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path_planning.py
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path_planning.py
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import rclpy
from rclpy.node import Node
from std_msgs.msg import String
from std_msgs.msg import Int32MultiArray, MultiArrayDimension, MultiArrayLayout
import math
# a star
class PathPlanning(Node):
def __init__(self):
"""
ox: x position list of Obstacles
oy: y position list of Obstacles
resolution: grid resolution
rr: robot radiums
"""
super().__init__("path_planning")
self.pos_list_pub = self.create_publisher(Int32MultiArray, '/pos_test', 10)
self.resolution = 1
self.rr = 100
# 3----2
# | |
# 0----1
#之后确定边界的顶点坐标后，应设定这个值, 按逆时针, 必须为长方形且与Gazebo中的经纬度平行
# self.fly_boundary_position = [[-26717208, 514234260], [-26701340, 514212462],
# [-26656878, 514224401], [-26670602, 514246918]]
# self.no_fly_zone = [[-26711717, 514226717], [-26673723, 514235482],
# [-26670013, 514228490], [-26706367, 514219368]]
self.fly_boundary_position = [[-267172, 5142342], [-267013, 5142124],
[-266568, 5142244], [-266706, 5142469]]
self.no_fly_zone = [[-267117, 5142267], [-266737, 5142354],
[-266700, 5142284], [-267063, 5142193]]
# A[-2.6711717, 51.4226717], D[-2.6706367, 51.4219368]
self.ox = []
self.oy = []
self.min_x, self.min_y = 0, 0
self.max_x, self.max_y = 0, 0
self.obstacle_map = None
self.x_width, self.y_width = 0, 0
self.motion = None
self.message = "AStar init"
self.pos_t = [[1, 1], [2, 2]]
self.path_planning_controller_pub = self.create_publisher(String, '/path_planning_controller', 10)
self.path_planning_controller_msg = String()
class PathNode:
def __init__(self, x, y, cost, parent_index):
self.x = x
self.y = y
self.cost = cost
self.parent_index = parent_index
def calc_heuristic(self, node1, node2):
w = 1.0 # weight of heuristic
d = w * math.hypot(node1.x - node2.x, node1.y - node2.y)
return d
def tf_xy_global_position(self, index, min_position):
# tf xy glocal position
pos = index * self.resolution + min_position
return pos
def tf_xy_local_position(self, position, min_pos):
# tf xy local position, e.g. index
return (position - min_pos) / self.resolution
def tf_dict_index(self, node):
# dict index: tf 2-D to 1-D dict
return (int)(node.y - self.min_y) * self.x_width + (node.x - self.min_x)
def verify_node(self, node):
px = self.tf_xy_global_position(node.x, self.min_x)
py = self.tf_xy_global_position(node.y, self.min_y)
if px < self.min_x:
return False
elif py < self.min_y:
return False
elif px >= self.max_x:
return False
elif py >= self.max_y:
return False
if self.obstacle_map[(int)(node.x)][(int)(node.y)]:
return False
return True
def calc_final_path(self, goal_node, closed_set):
rx = [self.tf_xy_global_position(goal_node.x, self.min_x)]
ry = [self.tf_xy_global_position(goal_node.y, self.min_y)]
parent_index = goal_node.parent_index
while parent_index != -1:
n = closed_set[parent_index]
rx.append(self.tf_xy_global_position(n.x, self.min_x))
ry.append(self.tf_xy_global_position(n.y, self.min_y))
parent_index = n.parent_index
return rx, ry
def planning(self, sx, sy, gx, gy):
"""
input:
sx: start x position
sy: start y position
gx: goal x position
gy: goal y position
output:
rx: x position list of the fianl path
ry: y position list of the final path
"""
start_node = self.PathNode(self.tf_xy_local_position(sx, self.min_x),
self.tf_xy_local_position(sy, self.min_y), 0.0, -1)
goal_node = self.PathNode(self.tf_xy_local_position(gx, self.min_x),
self.tf_xy_local_position(gy, self.min_y), 0.0, -1)
open_set, closed_set = dict(), dict()
open_set[self.tf_dict_index(start_node)] = start_node
while True:
if len(open_set) == 0:
self.message = "Open set is empty."
