282 lines
7.9 KiB
Python
Executable File
282 lines
7.9 KiB
Python
Executable File
#! /usr/bin/env python
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.integrate import odeint
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from cvxpy_mpc.utils import compute_path_from_wp, get_ref_trajectory
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from cvxpy_mpc import MPC, VehicleModel
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import sys
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# Robot Starting position
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SIM_START_X = 0.0
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SIM_START_Y = 0.5
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SIM_START_V = 0.0
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SIM_START_H = 0.0
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# Params
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TARGET_VEL = 1.0 # m/s
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T = 5 # Prediction Horizon [s]
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DT = 0.2 # discretization step [s]
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L = 0.3 # vehicle wheelbase [m]
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# Classes
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class MPCSim:
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def __init__(self):
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# State of the robot [x,y,v, heading]
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self.state = np.array([SIM_START_X, SIM_START_Y, SIM_START_V, SIM_START_H])
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# helper variable to keep track of mpc output
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# starting condition is 0,0
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self.control = np.zeros(2)
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self.K = int(T / DT)
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Q = [20, 20, 10, 20] # state error cost
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Qf = [30, 30, 30, 30] # state final error cost
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R = [10, 10] # input cost
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P = [10, 10] # input rate of change cost
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self.mpc = MPC(VehicleModel(), T, DT, Q, Qf, R, P)
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# Path from waypoint interpolation
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self.path = compute_path_from_wp(
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[0, 3, 4, 6, 10, 12, 13, 13, 6, 1, 0],
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[0, 0, 2, 4, 3, 3, -1, -2, -6, -2, -2],
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0.05,
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)
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# Helper variables to keep track of the sim
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self.sim_time = 0
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self.x_history = []
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self.y_history = []
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self.v_history = []
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self.h_history = []
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self.a_history = []
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self.d_history = []
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self.optimized_trajectory = None
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# Initialise plot
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plt.style.use("ggplot")
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self.fig = plt.figure()
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plt.ion()
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plt.show()
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def ego_to_global(self, mpc_out):
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"""
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transforms optimized trajectory XY points from ego(car) reference
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into global(map) frame
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Args:
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mpc_out ():
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"""
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trajectory = np.zeros((2, self.K))
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trajectory[:, :] = mpc_out[0:2, 1:]
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Rotm = np.array(
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[
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[np.cos(self.state[3]), np.sin(self.state[3])],
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[-np.sin(self.state[3]), np.cos(self.state[3])],
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]
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)
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trajectory = (trajectory.T.dot(Rotm)).T
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trajectory[0, :] += self.state[0]
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trajectory[1, :] += self.state[1]
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return trajectory
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def run(self):
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"""
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[TODO:summary]
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[TODO:description]
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"""
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self.plot_sim()
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input("Press Enter to continue...")
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while 1:
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if (
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np.sqrt(
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(self.state[0] - self.path[0, -1]) ** 2
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+ (self.state[1] - self.path[1, -1]) ** 2
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)
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< 0.5
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):
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print("Success! Goal Reached")
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input("Press Enter to continue...")
