def do_stuff_with_map(map):
# the following code is exactly the same as in do_stuff_with_map in ROSstuff.py
# setting the parameters for the STL specification generator
time_bound = 20
goal = (3, 1)
accuracy = 0.25
time_steps = time_bound + 1
# setting the parameters for the optimizer
initial_state = np.asarray([0.5, 0, 0.5, 0])[:, np.newaxis]
u_guess = np.zeros((2, time_steps)).flatten()
u_guess = np.asarray(
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
]])
# optimization method
method = 'Powell'
my_reachavoid = ReachAvoid(map, time_bound, goal, accuracy)
ax = my_reachavoid.return_region()
my_finished_specification = my_reachavoid.full_spec
my_optimizer = Optimizer(initial_state, my_finished_specification,
time_bound, time_steps, u_guess, ax)
optimal_trajectory = my_optimizer.optimize(method)
print("robustness: %s" % (my_optimizer.rho(optimal_trajectory)))
my_optimizer.plot_trajectory(optimal_trajectory)
print(my_reachavoid.full_spec)
def do_stuff_with_map(map):
# setting the parameters for the STL specification generator
time_bound = 20
goal = (11.1, 8.8)
accuracy = 0.45
time_steps = time_bound + 1
# setting the parameters for the optimizer
initial_state = np.asarray([7.9, 0, 11, 0])[:, np.newaxis]
u_guess = np.zeros((2, time_steps))
# optimization method
method = 'Nelder-Mead'
my_reachavoid = ReachAvoid(map, time_bound, goal, accuracy)
ax = my_reachavoid.return_region()
my_finished_specification = my_reachavoid.full_spec
my_optimizer = Optimizer(initial_state, my_finished_specification,
time_bound, time_steps, u_guess, ax)
optimal_trajectory = my_optimizer.optimize(method)
print("robustness: %s" % (my_optimizer.rho(optimal_trajectory)))
my_optimizer.plot_trajectory(optimal_trajectory)