break
# get the min value's index in open_set
c_id = min(open_set,
key=lambda o: open_set[o].cost + self.calc_heuristic(goal_node, open_set[o]))
current = open_set[c_id]
if current.x == goal_node.x and current.y == goal_node.y:
self.message = "Find goal"
goal_node.parent_index = current.parent_index
goal_node.cost = current.cost
break
del open_set[c_id]
closed_set[c_id] = current
if current.x > -266737:
self.motion = self.get_motion_model(2)
else:
self.motion = self.get_motion_model(1)
# expend the grid search based on motion model
for i, _ in enumerate(self.motion):
node = self.PathNode(current.x + self.motion[i][0], current.y + self.motion[i][1],
current.cost + self.motion[i][2], c_id)
n_id = self.tf_dict_index(node)
# if the node is not safe, do nothing
if not self.verify_node(node):
continue
if n_id in closed_set:
continue
if n_id not in open_set:
open_set[n_id] = node
else:
if open_set[n_id].cost > node.cost:
open_set[n_id] = node
rx, ry = self.calc_final_path(goal_node, closed_set)
return rx, ry
def calc_obstacle_map(self):
self.min_x = self.fly_boundary_position[0][0]
self.min_y = self.fly_boundary_position[1][1]
self.max_x = self.fly_boundary_position[2][0]
self.max_y = self.fly_boundary_position[3][1]
self.x_width = self.max_x - self.min_x
self.y_width = self.max_y - self.min_y
self.obstacle_map = [[True for _ in range(self.y_width)] for _ in range(self.x_width)]
obstacle_para = 1 # 1 = 1 m 1*1.414 = 1.41
for ix in range(self.x_width):
x = self.tf_xy_global_position(ix, self.min_x)
flight_ab = round((-137107 * x + 477603048596) / 100000) + obstacle_para
flight_bc = round(( 26966 * x + 521412672558) / 100000) + obstacle_para
flight_cd = round((-163043 * x + 470762353576) / 100000) - obstacle_para
flight_da = round(( 27253 * x + 521515438516) / 100000) - obstacle_para
no_fly_ab = round(( 16667 * x + 518681105735) / 100000) + obstacle_para
no_fly_bc = round((-189189 * x + 463771693707) / 100000) + obstacle_para
no_fly_cd = round(( 25000 * x + 520895900000) / 100000) - obstacle_para
for iy in range(self.y_width):
y = self.tf_xy_global_position(iy, self.min_y)
if x < self.no_fly_zone[0][0]:
if y > flight_ab and y < flight_da:
self.obstacle_map[ix][iy] = False
elif x < self.no_fly_zone[3][0]:
if y > no_fly_ab and y < flight_da:
self.obstacle_map[ix][iy] = False
elif x < self.fly_boundary_position[1][0]:
if y > flight_ab and y < no_fly_cd:
self.obstacle_map[ix][iy] = False
elif y > no_fly_ab and y < flight_da:
self.obstacle_map[ix][iy] = False
elif x < self.no_fly_zone[1][0]:
if y > flight_bc and y < no_fly_cd:
self.obstacle_map[ix][iy] = False
elif y > no_fly_ab and y < flight_da:
self.obstacle_map[ix][iy] = False
# elif x < self.no_fly_zone[2][0]:
# if y > flight_bc and y < no_fly_cd:
# self.obstacle_map[ix][iy] = False
# elif y > no_fly_bc and y < flight_da:
# self.obstacle_map[ix][iy] = False
elif x < self.no_fly_zone[2][0] + 20:
if y > flight_bc and y < (self.no_fly_zone[2][1] - 10):
self.obstacle_map[ix][iy] = False
elif y > (self.no_fly_zone[1][1] + 10) and y < flight_da:
self.obstacle_map[ix][iy] = False
elif x < self.fly_boundary_position[3][0]:
if y > flight_bc and y < flight_da:
self.obstacle_map[ix][iy] = False
elif x < self.fly_boundary_position[2][0]:
if y > flight_bc and y < flight_cd:
self.obstacle_map[ix][iy] = False
# self.min_x = (int)(min(ox))
# self.min_y = (int)(min(oy))
# self.max_x = (int)(max(ox))
# self.max_y = (int)(max(oy))
# self.x_width = (self.max_x - self.min_x) / self.resolution
# self.y_width = (self.max_y - self.min_y) / self.resolution
# self.obstacle_map = [[False for _ in range(self.y_width)] for _ in range(self.x_width)]
# for ix in range(self.x_width):
# x = self.tf_xy_global_position(ix, self.min_x)
# for iy in range(self.y_width):
# y = self.tf_xy_global_position(iy, self.min_y)
# for iox, ioy in zip(ox, oy):
# d = math.hypot(iox - x, ioy - y)
# if d <= self.rr:
# self.obstacle_map[ix][iy] = True
# break
def get_motion_model(self, state):
# x, y, cost
# motion = [[1, 0, 1], [0, 1, 1], [-1, 0, 1], [0, -1, 1],
# [-1, -1, math.sqrt(2)], [-1, 1, math.sqrt(2)],
# [1, -1, math.sqrt(2)], [1, 1, math.sqrt(2)]]
# motion = [[1, 0, 2], [0, 1, 1], [-1, 0, 2], [0, -1, 1],
# [-1, -1, math.sqrt(5)], [-1, 1, math.sqrt(5)],
# [1, -1, math.sqrt(5)], [1, 1, math.sqrt(5)]]
if state == 1:
motion = [[1, 0, 4], [0, 1, 3], [-1, 0, 4], [0, -1, 3],
[-1, -1, 5], [-1, 1, 5],
[1, -1, 5], [1, 1, 5]]
else:
motion = [[1, 0, 1], [0, 1, 1], [-1, 0, 1], [0, -1, 1],
[-1, -1, math.sqrt(2)], [-1, 1, math.sqrt(2)],
[1, -1, math.sqrt(2)], [1, 1, math.sqrt(2)]]
return motion
# 假设禁飞区的顶点坐标为no_fly_zone, 逆时针
# 设定飞行边界fly_boundary为顶点坐标
def obstacle_map_init(self, fly_boundary, no_fly_zone):
ox, oy = [], []
# set fly boundary
#fly boundary bc
for i in range(fly_boundary[1][0], fly_boundary[0][0]+1):
ox.append(i)
oy.append(round((-26852 * i * 100000 + 52138230581680) / 1000000000000, 7))
#fly boundary cd
for i in range(fly_boundary[1][0], fly_boundary[2][0]+1):
ox.append(i)
oy.append(round((164070 * i * 100000 + 47048846126540) / 1000000000000, 7))
#fly boundary da
for i in range(fly_boundary[2][0], fly_boundary[3][0]+1):
ox.append(i)
oy.append(round((-27160 * i * 100000 + 52149065369280) / 1000000000000, 7))
#fly boundary ab
for i in range(fly_boundary[0][0], fly_boundary[3][0]+1):
ox.append(i)
oy.append(round((137371 * i * 100000 + 47753256419832) / 1000000000000, 7))
# set no fly zone
#no fly zone dc
for i in range(no_fly_zone[1][0], no_fly_zone[0][0]+1):
ox.append(i)
oy.append(round((-25089 * i * 100000 + 52091972956157) / 1000000000000, 7))
#no fly zone bc
for i in range(no_fly_zone[1][0], no_fly_zone[2][0]+1):
ox.append(i)
oy.append(round((188464 * i * 100000 + 46396511668528) / 1000000000000, 7))
#no fly zone ab
for i in range(no_fly_zone[2][0], no_fly_zone[3][0]+1):
ox.append(i)
oy.append(round((-23068 * i * 100000 + 52038857631596) / 1000000000000, 7))
return ox, oy
def start(self):
pos = Int32MultiArray()
pos_layout = MultiArrayLayout()
pos_dim = MultiArrayDimension()
pos.data = [v for nested in self.pos_t for v in nested]
pos_dim.label = 'pos'
pos_dim.size = len(self.pos_t)
pos_dim.stride = len(pos.data)
pos_layout.dim = [pos_dim]
pos_layout.data_offset = 0
pos.layout = pos_layout
controller_path_planning_sub = self.create_subscription(String, '/controller_path_planning', self.controller_path_planning_msg_callback,10)
# self.ox, self.oy = self.obstacle_map_init(self.fly_boundary_position, self.no_fly_zone)
self.calc_obstacle_map()
rx, ry = self.planning(-267155, 5142341, -266877, 5142192)
if len(rx) == len(ry):
