
Ludwigfr | 5:19f24c363418 | 941 | |
Ludwigfr | 5:19f24c363418 | 942 | //error constants... |
Ludwigfr | 5:19f24c363418 | 943 | float kr=1; |
Ludwigfr | 5:19f24c363418 | 944 | float kl=1; |
Ludwigfr | 5:19f24c363418 | 945 | float covar[2][2]={{kr*abs(R_cm), 0}, {0, kl*abs(L_cm)}}; |
Ludwigfr | 5:19f24c363418 | 946 | |
Ludwigfr | 5:19f24c363418 | 947 | //uncertainty positionx, and theta at |
Ludwigfr | 5:19f24c363418 | 948 | //1,1,1 |
Ludwigfr | 5:19f24c363418 | 949 | float Q[3][3]={{1,0,0}, {0,2,0}, {0,0,0.01}}; |
Ludwigfr | 5:19f24c363418 | 950 | |
Ludwigfr | 5:19f24c363418 | 951 | 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 | 952 | 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 | 953 | 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 | 954 | 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 | 955 | 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 | 956 | 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 | 957 | 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 | 958 | 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 | 959 | 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 | 960 | |
Ludwigfr | 5:19f24c363418 | 961 | //=====OBSERVATION===== |
Ludwigfr | 5:19f24c363418 | 962 | //get the estimated measure we should have according to our knowledge of the map and the previously computed localisation |
Ludwigfr | 5:19f24c363418 | 963 | |
Ludwigfr | 5:19f24c363418 | 964 | float leftCmEstimated=this->sonarLeft.maxRange; |
Ludwigfr | 5:19f24c363418 | 965 | float frontCmEstimated=this->sonarFront.maxRange; |
Ludwigfr | 5:19f24c363418 | 966 | float rightCmEstimated=this->sonarRight.maxRange; |
Ludwigfr | 5:19f24c363418 | 967 | float xWorldCell; |
Ludwigfr | 5:19f24c363418 | 968 | float yWorldCell; |
Ludwigfr | 5:19f24c363418 | 969 | float currDistance; |
Ludwigfr | 5:19f24c363418 | 970 | float xClosestCellLeft; |
Ludwigfr | 5:19f24c363418 | 971 | float yClosestCellLeft; |
Ludwigfr | 5:19f24c363418 | 972 | float xClosestCellFront; |
Ludwigfr | 5:19f24c363418 | 973 | float yClosestCellFront; |
Ludwigfr | 5:19f24c363418 | 974 | float xClosestCellRight; |
Ludwigfr | 5:19f24c363418 | 975 | float yClosestCellRight; |
Ludwigfr | 5:19f24c363418 | 976 | //get theorical distance to sonar |
Ludwigfr | 5:19f24c363418 | 977 | for(int i=0;i<this->map.nbCellWidth;i++){ |
Ludwigfr | 5:19f24c363418 | 978 | for(int j=0;j<this->map.nbCellHeight;j++){ |
Ludwigfr | 5:19f24c363418 | 979 | //check if occupied, if not discard |
Ludwigfr | 5:19f24c363418 | 980 | if(this->map.get_proba_cell(i,j)<0.5){ |
Ludwigfr | 5:19f24c363418 | 981 | //check if in sonar range |
Ludwigfr | 5:19f24c363418 | 982 | xWorldCell=this->map.cell_width_coordinate_to_world(i); |
Ludwigfr | 5:19f24c363418 | 983 | yWorldCell=this->map.cell_height_coordinate_to_world(j); |
Ludwigfr | 5:19f24c363418 | 984 | //check left |
Ludwigfr | 5:19f24c363418 | 985 | currDistance=this->sonarLeft.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK); |
Ludwigfr | 5:19f24c363418 | 986 | if((currDistance < this->sonarLeft.maxRange) && currDistance!=-1){ |
Ludwigfr | 5:19f24c363418 | 987 | //check if distance is lower than previous, update lowest if so |
Ludwigfr | 5:19f24c363418 | 988 | if(currDistance < leftCmEstimated){ |
Ludwigfr | 5:19f24c363418 | 989 | leftCmEstimated= currDistance; |
Ludwigfr | 5:19f24c363418 | 990 | xClosestCellLeft=xWorldCell; |
Ludwigfr | 5:19f24c363418 | 991 | yClosestCellLeft=yWorldCell; |
Ludwigfr | 5:19f24c363418 | 992 | } |
Ludwigfr | 5:19f24c363418 | 993 | } |
Ludwigfr | 5:19f24c363418 | 994 | //check front |
Ludwigfr | 5:19f24c363418 | 995 | currDistance=this->sonarFront.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK); |
Ludwigfr | 5:19f24c363418 | 996 | if((currDistance < this->sonarFront.maxRange) && currDistance!=-1){ |
Ludwigfr | 5:19f24c363418 | 997 | //check if distance is lower than previous, update lowest if so |
Ludwigfr | 5:19f24c363418 | 998 | if(currDistance < frontCmEstimated){ |
Ludwigfr | 5:19f24c363418 | 999 | frontCmEstimated= currDistance; |
