mpc_python_learn/mpc_pybullet_demo/mpc_demo_pybullet.py

154 lines
5.3 KiB
Python

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()