mpc_python_learn/mpc_pybullet_demo/mpc_demo_pybullet.py

290 lines
7.2 KiB
Python

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation
from mpcpy.utils import compute_path_from_wp
import mpcpy
P = mpcpy.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 np.array(
[
robPos[0],
robPos[1],
np.sqrt(linVel[0] ** 2 + linVel[1] ** 2),
p.getEulerFromQuaternion(robOrn)[2],
]
)
def set_ctrl(robotId, currVel, acceleration, steeringAngle):
gearRatio = 1.0 / 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(1.0 / 120.0)
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, 0.3])
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)
# 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])
# starting guess
action = np.zeros(P.M)
action[0] = P.MAX_ACC / 2 # a
action[1] = 0.0 # delta
# Cost Matrices
Q = np.diag([20, 20, 10, 20]) # state error cost
Qf = np.diag([30, 30, 30, 30]) # state final error cost
R = np.diag([10, 10]) # input cost
R_ = np.diag([10, 10]) # input rate of change cost
mpc = mpcpy.MPC(P.N, P.M, Q, R)
x_history = []
y_history = []
time.sleep(0.5)
input("\033[92m Press Enter to continue... \033[0m")
while 1:
state = get_state(car)
x_history.append(state[0])
y_history.append(state[1])
# 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) < 0.2:
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
# for MPC car ref frame is used
state[0:2] = 0.0
state[3] = 0.0
# 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] + action[0] * P.DT
state[3] = state[3] + action[0] * np.tan(action[1]) / P.L * P.DT
# optimization loop
start = time.time()
# State Matrices
A, B, C = mpcpy.get_linear_model_matrices(state, action)
# Get Reference_traj -> inputs are in worldframe
target, _ = mpcpy.get_ref_trajectory(get_state(car), path, 1.0)
x_mpc, u_mpc = mpc.optimize_linearized_model(
A, B, C, state, target, time_horizon=P.T, verbose=False
)
# action = np.vstack((np.array(u_mpc.value[0,:]).flatten(),
# (np.array(u_mpc.value[1,:]).flatten())))
action[:] = [u_mpc.value[0, 1], u_mpc.value[1, 1]]
elapsed = time.time() - start
print("CVXPY Optimization Time: {:.4f}s".format(elapsed))
set_ctrl(car, state[2], action[0], action[1])
if P.DT - elapsed > 0:
time.sleep(P.DT - elapsed)
if __name__ == "__main__":
run_sim()