125 lines
3.6 KiB
Python
125 lines
3.6 KiB
Python
from map import Map
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import numpy as np
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from reference_path import ReferencePath
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from spatial_bicycle_models import SimpleBicycleModel, ExtendedBicycleModel
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import matplotlib.pyplot as plt
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from MPC import MPC
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from time import time
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if __name__ == '__main__':
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# Create Map
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map = Map(file_path='map_race.png', origin=[-1, -2], resolution=0.005)
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#map = Map(file_path='map_floor2.png')
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# Specify waypoints
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wp_x = [-0.75, -0.25, -0.25, 0.25, 0.25, 1.25, 1.25, 0.75, 0.75, 1.25,
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1.25, -0.75, -0.75, -0.25]
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wp_y = [-1.5, -1.5, -0.5, -0.5, -1.5, -1.5, -1, -1, -0.5, -0.5, 0, 0, -1.5,
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-1.5]
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#wp_x = [-9.169, 11.9, 7.3, -6.95]
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#wp_y = [-15.678, 10.9, 14.5, -3.31]
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# Specify path resolution
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path_resolution = 0.05 # m / wp
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# Smooth Path
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reference_path = ReferencePath(map, wp_x, wp_y, path_resolution,
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smoothing_distance=5)
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rx = [wp.x for wp in reference_path.waypoints]
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ry = [wp.y for wp in reference_path.waypoints]
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################
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# Motion Model #
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################
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# initial state
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e_y_0 = 0.0
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e_psi_0 = 0.0
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v_x_0 = 0.3
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v_y_0 = 0
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omega_0 = 0
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t_0 = 0
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# initialize car
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car = SimpleBicycleModel(reference_path=reference_path,
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e_y=e_y_0, e_psi=e_psi_0, v=v_x_0)
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#car = ExtendedBicycleModel(reference_path=reference_path,
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# e_y=e_y_0, e_psi=e_psi_0, v_x=v_x_0, v_y=v_y_0,
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# omega=omega_0, t=t_0)
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##############
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# Controller #
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##############
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# path tracker
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T = 10
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Q = np.diag([0.1, 0.001, 0.1])
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Qf = Q
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#Q = np.diag([1, 0, 0, 0, 0, 0])
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#Qf = Q
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R = np.diag([0, 0])
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StateConstraints = {'e_y': (-0.1, 0.1), 'v': (0, 4)}
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InputConstraints = {'D': (-1, 1), 'delta': (-0.44, 0.44)}
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Reference = {'e_y': 0, 'e_psi': 0, 'v': 4.0}
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#Reference = {'e_y': 0, 'e_psi': 0, 'v_x': 1.0, 'v_y': 0, 'omega': 0, 't':0}
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mpc = MPC(car, T, Q, R, Qf, StateConstraints, InputConstraints, Reference)
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##############
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# Simulation #
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##############
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# logging containers
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x_log = [car.temporal_state.x]
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y_log = [car.temporal_state.y]
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psi_log = [car.temporal_state.psi]
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v_log = [car.temporal_state.v_x]
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D_log = []
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delta_log = []
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start_time = time()
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# iterate over waypoints
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for wp_id in range(len(car.reference_path.waypoints)-T-1):
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print('V: {:.2f}'.format(car.temporal_state.v_x))
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# get control signals
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D, delta = mpc.get_control()
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# drive car
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car.drive(delta, D)
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# log current state
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x_log.append(car.temporal_state.x)
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y_log.append(car.temporal_state.y)
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v_log.append(car.temporal_state.v_x)
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D_log.append(D)
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delta_log.append(delta)
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###################
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# Plot Simulation #
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###################
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# plot path
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car.reference_path.show()
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# plot car trajectory and velocity
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plt.scatter(x_log, y_log, c='g', s=15)
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# plot mpc prediction
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if mpc.current_prediction is not None:
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x_pred = mpc.current_prediction[0]
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y_pred = mpc.current_prediction[1]
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plt.scatter(x_pred, y_pred, c='b', s=10)
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plt.title('MPC Simulation: Position: {:.2f} m, {:.2f} m, Velocity: '
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'{:.2f} m/s'.format(car.temporal_state.x,
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car.temporal_state.y, car.temporal_state.v_x))
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plt.xticks([])
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plt.yticks([])
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plt.pause(0.0000001)
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end_time = time()
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print('Time Elapsed: {:.2f} s'.format(end_time-start_time))
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plt.close()
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