282 lines
7.0 KiB
Python
282 lines
7.0 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib import animation
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from mpcpy.utils import compute_path_from_wp
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import mpcpy
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P = mpcpy.Params()
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import sys
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import time
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import pybullet as p
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import time
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def get_state(robotId):
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""" """
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robPos, robOrn = p.getBasePositionAndOrientation(robotId)
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linVel, angVel = p.getBaseVelocity(robotId)
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return np.array(
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[
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robPos[0],
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robPos[1],
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np.sqrt(linVel[0] ** 2 + linVel[1] ** 2),
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p.getEulerFromQuaternion(robOrn)[2],
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]
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)
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def set_ctrl(robotId, currVel, acceleration, steeringAngle):
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gearRatio = 1.0 / 21
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steering = [0, 2]
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wheels = [8, 15]
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maxForce = 50
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targetVelocity = currVel + acceleration * P.DT
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for wheel in wheels:
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p.setJointMotorControl2(
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robotId,
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wheel,
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p.VELOCITY_CONTROL,
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targetVelocity=targetVelocity / gearRatio,
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force=maxForce,
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)
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for steer in steering:
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p.setJointMotorControl2(
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robotId, steer, p.POSITION_CONTROL, targetPosition=steeringAngle
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)
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def plot_results(path, x_history, y_history):
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""" """
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plt.style.use("ggplot")
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plt.figure()
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plt.title("MPC Tracking Results")
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plt.plot(
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path[0, :], path[1, :], c="tab:orange", marker=".", label="reference track"
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)
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plt.plot(
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x_history,
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y_history,
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c="tab:blue",
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marker=".",
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alpha=0.5,
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label="vehicle trajectory",
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)
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plt.axis("equal")
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plt.legend()
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plt.show()
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def run_sim():
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""" """
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p.connect(p.GUI)
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p.resetDebugVisualizerCamera(
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cameraDistance=1.0,
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cameraYaw=-90,
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cameraPitch=-45,
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cameraTargetPosition=[-0.1, -0.0, 0.65],
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)
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p.resetSimulation()
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p.setGravity(0, 0, -10)
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useRealTimeSim = 1
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p.setTimeStep(1.0 / 120.0)
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p.setRealTimeSimulation(useRealTimeSim) # either this
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plane = p.loadURDF("racecar/plane.urdf")
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# track = p.loadSDF("racecar/f10_racecar/meshes/barca_track.sdf", globalScaling=1)
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car = p.loadURDF("racecar/f10_racecar/racecar_differential.urdf", [0, 0.3, 0.3])
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for wheel in range(p.getNumJoints(car)):
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# print("joint[",wheel,"]=", p.getJointInfo(car,wheel))
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p.setJointMotorControl2(
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car, wheel, p.VELOCITY_CONTROL, targetVelocity=0, force=0
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)
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p.getJointInfo(car, wheel)
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c = p.createConstraint(
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car,
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9,
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car,
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11,
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jointType=p.JOINT_GEAR,
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jointAxis=[0, 1, 0],
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parentFramePosition=[0, 0, 0],
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childFramePosition=[0, 0, 0],
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)
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p.changeConstraint(c, gearRatio=1, maxForce=10000)
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c = p.createConstraint(
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car,
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10,
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car,
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13,
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jointType=p.JOINT_GEAR,
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jointAxis=[0, 1, 0],
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parentFramePosition=[0, 0, 0],
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childFramePosition=[0, 0, 0],
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)
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p.changeConstraint(c, gearRatio=-1, maxForce=10000)
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c = p.createConstraint(
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car,
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9,
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car,
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13,
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jointType=p.JOINT_GEAR,
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jointAxis=[0, 1, 0],
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parentFramePosition=[0, 0, 0],
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childFramePosition=[0, 0, 0],
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)
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p.changeConstraint(c, gearRatio=-1, maxForce=10000)
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c = p.createConstraint(
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car,
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16,
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car,
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18,
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jointType=p.JOINT_GEAR,