Ludwigfr | 5:19f24c363418 | 1000 | xClosestCellFront=xWorldCell; |
Ludwigfr | 5:19f24c363418 | 1001 | yClosestCellFront=yWorldCell; |
Ludwigfr | 5:19f24c363418 | 1002 | } |
Ludwigfr | 5:19f24c363418 | 1003 | } |
Ludwigfr | 5:19f24c363418 | 1004 | //check right |
Ludwigfr | 5:19f24c363418 | 1005 | currDistance=this->sonarRight.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK); |
Ludwigfr | 5:19f24c363418 | 1006 | if((currDistance < this->sonarRight.maxRange) && currDistance!=-1){ |
Ludwigfr | 5:19f24c363418 | 1007 | //check if distance is lower than previous, update lowest if so |
Ludwigfr | 5:19f24c363418 | 1008 | if(currDistance < rightCmEstimated){ |
Ludwigfr | 5:19f24c363418 | 1009 | rightCmEstimated= currDistance; |
Ludwigfr | 5:19f24c363418 | 1010 | xClosestCellRight=xWorldCell; |
Ludwigfr | 5:19f24c363418 | 1011 | yClosestCellRight=yWorldCell; |
Ludwigfr | 5:19f24c363418 | 1012 | } |
Ludwigfr | 5:19f24c363418 | 1013 | } |
Ludwigfr | 5:19f24c363418 | 1014 | } |
Ludwigfr | 5:19f24c363418 | 1015 | } |
Ludwigfr | 5:19f24c363418 | 1016 | } |
Ludwigfr | 5:19f24c363418 | 1017 | |
Ludwigfr | 5:19f24c363418 | 1018 | //check measurements from sonars, see if they passed the validation gate |
Ludwigfr | 5:19f24c363418 | 1019 | float leftCm = get_distance_left_sensor()/10; |
Ludwigfr | 5:19f24c363418 | 1020 | float frontCm = get_distance_front_sensor()/10; |
Ludwigfr | 5:19f24c363418 | 1021 | float rightCm = get_distance_right_sensor()/10; |
Ludwigfr | 5:19f24c363418 | 1022 | //if superior to sonar range, put the value to sonar max range + 1 |
Ludwigfr | 5:19f24c363418 | 1023 | if(leftCm > this->sonarLeft.maxRange) |
Ludwigfr | 5:19f24c363418 | 1024 | leftCm=this->sonarLeft.maxRange; |
Ludwigfr | 5:19f24c363418 | 1025 | if(frontCm > this->sonarFront.maxRange) |
Ludwigfr | 5:19f24c363418 | 1026 | frontCm=this->sonarFront.maxRange; |
Ludwigfr | 5:19f24c363418 | 1027 | if(rightCm > this->sonarRight.maxRange) |
Ludwigfr | 5:19f24c363418 | 1028 | rightCm=this->sonarRight.maxRange; |
Ludwigfr | 5:19f24c363418 | 1029 | |
Ludwigfr | 5:19f24c363418 | 1030 | //======INNOVATION======== |
Ludwigfr | 5:19f24c363418 | 1031 | //get the innoncation: the value of the difference between the actual measure and what we anticipated |
Ludwigfr | 5:19f24c363418 | 1032 | float innovationLeft=leftCm-leftCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1033 | float innovationFront=frontCm-frontCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1034 | float innovationRight=-rightCm-rightCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1035 | //compute jacobian of observation |
Ludwigfr | 5:19f24c363418 | 1036 | float jacobianOfObservationLeft[1][3]; |
Ludwigfr | 5:19f24c363418 | 1037 | float jacobianOfObservationRight[1][3]; |
Ludwigfr | 5:19f24c363418 | 1038 | float jacobianOfObservationFront[1][3]; |
Ludwigfr | 5:19f24c363418 | 1039 | float xSonarLeft=xEstimatedK+this->sonarLeft.distanceX; |
Ludwigfr | 5:19f24c363418 | 1040 | float ySonarLeft=yEstimatedK+this->sonarLeft.distanceY; |
Ludwigfr | 5:19f24c363418 | 1041 | //left |
Ludwigfr | 5:19f24c363418 | 1042 | jacobianOfObservationLeft[0][0]=(xSonarLeft-xClosestCellLeft)/leftCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1043 | jacobianOfObservationLeft[0][1]=(ySonarLeft-yClosestCellLeft)/leftCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1044 | jacobianOfObservationLeft[0][2]=(xClosestCellLeft-xSonarLeft)*(xSonarLeft*sin(thetaWorldEstimatedK)+ySonarLeft*cos(thetaWorldEstimatedK))+(yClosestCellLeft-ySonarLeft)*(-xSonarLeft*cos(thetaWorldEstimatedK)+ySonarLeft*sin(thetaWorldEstimatedK)); |
Ludwigfr | 5:19f24c363418 | 1045 | //front |
Ludwigfr | 5:19f24c363418 | 1046 | float xSonarFront=xEstimatedK+this->sonarFront.distanceX; |
Ludwigfr | 5:19f24c363418 | 1047 | float ySonarFront=yEstimatedK+this->sonarFront.distanceY; |
