A星算法的多机器人路径规划的python代码

A星算法是一种用于解决路径规划问题的算法，它可以用于单机器人和多机器人的情况。下面是一段使用 Python 实现多机器人路径规划的代码示例：

``` import heapq import numpy as np

def a_star(grid, start, goal, robot_starts, robot_goals): def heuristic(position, goal): # 计算启发函数(曼哈顿距离) return np.abs(position[0] - goal[0]) + np.abs(position[1] - goal[1])

def get_neighbors(position, grid):
    # 获取当前位置的邻居
    row, col = position
    neighbors = []
    if row > 0 and grid[row-1, col] != 1:
        neighbors.append((row-1, col))
    if row < grid.shape[0]-1 and grid[row+1, col] != 1:
        neighbors.append((row+1, col))
    if col > 0 and grid[row, col-1] != 1:
        neighbors.append((row, col-1))
    if col < grid.shape[1]-1 and grid[row, col+1] != 1:
        neighbors.append((row, col+1))
    return neighbors
 
def reconstruct_path(came_from, current):
    # 通过 came_from 数组重建路径
    path = [current]
    while current in came_from:
        current = came_from[current]
        path.append(current)
    path.reverse()
    return path
 
robot_num = len(robot_starts)
closed_set = [set() for i in range(robot_num)]
came_from = [{} for i in range(robot_num)]
g_score = [{robot_starts[i]: 0} for i in range(robot_num)]
f_score = [{robot_starts[i]: heuristic(robot_starts[i], goal)} for i in range(robot_num)]
heap = [(f_score[i][robot_starts[i]], i, robot_starts[i]) for i in range(robot_num)]
heapq.heapify(heap)
 
while heap:
    f, robot_idx, current = heapq.heappop(heap)
    if current == goal:
        return [reconstruct_path(came_from[i], current) for i in range(robot_num)]
 
    closed_set[robot_idx].add(current)
   
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