def reset(self):
# camera
dronesim.installcamera([0,-15,180], 110, 63, 0.01, 500)
# state related property
position_hunter = [0.0, 0.0, 10.0] # x, y, z
orientation_hunter = [0.0, 0.0, 0.0] # roll, pitch, taw
#the position of target is generated randomly and should not exceed the vision range of hunter
position_target = (np.array([10.0, 0.0, 10.0]) + np.random.normal(0, 5)).tolist() # x, y, z
orientation_target = [0.0, 0.0, 0.0] # roll, pitch, yaw
self.roll_target, self.pitch_target, self.yaw_target, self.thrust_target = 0, 0, 0, 0
absolute_x, absolute_y, target_in_front = dronesim.projection(position_hunter, orientation_hunter, position_target, float(self.width), float(self.height))
distance = np.linalg.norm(np.array(position_hunter) - np.array(position_target))
# invalid initialization
while (not target_in_front or absolute_x > self.max_absolute_x or absolute_x < self.min_absolute_x or absolute_y > self.max_absolute_y or absolute_y < self.min_absolute_y or distance > self.max_initial_distance or distance < self.min_initial_distance):
position_target = (np.array([10.0, 0.0, 10.0]) + np.random.normal(0, 5)).tolist()
absolute_x, absolute_y, target_in_front = dronesim.projection(position_hunter, orientation_hunter, position_target, float(self.width), float(self.height))
distance = np.linalg.norm(np.array(position_hunter) - np.array(position_target))
dronesim.siminit(np.squeeze(np.asarray(position_hunter)),np.squeeze(np.asarray(orientation_hunter)), \
np.squeeze(np.asarray(position_target)),np.squeeze(np.asarray(orientation_target)), 20, 5)
self.init_pos_target = position_target
self.previous_distance = distance
self.state = self.get_state()
self.iteration = 0
self.episodes = 0
return self.state
def reset(self):
# camera
dronesim.installcamera([0, -15, 180], 110, 63, 0.01, 500)
# state related property
position_hunter = [0.0, 0.0, 10.0] # x, y, z
orientation_hunter = [0.0, 0.0, 0.0] # roll, pitch, taw
# position_hunter = np.matrix([44.41015975 -3.96066086 7.88967305])
# orientation_hunter = np.matrix([-11.276222823141545, 8.627114153116235, 27.256829024182572])
# position_target = np.matrix([17.49014858 -5.98676401 21.02755752])
#the position of target is generated randomly and should not exceed the vision range of hunter
position_target = (np.array([10.0, 0.0, 10.0]) +
np.random.normal(0, 5)).tolist() # x, y, z
orientation_target = [0.0, 0.0, 0.0] # roll, pitch, yaw
self.roll_target, self.pitch_target, self.yaw_target, self.thrust_target = 0, 0, 0, 0
# print('error c')
# print('lala: ', position_hunter, orientation_hunter, position_target, self.width, self.height)
absolute_x, absolute_y, target_in_front = dronesim.projection(
position_hunter, orientation_hunter, position_target,
float(self.width), float(self.height))
# print('position: ', position_hunter)
# print('error e')
# print('pos_hunter: ', position_hunter)
# print('pos_target: ', position_target)
distance = np.linalg.norm(
np.array(position_hunter) - np.array(position_target))
# invalid initialization
# print('error d')
while (not target_in_front or absolute_x > self.max_absolute_x
or absolute_x < self.min_absolute_x
or absolute_y > self.max_absolute_y
or absolute_y < self.min_absolute_y
or distance > self.max_initial_distance
or distance < self.min_initial_distance):
position_target = (np.array([10.0, 0.0, 10.0]) +
np.random.normal(0, 5)).tolist()
absolute_x, absolute_y, target_in_front = dronesim.projection(
position_hunter, orientation_hunter, position_target,
float(self.width), float(self.height))
distance = np.linalg.norm(
np.array(position_hunter) - np.array(position_target))
# print(position_hunter, orientation_hunter, position_target)
# position_hunter = np.matrix([38.5, 26.8, 30.3])
# orientation_hunter = np.matrix([20.5,12.9,84.9])
# position_target = np.matrix([16.4,8.8,39.9])
# print('error a')
dronesim.siminit(np.squeeze(np.asarray(position_hunter)),np.squeeze(np.asarray(orientation_hunter)), \
np.squeeze(np.asarray(position_target)),np.squeeze(np.asarray(orientation_target)), 20, 5)
# print('error b')
self.previous_distance = distance
self.state = self.get_state()
self.iteration = 0
self.episodes = 0
return self.state
def reset(self):
# state related property
position_hunter = np.matrix([0.0, 0.0, 10.0]) # x, y, z
orientation_hunter = np.matrix([0.0, 0.0, 0.0]) # roll, pitch, taw
#the position of target is generated randomly and should not exceed the vision range of hunter
position_target = np.matrix([10.0, 0.0, 10.0]) + np.random.normal(
0, 5) # x, y, z
orientation_target = np.matrix([0.0, 0.0, 0.0]) # roll, pitch, yaw
(absolute_x, absolute_y), _ = projection(position_target,
position_hunter,
orientation_hunter,
w=float(self.width),
h=float(self.height))
print('x,y: ', absolute_x, absolute_y)
distance = np.linalg.norm(position_hunter - position_target)
# invalid initialization
while (absolute_x > self.max_absolute_x
or absolute_x < self.min_absolute_x
or absolute_y > self.max_absolute_y
or absolute_y < self.min_absolute_y
or distance > self.max_initial_distance
or distance < self.min_initial_distance):
position_target = np.matrix([10.0, 0.0, 10.0]) + np.random.normal(
0, 5)
(absolute_x, absolute_y), _ = projection(position_target,
position_hunter,
orientation_hunter,
w=float(self.width),
h=float(self.height))
distance = np.linalg.norm(position_hunter - position_target)
dronesim.siminit(np.squeeze(np.asarray(position_hunter)),np.squeeze(np.asarray(orientation_hunter)), \
np.squeeze(np.asarray(position_target)),np.squeeze(np.asarray(orientation_target)), 20, 10)
self.previous_distance = distance
self.state = self.get_state()
self.iteration = 0
self.episodes = 0
return self.state