Ludwigfr | 5:19f24c363418 | 1048 | jacobianOfObservationFront[0][0]=(xSonarFront-xClosestCellFront)/frontCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1049 | jacobianOfObservationFront[0][1]=(ySonarFront-yClosestCellFront)/frontCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1050 | jacobianOfObservationFront[0][2]=(xClosestCellFront-xSonarFront)*(xSonarFront*sin(thetaWorldEstimatedK)+ySonarFront*cos(thetaWorldEstimatedK))+(yClosestCellFront-ySonarFront)*(-xSonarFront*cos(thetaWorldEstimatedK)+ySonarFront*sin(thetaWorldEstimatedK)); |
Ludwigfr | 5:19f24c363418 | 1051 | //right |
Ludwigfr | 5:19f24c363418 | 1052 | float xSonarRight=xEstimatedK+this->sonarRight.distanceX; |
Ludwigfr | 5:19f24c363418 | 1053 | float ySonarRight=yEstimatedK+this->sonarRight.distanceY; |
Ludwigfr | 5:19f24c363418 | 1054 | jacobianOfObservationRight[0][0]=(xSonarRight-xClosestCellRight)/rightCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1055 | jacobianOfObservationRight[0][1]=(ySonarRight-yClosestCellRight)/rightCmEstimated; |
Ludwigfr | 5:19f24c363418 | 1056 | jacobianOfObservationRight[0][2]=(xClosestCellRight-xSonarRight)*(xSonarRight*sin(thetaWorldEstimatedK)+ySonarRight*cos(thetaWorldEstimatedK))+(yClosestCellRight-ySonarRight)*(-xSonarRight*cos(thetaWorldEstimatedK)+ySonarRight*sin(thetaWorldEstimatedK)); |
Ludwigfr | 5:19f24c363418 | 1057 | |
Ludwigfr | 5:19f24c363418 | 1058 | //error constants 0,0,0 sonars perfect; must be found by experimenting; gives mean and standanrt deviation |
Ludwigfr | 5:19f24c363418 | 1059 | //let's assume |
Ludwigfr | 5:19f24c363418 | 1060 | //in centimeters |
Ludwigfr | 5:19f24c363418 | 1061 | float R_front=4; |
Ludwigfr | 5:19f24c363418 | 1062 | float R_left=4; |
Ludwigfr | 5:19f24c363418 | 1063 | float R_right=4; |
Ludwigfr | 5:19f24c363418 | 1064 | |
Ludwigfr | 5:19f24c363418 | 1065 | //R-> 4 in diagonal |
Ludwigfr | 5:19f24c363418 | 1066 | |
Ludwigfr | 5:19f24c363418 | 1067 | //S for each sonar (concatenated covariance matrix of innovation) |
Ludwigfr | 5:19f24c363418 | 1068 | 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 | 1069 | 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 | 1070 | 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 | 1071 | |
Ludwigfr | 5:19f24c363418 | 1072 | //check if it pass the validation gate |
Ludwigfr | 5:19f24c363418 | 1073 | float gateScoreLeft=innovationLeft*innovationLeft/innovationCovarianceLeft; |
Ludwigfr | 5:19f24c363418 | 1074 | float gateScoreFront=innovationFront*innovationFront/innovationCovarianceFront; |
Ludwigfr | 5:19f24c363418 | 1075 | float gateScoreRight=innovationRight*innovationRight/innovationCovarianceRight; |
Ludwigfr | 5:19f24c363418 | 1076 | int leftPassed=0; |
Ludwigfr | 5:19f24c363418 | 1077 | int frontPassed=0; |
Ludwigfr | 5:19f24c363418 | 1078 | int rightPassed=0; |
Ludwigfr | 5:19f24c363418 | 1079 | |
Ludwigfr | 5:19f24c363418 | 1080 | //5cm -> 25 |
Ludwigfr | 5:19f24c363418 | 1081 | int errorsquare=25;//constant error |
Ludwigfr | 5:19f24c363418 | 1082 | |
Ludwigfr | 5:19f24c363418 | 1083 | if(gateScoreLeft <= errorsquare) |
Ludwigfr | 5:19f24c363418 | 1084 | leftPassed=1; |
Ludwigfr | 5:19f24c363418 | 1085 | if(gateScoreFront <= errorsquare) |
Ludwigfr | 5:19f24c363418 | 1086 | frontPassed=10; |
Ludwigfr | 5:19f24c363418 | 1087 | if(gateScoreRight <= errorsquare) |
Ludwigfr | 5:19f24c363418 | 1088 | rightPassed=100; |
Ludwigfr | 5:19f24c363418 | 1089 | //for those who passed |
Ludwigfr | 5:19f24c363418 | 1090 | //compute composite innovation |
Ludwigfr | 5:19f24c363418 | 1091 | int nbPassed=leftPassed+frontPassed+rightPassed; |
Ludwigfr | 5:19f24c363418 | 1092 | |
Ludwigfr | 5:19f24c363418 | 1093 | float xEstimatedKNext=xEstimatedK; |
Ludwigfr | 5:19f24c363418 | 1094 | float yEstimatedKNext=xEstimatedK; |
Ludwigfr | 5:19f24c363418 | 1095 | float thetaWorldEstimatedKNext=thetaWorldEstimatedK; |
Ludwigfr | 5:19f24c363418 | 1096 | |
Ludwigfr | 5:19f24c363418 | 1097 | float compositeInnovationCovariance3x3[3][3]={{0,0,0}, {0,0,0}, {0,0,0}}; |
Ludwigfr | 5:19f24c363418 | 1098 | |
Ludwigfr | 5:19f24c363418 | 1099 | float compositeInnovationCovariance2x2[2][2]={{0,0}, {0,0}}; |
Ludwigfr | 5:19f24c363418 | 1100 | |
