def test_new_gen_env():
from env_gen import load_folder_and_plot
from obstacle_environment import ObstacleEnvironment2D
from mip_planner import MIPPlanner, TESTA, TESTB
from core.dynamics.linear_system_dynamics import LinearSystemDynamics
import matplotlib.pyplot as plt
START = (15, 10, 0, 0)
END = (90, 90, 0, 0)
OBSTACLES = None
x_b = (0, 100)
y_b = (0, 100)
fig = plt.figure()
ax = fig.add_subplot(111)
a, b = load_folder_and_plot(ax, (START[0], START[1]), (END[0], END[1]),
"../envs/2", x_b, y_b) # read and plot
START_ENV = ObstacleEnvironment2D(START, END, OBSTACLES, a, b)
planner = MIPPlanner(START_ENV, LinearSystemDynamics(TESTA, TESTB),
T=50,
h_k=0.01, presolve=0)
planner.set_optim_timelimit(180)
wp, _, _, _, _, _ = planner.optimize_path()
START_ENV.plot_path(wp, ax)
ax.set_xlim(-50, 200)
ax.set_ylim(-50, 200)
fig.show()
fig = plt.figure()
ax = fig.add_subplot(111)
a, b = load_folder_and_plot(ax, (START[0], START[1]), (END[0], END[1]),
"envs/10", x_b, y_b) # read and plot
paths = generate_path_list(a, b, START, END)
print(paths)
# pick a path
p = paths[np.random.randint(len(paths))]
print(p)
START_ENV = ObstacleEnvironment2D(START, END, OBSTACLES, a, b)
planner = MIPPlanner(START_ENV,
LinearSystemDynamics(TESTA, TESTB),
T=t,
h_k=0.001,
presolve=2)
planner.set_optim_timelimit(10)
last_inactive_set = np.zeros((t, len(a)), dtype=bool)
for row in range(0, int(t / time_steps)):
last_inactive_set[row][p[0]] = True
last_inactive_set[180 + row][p[-1]] = True
curr_reg = 0
wp = None
ob = None
runtime = 0
for step in range(0, time_steps):
print("STEP " + str(step))
active_set = np.zeros((t, len(a)), dtype=bool)
inactive_set = last_inactive_set.copy()
for row in range(0, int(t / time_steps)):
active_set[20 * step + row] = True
for step2 in range(step + 1, time_steps - 1):