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formatted scripts with black

master
mcarfagno 2022-07-21 12:23:08 +01:00
parent 7fd594bd6c
commit 7bcd72cc66
2 changed files with 208 additions and 113 deletions
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5
mpc_pybullet_demo/mpc_demo_nosim.py
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@ -109,9 +109,7 @@ class MPCSim:
# optimization loop
# start=time.time()
# dynamycs w.r.t robot frame
curr_state = np.array(
[0, 0, self.state[2], 0]
)
curr_state = np.array([0, 0, self.state[2], 0])
# State Matrices
A, B, C = mpcpy.get_linear_model_matrices(curr_state, self.action)
# Get Reference_traj -> inputs are in worldframe
@ -139,6 +137,7 @@ class MPCSim:
self.predict([self.action[0], self.action[1]])
self.preview(x_mpc.value)
self.plot_sim()
def predict(self, u):
def kinematics_model(x, t, u):
dxdt = x[2] * np.cos(x[3])

296
mpc_pybullet_demo/mpc_demo_pybullet.py
View File

@ -13,181 +13,277 @@ import time
import pybullet as p
import time
def get_state(robotId):
"""
"""
""" """
robPos, robOrn = p.getBasePositionAndOrientation(robotId)
linVel,angVel = p.getBaseVelocity(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]])
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./21
steering = [0,2]
wheels = [8,15]
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)
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)
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)
p.setJointMotorControl2(
robotId, steer, p.POSITION_CONTROL, targetPosition=steeringAngle
)
def plot_results(path,x_history,y_history):
"""
"""
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.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.resetDebugVisualizerCamera(
cameraDistance=1.0,
cameraYaw=-90,
cameraPitch=-45,
cameraTargetPosition=[-0.1, -0.0, 0.65],
)
p.resetSimulation()
p.setGravity(0,0,-10)
p.setGravity(0, 0, -10)
useRealTimeSim = 1
p.setTimeStep(1./120.)
p.setRealTimeSimulation(useRealTimeSim) # either this
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)
# 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])
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)
# 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,
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,
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,
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,
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,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,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)
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)
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])
for x_, y_ in zip(path[0, :], path[1, :]):
p.addUserDebugLine([x_, y_, 0], [x_, y_, 0.33], [0, 0, 1])
#starting guess
# starting guess
action = np.zeros(P.M)
action[0] = P.MAX_ACC/2 #a
action[1] = 0.0 #delta
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
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=[]
mpc = mpcpy.MPC(P.N, P.M, Q, R)
x_history = []
y_history = []
time.sleep(0.5)
input("Press Enter to continue...")
while (1):
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])
# 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:
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)
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
# 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
# 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()
# optimization loop
start = time.time()
# State Matrices
A,B,C = mpcpy.get_linear_model_matrices(state, action)
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)
# 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)
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(),
# 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]]
action[:] = [u_mpc.value[0, 1], u_mpc.value[1, 1]]
elapsed=time.time()-start
elapsed = time.time() - start
print("CVXPY Optimization Time: {:.4f}s".format(elapsed))
set_ctrl(car,state[2],action[0],action[1])
set_ctrl(car, state[2], action[0], action[1])
if P.DT-elapsed>0:
time.sleep(P.DT-elapsed)
if P.DT - elapsed > 0:
time.sleep(P.DT - elapsed)
if __name__ == '__main__':
if __name__ == "__main__":
run_sim()