Ludwigfr | 5:19f24c363418 | 1101 | float compositeInnovationCovariance1x1=0; |
Ludwigfr | 5:19f24c363418 | 1102 | |
Ludwigfr | 5:19f24c363418 | 1103 | float kalmanGain3X1[3][1]={{0}, {0}, {0}}; |
Ludwigfr | 5:19f24c363418 | 1104 | float kalmanGain3X2[3][2]={{0,0}, {0.0}, {0,0}}; |
Ludwigfr | 5:19f24c363418 | 1105 | float kalmanGain3X3[3][3]={{0,0,0}, {0,0,0}, {0,0,0}}; |
Ludwigfr | 5:19f24c363418 | 1106 | |
Ludwigfr | 5:19f24c363418 | 1107 | //we do not use the composite measurement jacobian (16), we directly use the values from the measurement jacobian (jacobianOfObservation) |
Ludwigfr | 5:19f24c363418 | 1108 | |
Ludwigfr | 5:19f24c363418 | 1109 | switch(nbPassed){ |
Ludwigfr | 5:19f24c363418 | 1110 | case 0://none |
Ludwigfr | 5:19f24c363418 | 1111 | //nothings happens it's okay |
Ludwigfr | 5:19f24c363418 | 1112 | break; |
Ludwigfr | 5:19f24c363418 | 1113 | case 1://left |
Ludwigfr | 5:19f24c363418 | 1114 | 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 | 1115 | |
Ludwigfr | 5:19f24c363418 | 1116 | 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 | 1117 | 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 | 1118 | 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 | 1119 | |
Ludwigfr | 5:19f24c363418 | 1120 | xEstimatedKNext+= kalmanGain3X1[0][0]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1121 | yEstimatedKNext+= kalmanGain3X1[1][0]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1122 | thetaWorldEstimatedKNext+= kalmanGain3X1[2][0]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1123 | |
Ludwigfr | 5:19f24c363418 | 1124 | covariancePositionEstimationK[0][0]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[0][0],2) + covariancePositionEstimationK[0][0]; |
Ludwigfr | 5:19f24c363418 | 1125 | covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1126 | covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1127 | covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1128 | covariancePositionEstimationK[1][1]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[1][0],2) + covariancePositionEstimationK[1][1]; |
Ludwigfr | 5:19f24c363418 | 1129 | covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1130 | covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1131 | covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1132 | covariancePositionEstimationK[2][2]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[2][0],2) + covariancePositionEstimationK[2][2]; |
Ludwigfr | 5:19f24c363418 | 1133 | |
Ludwigfr | 5:19f24c363418 | 1134 | break; |
Ludwigfr | 5:19f24c363418 | 1135 | case 10://front |
Ludwigfr | 5:19f24c363418 | 1136 | |
Ludwigfr | 5:19f24c363418 | 1137 | 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 | 1138 | |
Ludwigfr | 5:19f24c363418 | 1139 | 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 | 1140 | 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 | 1141 | 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 | 1142 | |
Ludwigfr | 5:19f24c363418 | 1143 | xEstimatedKNext+= kalmanGain3X1[0][0]*innovationFront; |
Ludwigfr | 5:19f24c363418 | 1144 | yEstimatedKNext+= kalmanGain3X1[1][0]*innovationFront; |
Ludwigfr | 5:19f24c363418 | 1145 | thetaWorldEstimatedKNext+= kalmanGain3X1[2][0]*innovationFront; |
Ludwigfr | 5:19f24c363418 | 1146 | |
Ludwigfr | 5:19f24c363418 | 1147 | covariancePositionEstimationK[0][0]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[0][0],2) + covariancePositionEstimationK[0][0]; |
Ludwigfr | 5:19f24c363418 | 1148 | covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1149 | covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1150 | covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1151 | covariancePositionEstimationK[1][1]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[1][0],2) + covariancePositionEstimationK[1][1]; |
Ludwigfr | 5:19f24c363418 | 1152 | covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1153 | covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1154 | covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1155 | covariancePositionEstimationK[2][2]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[2][0],2) + covariancePositionEstimationK[2][2]; |
