142 lines
4.2 KiB
Python
142 lines
4.2 KiB
Python
from map import Map
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import numpy as np
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from reference_path import ReferencePath, Obstacle
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from spatial_bicycle_models import BicycleModel
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import matplotlib.pyplot as plt
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from MPC import MPC
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from scipy import sparse
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from time import time
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from lidar_model import LidarModel
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if __name__ == '__main__':
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# Select Simulation Mode | 'Race' or 'Q'
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sim_mode = 'Race'
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# Create Map
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if sim_mode == 'Race':
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map = Map(file_path='map_race.png', origin=[-1, -2], resolution=0.005)
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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,
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-1.5, -1.5]
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# Specify path resolution
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path_resolution = 0.05 # m / wp
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# Create smoothed reference path
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reference_path = ReferencePath(map, wp_x, wp_y, path_resolution,
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smoothing_distance=5, max_width=0.23,
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circular=True)
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elif sim_mode == 'Q':
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map = Map(file_path='map_floor2.png')
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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 = 0.20 # m / wp
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# Create smoothed reference path
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reference_path = ReferencePath(map, wp_x, wp_y, path_resolution,
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smoothing_distance=5, max_width=1.50,
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circular=False)
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else:
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print('Invalid Simulation Mode!')
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map, wp_x, wp_y, path_resolution, reference_path \
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= None, None, None, None, None
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exit(1)
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obs1 = Obstacle(cx=0.0, cy=0.0, radius=0.05)
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obs2 = Obstacle(cx=-0.8, cy=-0.5, radius=0.05)
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obs3 = Obstacle(cx=-0.7, cy=-1.5, radius=0.07)
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obs4 = Obstacle(cx=-0.3, cy=-1.0, radius=0.07)
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obs5 = Obstacle(cx=0.3, cy=-1.0, radius=0.05)
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obs6 = Obstacle(cx=0.75, cy=-1.5, radius=0.07)
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obs7 = Obstacle(cx=0.7, cy=-0.9, radius=0.08)
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obs8 = Obstacle(cx=1.2, cy=0.0, radius=0.08)
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obs9 = Obstacle(cx=0.7, cy=-0.1, radius=0.05)
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obs10 = Obstacle(cx=1.1, cy=-0.6, radius=0.07)
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reference_path.add_obstacles([obs1, obs2, obs3, obs4, obs5, obs6, obs7,
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obs8, obs9, obs10])
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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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t_0 = 0.0
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car = BicycleModel(length=0.12, width=0.06, reference_path=reference_path,
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e_y=e_y_0, e_psi=e_psi_0, t=t_0)
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##############
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# Controller #
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##############
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N = 30
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Q = sparse.diags([1.0, 0.0, 0.0])
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R = sparse.diags([1.0, 0.0])
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QN = sparse.diags([0.0, 0.0, 0.0])
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InputConstraints = {'umin': np.array([0.0, -np.tan(0.66)/car.l]),
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'umax': np.array([2.5, np.tan(0.66)/car.l])}
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StateConstraints = {'xmin': np.array([-np.inf, -np.inf, -np.inf]),
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'xmax': np.array([np.inf, np.inf, np.inf])}
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velocity_reference = 1.5 # m/s
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mpc = MPC(car, N, Q, R, QN, StateConstraints, InputConstraints,
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velocity_reference)
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#########
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# LiDAR #
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#########
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sensor = LidarModel(FoV=90, range=0.25, resolution=4.0)
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##############
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# Simulation #
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##############
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# Sampling time
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Ts = 0.05
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t = 0
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car.set_sampling_time(Ts)
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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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# Until arrival at end of path
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while car.s < reference_path.length:
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# get control signals
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u = mpc.get_control()
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# drive car
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car.drive(u)
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# log
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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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###################
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# Plot Simulation #
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###################
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# Plot path and drivable area
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reference_path.show()
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# Plot MPC prediction
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mpc.show_prediction()
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# Plot car
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car.show()
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t += Ts
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plt.title('MPC Simulation: v(t): {:.2f}, delta(t): {:.2f}, Duration: '
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'{:.2f} s'.
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format(u[0], u[1], t))
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plt.pause(0.01)
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print('Final Time: {:.2f}'.format(t))
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plt.close()
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