current_pos = [-2.67155, 51.42341]
for i in range(len(rx) - 1 , -1, -1):
position_x = rx[i] / 100000
position_y = ry[i] / 100000
# d_x_no_fly_b = position_x - (-2.66737)
# d_y_no_fly_b = position_y - 51.42355
# d_x_no_fly_c = position_x - (-2.667)
# d_y_no_fly_c = position_y - 51.42285
# if (abs(d_x_no_fly_b) < 0.0001) and (abs(d_y_no_fly_b) < 0.0001):
# current_pos = [position_x, position_y]
# # position_xy = str(position_x) + "," + str(position_y)
# position_xy = '{},{}'.format(position_x, position_y)
# self.path_planning_controller_msg.data = position_xy
# self.get_logger().info('x={}, y={}'.format(position_x, position_y))
# self.path_planning_controller_pub.publish(self.path_planning_controller_msg)
# continue
# if (abs(d_x_no_fly_c) < 0.0001) and (abs(d_y_no_fly_c) < 0.0001):
# current_pos = [position_x, position_y]
# # position_xy = str(position_x) + "," + str(position_y)
# position_xy = '{},{}'.format(position_x, position_y)
# self.path_planning_controller_msg.data = position_xy
# self.get_logger().info('x={}, y={}'.format(position_x, position_y))
# self.path_planning_controller_pub.publish(self.path_planning_controller_msg)
# continue
if math.sqrt((current_pos[0] - position_x)**2 + (current_pos[1] - position_y)**2) > 0.0006:
current_pos = [position_x, position_y]
# position_xy = str(position_x) + "," + str(position_y)
position_xy = '{},{}'.format(position_x, position_y)
self.path_planning_controller_msg.data = position_xy
self.get_logger().info('x={}, y={}'.format(position_x, position_y))
self.path_planning_controller_pub.publish(self.path_planning_controller_msg)
# self.pos_list_pub.publish(pos)
# self.path_planning_controller_msg.data = 'pos list finished'
# self.path_planning_controller_pub.publish(self.path_planning_controller_msg)
def controller_path_planning_msg_callback(self, msg):
controller_path_planning_msg = msg.data.split(",")
if controller_path_planning_msg[0] == 'return_home':
cur_lon = round(float(controller_path_planning_msg[2]) * 100000)
cur_lat = round(float(controller_path_planning_msg[1]) * 100000)
self.path_planning_home(cur_lon, cur_lat)
self.path_planning_controller_msg.data = 'return home path planning finished'
self.path_planning_controller_pub.publish(self.path_planning_controller_msg)
def path_planning_home(self, cur_lon, cur_lat):
rx, ry = self.planning(cur_lon, cur_lat, -267155, 5142341)
if len(rx) == len(ry):
cur_lon = cur_lon / 100000
cur_lat = cur_lat / 100000
current_pos = [cur_lon, cur_lat]
for i in range(len(rx) - 1 , -1, -1):
position_x = rx[i] / 100000
position_y = ry[i] / 100000
if math.sqrt((current_pos[0] - position_x)**2 + (current_pos[1] - position_y)**2) > 0.0006:
current_pos = [position_x, position_y]
# position_xy = str(position_x) + "," + str(position_y)
position_xy = '{},{}'.format(position_x, position_y)
self.path_planning_controller_msg.data = position_xy
self.get_logger().info('x={}, y={}'.format(position_x, position_y))
self.path_planning_controller_pub.publish(self.path_planning_controller_msg)
def main(args=None):
rclpy.init(args=args)
pathplanning_node = PathPlanning()
pathplanning_node.start()
rclpy.spin(pathplanning_node)
if __name__ == '__main__':
main()
# 用法
# a_star = AStar(ox, oy, resolution, rr)
# rx, ry = a_star.planning(sx, sy, gx, gy)