Ludwigfr | 5:19f24c363418 | 1156 | |
Ludwigfr | 5:19f24c363418 | 1157 | break; |
Ludwigfr | 5:19f24c363418 | 1158 | case 11://left and front |
Ludwigfr | 5:19f24c363418 | 1159 | 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 | 1160 | 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 | 1161 | 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 | 1162 | 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 | 1163 | |
Ludwigfr | 5:19f24c363418 | 1164 | 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 | 1165 | 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 | 1166 | 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 | 1167 | 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 | 1168 | 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 | 1169 | 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 | 1170 | |
Ludwigfr | 5:19f24c363418 | 1171 | xEstimatedKNext+= kalmanGain3X2[0][0]*innovationFront + kalmanGain3X2[0][1]*innovationLeft; |
Ludwigfr | 5:19f24c363418 | 1172 | yEstimatedKNext+= kalmanGain3X2[1][0]*innovationFront + kalmanGain3X2[1][1]*innovationLeft; |
Ludwigfr | 5:19f24c363418 | 1173 | thetaWorldEstimatedKNext+= kalmanGain3X2[2][0]*innovationFront + kalmanGain3X2[2][1]*innovationLeft; |
Ludwigfr | 5:19f24c363418 | 1174 | |
Ludwigfr | 5:19f24c363418 | 1175 | 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 | 1176 | 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 | 1177 | 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 | 1178 | 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 | 1179 | 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 | 1180 | 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 | 1181 | 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 | 1182 | 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 | 1183 | 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 | 1184 | |
Ludwigfr | 5:19f24c363418 | 1185 | break; |
Ludwigfr | 5:19f24c363418 | 1186 | case 100://right |
Ludwigfr | 5:19f24c363418 | 1187 | |
Ludwigfr | 5:19f24c363418 | 1188 | 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 | 1189 | |
Ludwigfr | 5:19f24c363418 | 1190 | 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 | 1191 | 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 | 1192 | 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 | 1193 | |
Ludwigfr | 5:19f24c363418 | 1194 | xEstimatedKNext+= kalmanGain3X1[0][0]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1195 | yEstimatedKNext+= kalmanGain3X1[1][0]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1196 | thetaWorldEstimatedKNext+= kalmanGain3X1[2][0]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1197 | |
Ludwigfr | 5:19f24c363418 | 1198 | covariancePositionEstimationK[0][0]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[0][0],2) + covariancePositionEstimationK[0][0]; |
Ludwigfr | 5:19f24c363418 | 1199 | covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1200 | covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1201 | covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1202 | covariancePositionEstimationK[1][1]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[1][0],2) + covariancePositionEstimationK[1][1]; |
Ludwigfr | 5:19f24c363418 | 1203 | covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1204 | covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1205 | covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1; |
Ludwigfr | 5:19f24c363418 | 1206 | covariancePositionEstimationK[2][2]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[2][0],2) + covariancePositionEstimationK[2][2]; |
Ludwigfr | 5:19f24c363418 | 1207 | |
Ludwigfr | 5:19f24c363418 | 1208 | break; |
Ludwigfr | 5:19f24c363418 | 1209 | case 101://right and left |
Ludwigfr | 5:19f24c363418 | 1210 | 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 | 1211 | 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 | 1212 | 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 | 1213 | 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 | 1214 | |
