print('The best fitness reaches the threshold value.')
break
if g >= 5:
diff = np.abs(self.history[-5][1] - self.history[-1][1])
if diff < 1e-2:
print('The best fitness does not change for 10 steps.')
break
self.gtot += 1
if __name__ == '__main__':
# World
world = World()
world.generate_frame([-10, 10], [-10, 10])
world.type = 'cartesian'
# Objects in the cartesian space
og = ObjectGenerator(world)
og.generate_object([[3.5, 6.5], [3.5, 3.5], [6.5, 3.5], [6.5, 6.5]])
# Robot
robot2R = RobotArm(base=[0, 0], lengths=[3.0, 5.0])
robot2R.start = np.array([pi / 2, -pi / 7])
robot2R.goal = np.array([pi / 5, -pi / 7])
# C-space
cspace = World()
cspace.robot = robot2R
cspace.generate_frame([-pi, pi], [-pi, pi])
self.path = np.vstack((djk1.path, djk2.path))
self.smoothed_path = self.smoothing(self.path)
# '''
self.path_list.append(self.smoothed_path)
if verbose:
t2 = time.time()
print('Generate multiple paths: {}\n'.format(t2 - t1))
if __name__ == '__main__':
# --- world class
world = World()
world.generate_frame([-pi, pi], [-pi, pi])
# --- Set start/goal point
world.start = np.array([-3.0, -3.0])
world.goal = np.array([3.0, 3.0])
# --- Set objects
og = ObjectGenerator(world)
og.generate_object_sample1()
og.set_object_type()
# '''
# --- Generate 10x10 valid roadmaps
t0 = time.time()
prm_list = [PRM(world, 100, 5) for i in range(10)]
path_list = []