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jointAxis=[0, 1, 0],
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parentFramePosition=[0, 0, 0],
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childFramePosition=[0, 0, 0],
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)
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p.changeConstraint(c, gearRatio=1, maxForce=10000)
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c = p.createConstraint(
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car,
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16,
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car,
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19,
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jointType=p.JOINT_GEAR,
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jointAxis=[0, 1, 0],
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parentFramePosition=[0, 0, 0],
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childFramePosition=[0, 0, 0],
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)
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p.changeConstraint(c, gearRatio=-1, maxForce=10000)
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c = p.createConstraint(
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car,
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17,
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car,
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19,
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jointType=p.JOINT_GEAR,
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jointAxis=[0, 1, 0],
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parentFramePosition=[0, 0, 0],
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childFramePosition=[0, 0, 0],
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)
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p.changeConstraint(c, gearRatio=-1, maxForce=10000)
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c = p.createConstraint(
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car,
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1,
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car,
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18,
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jointType=p.JOINT_GEAR,
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jointAxis=[0, 1, 0],
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parentFramePosition=[0, 0, 0],
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childFramePosition=[0, 0, 0],
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)
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p.changeConstraint(c, gearRatio=-1, gearAuxLink=15, maxForce=10000)
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c = p.createConstraint(
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car,
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3,
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car,
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19,
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jointType=p.JOINT_GEAR,
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jointAxis=[0, 1, 0],
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parentFramePosition=[0, 0, 0],
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childFramePosition=[0, 0, 0],
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)
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p.changeConstraint(c, gearRatio=-1, gearAuxLink=15, maxForce=10000)
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# Interpolated Path to follow given waypoints
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path = compute_path_from_wp(
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[0, 3, 4, 6, 10, 11, 12, 6, 1, 0],
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[0, 0, 2, 4, 3, 3, -1, -6, -2, -2],
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P.path_tick,
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)
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for x_, y_ in zip(path[0, :], path[1, :]):
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p.addUserDebugLine([x_, y_, 0], [x_, y_, 0.33], [0, 0, 1])
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# starting guess
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action = np.zeros(P.M)
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action[0] = P.MAX_ACC / 2 # a
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action[1] = 0.0 # delta
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# Cost Matrices
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Q = np.diag([20, 20, 10, 20]) # state error cost
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Qf = np.diag([30, 30, 30, 30]) # state final error cost
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R = np.diag([10, 10]) # input cost
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R_ = np.diag([10, 10]) # input rate of change cost
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mpc = mpcpy.MPC(P.N, P.M, Q, R)
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x_history = []
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y_history = []
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time.sleep(0.5)
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input("\033[92m Press Enter to continue... \033[0m")
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while 1:
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state = get_state(car)
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x_history.append(state[0])
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y_history.append(state[1])
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# track path in bullet
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p.addUserDebugLine(
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[state[0], state[1], 0], [state[0], state[1], 0.5], [1, 0, 0]
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)
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if np.sqrt((state[0] - path[0, -1]) ** 2 + (state[1] - path[1, -1]) ** 2) < 0.2:
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print("Success! Goal Reached")
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set_ctrl(car, 0, 0, 0)
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plot_results(path, x_history, y_history)
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input("Press Enter to continue...")
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p.disconnect()
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return
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# for MPC car ref frame is used
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state[0:2] = 0.0
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state[3] = 0.0
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# add 1 timestep delay to input
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state[0] = state[0] + state[2] * np.cos(state[3]) * P.DT
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state[1] = state[1] + state[2] * np.sin(state[3]) * P.DT
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state[2] = state[2] + action[0] * P.DT
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state[3] = state[3] + action[0] * np.tan(action[1]) / P.L * P.DT
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# optimization loop
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start = time.time()
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# State Matrices
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A, B, C = mpcpy.get_linear_model_matrices(state, action)
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# Get Reference_traj -> inputs are in worldframe
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target, _ = mpcpy.get_ref_trajectory(get_state(car), path, 1.0)
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# MPC step
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acc, steer = mpc.optimize_linearized_model(
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A, B, C, state, target, time_horizon=P.T, verbose=False
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)
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elapsed = time.time() - start
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print("CVXPY Optimization Time: {:.4f}s".format(elapsed))
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set_ctrl(car, state[2], acc, steer)
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if P.DT - elapsed > 0:
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time.sleep(P.DT - elapsed)
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if __name__ == "__main__":
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run_sim()
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