Ludwigfr | 5:19f24c363418 | 1215 | 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 | 1216 | 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 | 1217 | 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 | 1218 | 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 | 1219 | 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 | 1220 | 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 | 1221 | |
Ludwigfr | 5:19f24c363418 | 1222 | xEstimatedKNext+= kalmanGain3X2[0][0]*innovationLeft + kalmanGain3X2[0][1]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1223 | yEstimatedKNext+= kalmanGain3X2[1][0]*innovationLeft + kalmanGain3X2[1][1]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1224 | thetaWorldEstimatedKNext+= kalmanGain3X2[2][0]*innovationLeft + kalmanGain3X2[2][1]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1225 | |
Ludwigfr | 5:19f24c363418 | 1226 | 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 | 1227 | 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 | 1228 | 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 | 1229 | 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 | 1230 | 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 | 1231 | 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 | 1232 | 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 | 1233 | 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 | 1234 | 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 | 1235 | |
Ludwigfr | 5:19f24c363418 | 1236 | break; |
Ludwigfr | 5:19f24c363418 | 1237 | case 110://right and front |
Ludwigfr | 5:19f24c363418 | 1238 | |
Ludwigfr | 5:19f24c363418 | 1239 | 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 | 1240 | 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 | 1241 | 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 | 1242 | 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 | 1243 | |
Ludwigfr | 5:19f24c363418 | 1244 | 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 | 1245 | 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 | 1246 | 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 | 1247 | 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 | 1248 | 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 | 1249 | 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 | 1250 | |
Ludwigfr | 5:19f24c363418 | 1251 | xEstimatedKNext+= kalmanGain3X2[0][0]*innovationFront + kalmanGain3X2[0][1]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1252 | yEstimatedKNext+= kalmanGain3X2[1][0]*innovationFront + kalmanGain3X2[1][1]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1253 | thetaWorldEstimatedKNext+= kalmanGain3X2[2][0]*innovationFront + kalmanGain3X2[2][1]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1254 | |
Ludwigfr | 5:19f24c363418 | 1255 | 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 | 1256 | 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 | 1257 | 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 | 1258 | 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 | 1259 | 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 | 1260 | 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 | 1261 | 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 | 1262 | 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 | 1263 | 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 | 1264 | |
Ludwigfr | 5:19f24c363418 | 1265 | break; |
Ludwigfr | 5:19f24c363418 | 1266 | case 111://right front and left |
Ludwigfr | 5:19f24c363418 | 1267 | //get the compositeInnovationCovariance3x3 |
Ludwigfr | 5:19f24c363418 | 1268 | 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 | 1269 | 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 | 1270 | 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 | 1271 | 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 | 1272 | 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 | 1273 | 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 | 1274 | 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 | 1275 | 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 | 1276 | 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 | 1277 | |
