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Multi-Purpose-MPC
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Update and declutter simulation script. 

Add obstacles for Race path.
master
matssteinweg 2019-12-01 16:04:18 +01:00
parent bbae14c519
commit 12329708a7
1 changed files with 40 additions and 26 deletions
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68
simulation.py
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@ -1,11 +1,10 @@
from map import Map
import numpy as np
from reference_path import ReferencePath
from reference_path import ReferencePath, Obstacle
from spatial_bicycle_models import BicycleModel
import matplotlib.pyplot as plt
from MPC import MPC, MPC_OSQP
from scipy import sparse
import time
if __name__ == '__main__':
@ -23,20 +22,36 @@ if __name__ == '__main__':
-1.5, -1.5]
# Specify path resolution
path_resolution = 0.05 # m / wp
# Create smoothed reference path
reference_path = ReferencePath(map, wp_x, wp_y, path_resolution,
smoothing_distance=5, max_width=0.22)
elif sim_mode == 'Q':
map = Map(file_path='map_floor2.png')
wp_x = [-9.169, 11.9, 7.3, -6.95]
wp_y = [-15.678, 10.9, 14.5, -3.31]
# Specify path resolution
path_resolution = 0.20 # m / wp
else:
print('Invalid Simulation Mode!')
map, wp_x, wp_y, path_resolution = None, None, None, None
exit(1)
# Create smoothed reference path
reference_path = ReferencePath(map, wp_x, wp_y, path_resolution,
smoothing_distance=5)
smoothing_distance=5, max_width=1.50)
else:
print('Invalid Simulation Mode!')
map, wp_x, wp_y, path_resolution, reference_path \
= None, None, None, None, None
exit(1)
obs1 = Obstacle(cx=0.0, cy=0.0, radius=0.05)
obs2 = Obstacle(cx=-0.8, cy=-0.5, radius=0.05)
obs3 = Obstacle(cx=-0.7, cy=-1.5, radius=0.07)
obs4 = Obstacle(cx=-0.3, cy=-1.0, radius=0.07)
obs5 = Obstacle(cx=0.3, cy=-1.0, radius=0.05)
obs6 = Obstacle(cx=0.75, cy=-1.5, radius=0.07)
obs7 = Obstacle(cx=0.7, cy=-0.9, radius=0.08)
obs8 = Obstacle(cx=1.2, cy=0.0, radius=0.08)
obs9 = Obstacle(cx=0.7, cy=-0.1, radius=0.05)
obs10 = Obstacle(cx=1.1, cy=-0.6, radius=0.07)
reference_path.add_obstacles([obs1, obs2, obs3, obs4, obs5, obs6, obs7,
obs8, obs9, obs10])
################
# Motion Model #
@ -48,19 +63,21 @@ if __name__ == '__main__':
t_0 = 0.0
v = 1.0
car = BicycleModel(reference_path=reference_path,
car = BicycleModel(length=0.12, width=0.06, reference_path=reference_path,
e_y=e_y_0, e_psi=e_psi_0, t=t_0)
##############
# Controller #
##############
N = 20
Q = sparse.diags([0.01, 0.0, 0.4])
R = sparse.diags([0.01])
N = 30
Q = sparse.diags([1.0, 0.0, 0.1])
R = sparse.diags([0.0001])
QN = Q
InputConstraints = {'umin': np.array([-np.tan(0.44)/car.l]), 'umax': np.array([np.tan(0.44)/car.l])}
StateConstraints = {'xmin': np.array([-0.2, -np.inf, 0]), 'xmax': np.array([0.2, np.inf, np.inf])}
InputConstraints = {'umin': np.array([-np.tan(0.66)/car.l]),
'umax': np.array([np.tan(0.66)/car.l])}
StateConstraints = {'xmin': np.array([-np.inf, -np.inf, -np.inf]),
'xmax': np.array([np.inf, np.inf, np.inf])}
mpc = MPC_OSQP(car, N, Q, R, QN, StateConstraints, InputConstraints)
##############
@ -72,13 +89,10 @@ if __name__ == '__main__':
y_log = [car.temporal_state.y]
# iterate over waypoints
for wp_id in range(len(car.reference_path.waypoints)-mpc.N-1):
for wp_id in range(len(car.reference_path.waypoints)-N-1):
# get control signals
start = time.time()
delta = mpc.get_control(v)
end = time.time()
u = np.array([v, delta])
u = mpc.get_control(v)
# drive car
car.drive(u)
@ -90,17 +104,17 @@ if __name__ == '__main__':
###################
# Plot Simulation #
###################
# plot path
car.reference_path.show()
# plot car trajectory and velocity
plt.scatter(x_log[:-1], y_log[:-1], c='g', s=15)
# Plot path and drivable area
reference_path.show()
plt.scatter(mpc.current_prediction[0], mpc.current_prediction[1], c='b', s=5)
# Plot MPC prediction
mpc.show_prediction()
# Plot car
car.show()
plt.title('MPC Simulation: Position: {:.2f} m, {:.2f} m'.
format(car.temporal_state.x, car.temporal_state.y))
plt.xticks([])
plt.yticks([])
plt.pause(0.00000001)
plt.pause(0.05)
plt.close()