SAMPLING_TIME = 0.02
HORIZON = 20
COST_Q = np.diag([1, 1])
COST_P = np.diag([0, 0])
COST_R = np.diag([5 / 1000, 1])
if not TRACK_CONS:
SUFFIX = 'NOCONS-'
else:
SUFFIX = ''
#####################################################################
# load vehicle parameters
params = ORCA(control='pwm')
model = Dynamic(**params)
#####################################################################
# load track
TRACK_NAME = 'ETHZ'
track = ETHZ(reference='optimal', longer=True)
SIM_TIME = 8.5
#####################################################################
# load GP models
with open('../gp/orca/vxgp.pickle', 'rb') as f:
(vxmodel, vxxscaler, vxyscaler) = pickle.load(f)
vxgp = loadGPModel('vx', vxmodel, vxxscaler, vxyscaler)
PLOT_RESULTS = False # whether to plot results
SAVE_RESULTS = True # whether to save results
N_WAYPOINTS = 100 # resampled waypoints
SCALE = 0.95 # shrinking factor for track width
# define indices for the nodes
NODES = [
5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95,
105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175,
180, 185
]
#####################################################################
# track specific data
params = ORCA()
track = ETHZ()
track_width = track.track_width * SCALE
theta = track.theta_track[NODES]
N_DIMS = len(NODES)
n_waypoints = N_DIMS
rand_traj = randomTrajectory(track=track, n_waypoints=n_waypoints)
bounds = torch.tensor(
[[-track_width / 2] * N_DIMS, [track_width / 2] * N_DIMS],
device=device,
dtype=dtype)