Ludwigfr | 5:19f24c363418 | 1278 | //compute the kalman gain |
Ludwigfr | 5:19f24c363418 | 1279 | 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 | 1280 | 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 | 1281 | 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 | 1282 | 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 | 1283 | 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 | 1284 | 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 | 1285 | 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 | 1286 | 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 | 1287 | 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 | 1288 | |
Ludwigfr | 5:19f24c363418 | 1289 | //update the prediction |
Ludwigfr | 5:19f24c363418 | 1290 | xEstimatedKNext+= kalmanGain3X3[0][0]*innovationFront + kalmanGain3X3[0][1]*innovationLeft + kalmanGain3X3[0][2]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1291 | yEstimatedKNext+= kalmanGain3X3[1][0]*innovationFront + kalmanGain3X3[1][1]*innovationLeft + kalmanGain3X3[1][2]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1292 | thetaWorldEstimatedKNext+= kalmanGain3X3[2][0]*innovationFront + kalmanGain3X3[2][1]*innovationLeft + kalmanGain3X3[2][2]*innovationRight; |
Ludwigfr | 5:19f24c363418 | 1293 | |
Ludwigfr | 5:19f24c363418 | 1294 | //update covariancePositionEstimationK |
Ludwigfr | 5:19f24c363418 | 1295 | 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 | 1296 | 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 | 1297 | 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 | 1298 | 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 | 1299 | 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 | 1300 | 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 | 1301 | 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 | 1302 | 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 | 1303 | 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 | 1304 | |
Ludwigfr | 5:19f24c363418 | 1305 | break; |
Ludwigfr | 5:19f24c363418 | 1306 | } |
Ludwigfr | 5:19f24c363418 | 1307 | //big question, in wich coordinate space are those measurements... |
Ludwigfr | 5:19f24c363418 | 1308 | //try with world coordinate system |
Ludwigfr | 5:19f24c363418 | 1309 | |
Ludwigfr | 5:19f24c363418 | 1310 | this->xWorld=xEstimatedKNext; |
Ludwigfr | 5:19f24c363418 | 1311 | this->yWorld=yEstimatedKNext; |
Ludwigfr | 5:19f24c363418 | 1312 | this->thetaWorld=thetaWorldEstimatedKNext; |
Ludwigfr | 5:19f24c363418 | 1313 | |
Ludwigfr | 5:19f24c363418 | 1314 | |
Ludwigfr | 5:19f24c363418 | 1315 | //try with robot one |
Ludwigfr | 5:19f24c363418 | 1316 | /* |
Ludwigfr | 5:19f24c363418 | 1317 | X=xEstimatedKNext; |
Ludwigfr | 5:19f24c363418 | 1318 | Y=yEstimatedKNext; |
Ludwigfr | 5:19f24c363418 | 1319 | theta=thetaWorldEstimatedKNext; |
Ludwigfr | 5:19f24c363418 | 1320 | this->xWorld=-Y; |
Ludwigfr | 5:19f24c363418 | 1321 | this->yWorld=X; |
Ludwigfr | 5:19f24c363418 | 1322 | if(theta >PI/2) |
Ludwigfr | 5:19f24c363418 | 1323 | this->thetaWorld=-PI+(theta-PI/2); |
Ludwigfr | 5:19f24c363418 | 1324 | else |
Ludwigfr | 5:19f24c363418 | 1325 | this->thetaWorld=theta+PI/2; |
Ludwigfr | 5:19f24c363418 | 1326 | |
Ludwigfr | 5:19f24c363418 | 1327 | |
Ludwigfr | 5:19f24c363418 | 1328 | this->print_map_with_robot_position(); |
Ludwigfr | 5:19f24c363418 | 1329 | pc.printf("\n\rX= %f",this->xWorld); |
Ludwigfr | 5:19f24c363418 | 1330 | pc.printf("\n\rY= %f",this->yWorld); |
Ludwigfr | 5:19f24c363418 | 1331 | pc.printf("\n\rtheta= %f",this->thetaWorld); |
Ludwigfr | 5:19f24c363418 | 1332 | */ |
Ludwigfr | 5:19f24c363418 | 1333 | //update odometrie X Y theta... |
Ludwigfr | 5:19f24c363418 | 1334 | } |
Ludwigfr | 5:19f24c363418 | 1335 | |
Ludwigfr | 5:19f24c363418 | 1336 | |
Ludwigfr | 5:19f24c363418 | 1337 |