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mpc_python_learn
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tidy up comments, remove useless vars

master
mcarfagno 2023-10-24 09:19:22 +01:00
parent 1e62c0cbe7
commit 7369fedcb3
1 changed files with 17 additions and 20 deletions
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37
mpc_pybullet_demo/mpc_demo_nosim.py
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@ -27,33 +27,32 @@ L = 0.3 # vehicle wheelbase [m]
# Classes
class MPCSim:
def __init__(self):
# State for the robot mathematical model [x,y,heading]
# State of the robot [x,y,v, heading]
self.state = np.array([SIM_START_X, SIM_START_Y, SIM_START_V, SIM_START_H])
# starting guess
# helper variable to keep track of mpc output
self.action = np.zeros(2)
# starting guess
self.action[0] = 1.0 # a
self.action[1] = 0.0 # delta
self.K = int(T / DT)
self.opt_u = np.zeros((2, self.K))
# Weights for Cost Matrices
Q = [20, 20, 10, 20] # state error cost
Qf = [30, 30, 30, 30] # state final error cost
R = [10, 10] # input cost
P = [10, 10] # input rate of change cost
self.mpc = MPC(VehicleModel(), T, DT, Q, Qf, R, P)
# Interpolated Path to follow given waypoints
# Path from waypoint interpolation
self.path = compute_path_from_wp(
[0, 3, 4, 6, 10, 12, 13, 13, 6, 1, 0],
[0, 0, 2, 4, 3, 3, -1, -2, -6, -2, -2],
0.05,
)
# Sim help vars
# Helper variables to keep track of the sim
self.sim_time = 0
self.x_history = []
self.y_history = []
@ -75,7 +74,7 @@ class MPCSim:
[TODO:description]
"""
predicted = np.zeros(self.opt_u.shape)
predicted = np.zeros((2, self.K))
predicted[:, :] = mpc_out[0:2, 1:]
Rotm = np.array(
[
@ -109,27 +108,25 @@ class MPCSim:
return
# optimization loop
# start=time.time()
# dynamycs w.r.t robot frame
curr_state = np.array([0, 0, self.state[2], 0])
# Get Reference_traj -> inputs are in worldframe
target = get_ref_trajectory(self.state, self.path, TARGET_VEL, T, DT)
# dynamycs w.r.t robot frame
curr_state = np.array([0, 0, self.state[2], 0])
x_mpc, u_mpc = self.mpc.step(
curr_state,
target,
self.action,
verbose=False,
)
# NOTE: used only for preview purposes
self.opt_u = np.vstack(
(
np.array(u_mpc.value[0, :]).flatten(),
np.array(u_mpc.value[1, :]).flatten(),
)
)
self.action[:] = [u_mpc.value[0, 0], u_mpc.value[1, 0]]
# print("CVXPY Optimization Time: {:.4f}s".format(time.time()-start))
# only the first one is used to advance the simulation
self.action[:] = [u_mpc.value[0, 0], u_mpc.value[1, 0]]
self.predict([self.action[0], self.action[1]], DT)
# use the state trajectory to preview the optimizer output
self.preview(x_mpc.value)
self.plot_sim()
@ -157,8 +154,8 @@ class MPCSim:
self.y_history.append(self.state[1])
self.v_history.append(self.state[2])
self.h_history.append(self.state[3])
self.a_history.append(self.opt_u[0, 1])
self.d_history.append(self.opt_u[1, 1])
self.a_history.append(self.action[0])
self.d_history.append(self.action[1])
plt.clf()