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)