import numpy as np import matplotlib.pyplot as plt from matplotlib import animation from utils import compute_path_from_wp from cvxpy_mpc import optimize from mpc_config import Params P=Params() import sys import time import pybullet as p import time def get_state(robotId): """ """ robPos, robOrn = p.getBasePositionAndOrientation(robotId) linVel,angVel = p.getBaseVelocity(robotId) return[robPos[0], robPos[1], np.sqrt(linVel[0]**2 + linVel[1]**2), p.getEulerFromQuaternion(robOrn)[2]] def set_ctrl(robotId,currVel,acceleration,steeringAngle): gearRatio=1./21 steering = [0,2] wheels = [8,15] maxForce = 50 targetVelocity = currVel + acceleration*P.dt #targetVelocity=lastVel #print(targetVelocity) for wheel in wheels: p.setJointMotorControl2(robotId,wheel,p.VELOCITY_CONTROL,targetVelocity=targetVelocity/gearRatio,force=maxForce) for steer in steering: p.setJointMotorControl2(robotId,steer,p.POSITION_CONTROL,targetPosition=steeringAngle) def plot_results(path,x_history,y_history): """ """ plt.style.use("ggplot") plt.figure() plt.title("MPC Tracking Results") plt.plot(path[0,:],path[1,:], c='tab:orange',marker=".",label="reference track") plt.plot(x_history, y_history, c='tab:blue',marker=".",alpha=0.5,label="vehicle trajectory") plt.axis("equal") plt.legend() plt.show() def run_sim(): """ """ p.connect(p.GUI) p.resetDebugVisualizerCamera(cameraDistance=1.0, cameraYaw=-90, cameraPitch=-45, cameraTargetPosition=[-0.1,-0.0,0.65]) p.resetSimulation() p.setGravity(0,0,-10) useRealTimeSim = 1 p.setTimeStep(1./120.) p.setRealTimeSimulation(useRealTimeSim) # either this plane = p.loadURDF("racecar/plane.urdf") #track = p.loadSDF("racecar/f10_racecar/meshes/barca_track.sdf", globalScaling=1) car = p.loadURDF("racecar/f10_racecar/racecar_differential.urdf", [0,0.3,.3]) for wheel in range(p.getNumJoints(car)): print("joint[",wheel,"]=", p.getJointInfo(car,wheel)) p.setJointMotorControl2(car,wheel,p.VELOCITY_CONTROL,targetVelocity=0,force=0) p.getJointInfo(car,wheel) c = p.createConstraint(car,9,car,11,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) p.changeConstraint(c,gearRatio=1, maxForce=10000) c = p.createConstraint(car,10,car,13,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) p.changeConstraint(c,gearRatio=-1, maxForce=10000) c = p.createConstraint(car,9,car,13,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) p.changeConstraint(c,gearRatio=-1, maxForce=10000) c = p.createConstraint(car,16,car,18,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) p.changeConstraint(c,gearRatio=1, maxForce=10000) c = p.createConstraint(car,16,car,19,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) p.changeConstraint(c,gearRatio=-1, maxForce=10000) c = p.createConstraint(car,17,car,19,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) p.changeConstraint(c,gearRatio=-1, maxForce=10000) c = p.createConstraint(car,1,car,18,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) p.changeConstraint(c,gearRatio=-1, gearAuxLink = 15, maxForce=10000) c = p.createConstraint(car,3,car,19,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) p.changeConstraint(c,gearRatio=-1, gearAuxLink = 15,maxForce=10000) opt_u = np.zeros((P.M,P.T)) opt_u[0,:] = 1 #m/ss opt_u[1,:] = np.radians(0) #rad/ # Interpolated Path to follow given waypoints path = compute_path_from_wp([0,3,4,6,10,11,12,6,1,0], [0,0,2,4,3,3,-1,-6,-2,-2],P.path_tick) for x_,y_ in zip(path[0,:],path[1,:]): p.addUserDebugLine([x_,y_,0],[x_,y_,0.33],[0,0,1]) x_history=[] y_history=[] time.sleep(0.5) input("Press Enter to continue...") while (1): state = get_state(car) x_history.append(state[0]) y_history.append(state[1]) #add 1 timestep delay to input state[0]=state[0]+state[2]*np.cos(state[3])*P.dt state[1]=state[1]+state[2]*np.sin(state[3])*P.dt state[2]=state[2]+opt_u[0,0]*P.dt state[3]=state[3]+opt_u[0,0]*np.tan(opt_u[1,0])/0.3*P.dt #track path in bullet p.addUserDebugLine([state[0],state[1],0],[state[0],state[1],0.5],[1,0,0]) if np.sqrt((state[0]-path[0,-1])**2+(state[1]-path[1,-1])**2)<0.1: print("Success! Goal Reached") set_ctrl(car,0,0,0) plot_results(path,x_history,y_history) input("Press Enter to continue...") p.disconnect() return #optimization loop start=time.time() opt_u = optimize(state,opt_u,path,ref_vel=1.0) elapsed=time.time()-start print("CVXPY Optimization Time: {:.4f}s".format(elapsed)) set_ctrl(car,state[2],opt_u[0,1],opt_u[1,1]) if P.dt-elapsed>0: time.sleep(P.dt-elapsed) if __name__ == '__main__': run_sim()