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return
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# optimization loop
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# start=time.time()
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# Get Reference_traj -> inputs are in worldframe
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target = get_ref_trajectory(self.state, self.path, TARGET_VEL, T, DT)
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# dynamycs w.r.t robot frame
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curr_state = np.array([0, 0, self.state[2], 0])
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x_mpc, u_mpc = self.mpc.step(
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curr_state,
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target,
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self.control,
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verbose=False,
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)
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# print("CVXPY Optimization Time: {:.4f}s".format(time.time()-start))
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# only the first one is used to advance the simulation
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self.control[:] = [u_mpc.value[0, 0], u_mpc.value[1, 0]]
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self.state = self.predict_next_state(
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self.state, [self.control[0], self.control[1]], DT
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)
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# use the optimizer output to preview the predicted state trajectory
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self.optimized_trajectory = self.ego_to_global(x_mpc.value)
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self.plot_sim()
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def predict_next_state(self, state, u, dt):
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def kinematics_model(x, t, u):
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dxdt = x[2] * np.cos(x[3])
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dydt = x[2] * np.sin(x[3])
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dvdt = u[0]
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dthetadt = x[2] * np.tan(u[1]) / L
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dqdt = [dxdt, dydt, dvdt, dthetadt]
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return dqdt
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# solve ODE
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tspan = [0, dt]
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new_state = odeint(kinematics_model, state, tspan, args=(u[:],))[1]
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return new_state
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def plot_sim(self):
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self.sim_time = self.sim_time + DT
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self.x_history.append(self.state[0])
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self.y_history.append(self.state[1])
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self.v_history.append(self.state[2])
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self.h_history.append(self.state[3])
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self.a_history.append(self.control[0])
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self.d_history.append(self.control[1])
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plt.clf()
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grid = plt.GridSpec(2, 3)
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plt.subplot(grid[0:2, 0:2])
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plt.title(
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"MPC Simulation \n" + "Simulation elapsed time {}s".format(self.sim_time)
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)
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plt.plot(
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self.path[0, :],
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self.path[1, :],
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c="tab:orange",
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marker=".",
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label="reference track",
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)
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plt.plot(
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self.x_history,
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self.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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if self.optimized_trajectory is not None:
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plt.plot(
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self.optimized_trajectory[0, :],
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self.optimized_trajectory[1, :],
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c="tab:green",
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marker="+",
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alpha=0.5,
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label="mpc opt trajectory",
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)
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# plt.plot(self.x_history[-1], self.y_history[-1], c='tab:blue',
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# marker=".",
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# markersize=12,
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# label="vehicle position")
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# plt.arrow(self.x_history[-1],
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# self.y_history[-1],
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# np.cos(self.h_history[-1]),
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# np.sin(self.h_history[-1]),
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# color='tab:blue',
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# width=0.2,
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# head_length=0.5,
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# label="heading")
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plot_car(self.x_history[-1], self.y_history[-1], self.h_history[-1])
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plt.ylabel("map y")
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plt.yticks(
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np.arange(min(self.path[1, :]) - 1.0, max(self.path[1, :] + 1.0) + 1, 1.0)
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)
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plt.xlabel("map x")
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plt.xticks(
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np.arange(min(self.path[0, :]) - 1.0, max(self.path[0, :] + 1.0) + 1, 1.0)
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)
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plt.axis("equal")
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# plt.legend()
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plt.subplot(grid[0, 2])
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# plt.title("Linear Velocity {} m/s".format(self.v_history[-1]))
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plt.plot(self.a_history, c="tab:orange")
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locs, _ = plt.xticks()
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plt.xticks(locs[1:], locs[1:] * DT)
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plt.ylabel("a(t) [m/ss]")
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plt.xlabel("t [s]")
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plt.subplot(grid[1, 2])
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# plt.title("Angular Velocity {} m/s".format(self.w_history[-1]))
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plt.plot(np.degrees(self.d_history), c="tab:orange")
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plt.ylabel("gamma(t) [deg]")
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locs, _ = plt.xticks()
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plt.xticks(locs[1:], locs[1:] * DT)
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plt.xlabel("t [s]")
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plt.tight_layout()
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plt.draw()
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plt.pause(0.1)
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def plot_car(x, y, yaw):
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"""
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Args:
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x ():
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y ():
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yaw ():
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"""
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LENGTH = 0.5 # [m]
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WIDTH = 0.25 # [m]
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OFFSET = LENGTH # [m]
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outline = np.array(
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[
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[-OFFSET, (LENGTH - OFFSET), (LENGTH - OFFSET), -OFFSET, -OFFSET],
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[WIDTH / 2, WIDTH / 2, -WIDTH / 2, -WIDTH / 2, WIDTH / 2],
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]
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)
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Rotm = np.array([[np.cos(yaw), np.sin(yaw)], [-np.sin(yaw), np.cos(yaw)]])
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outline = (outline.T.dot(Rotm)).T
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outline[0, :] += x
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outline[1, :] += y
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plt.plot(
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np.array(outline[0, :]).flatten(), np.array(outline[1, :]).flatten(), "tab:blue"
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)
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def do_sim():
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sim = MPCSim()
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try:
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sim.run()
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except Exception as e:
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sys.exit(e)
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if __name__ == "__main__":
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do_sim()
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