### 基于 A* 算法的 AGV 路径规划 Python 实现 A* 算法是一种高效的启发式搜索算法，特别适合解决带有权重的地图上的最短路径问题。在 AGV 路径规划中，可以通过构建栅格地图来模拟仓库环境，并利用 A* 算法找到从起点到终点的最优路径[^5]。 以下是完整的 Python 实现代码： ```python import heapq class Node: def __init__(self, position, g_cost=float('inf'), h_cost=0, parent=None): self.position = position # (row, col) self.g_cost = g_cost # Cost from start to this node self.h_cost = h_cost # Heuristic cost from this node to end self.parent = parent # Parent node in the path @property def f_cost(self): return self.g_cost + self.h_cost def __lt__(self, other): return self.f_cost < other.f_cost def heuristic(node_position, goal_position): """Manhattan distance as heuristic.""" dx = abs(node_position[0] - goal_position[0]) dy = abs(node_position[1] - goal_position[1]) return dx + dy # Manhattan Distance def a_star(grid, start, goal): open_list = [] closed_set = set() start_node = Node(position=start, g_cost=0, h_cost=heuristic(start, goal)) goal_node = Node(position=goal) heapq.heappush(open_list, start_node) while open_list: current_node = heapq.heappop(open_list) if current_node.position == goal_node.position: path = [] while current_node is not None: path.append(current_node.position) current_node = current_node.parent return path[::-1] closed_set.add(current_node.position) neighbors = [(current_node.position[0]+dx, current_node.position[1]+dy) for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1)]] for neighbor_pos in neighbors: row, col = neighbor_pos if 0 <= row < len(grid) and 0 <= col < len(grid[0]) and grid[row][col] != 1: tentative_g_cost = current_node.g_cost + 1 # Assuming uniform movement cost if neighbor_pos in closed_set: continue neighbor_node = Node( position=neighbor_pos, g_cost=tentative_g_cost, h_cost=heuristic(neighbor_pos, goal), parent=current_node ) if any(n for n in open_list if n.position == neighbor_pos and n.g_cost <= tentative_g_cost): continue heapq.heappush(open_list, neighbor_node) return None # Example usage if __name__ == "__main__": grid_map = [ [0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0] ] start_point = (0, 0) goal_point = (4, 4) optimal_path = a_star(grid_map, start_point, goal_point) print("Optimal Path:", optimal_path) ``` 上述代码定义了一个 `Node` 类用来存储节点的相关信息，包括位置、代价以及父节点指针。通过曼哈顿距离作为启发函数计算估计成本 \(h\)，并通过最小堆维护开放列表以提高效率[^6]。 --- ### 解决思路解析 - **启发函数的选择**：在此实现中选择了简单的曼哈顿距离作为启发函数，因为它易于计算且通常能提供良好的近似值。 - **障碍物检测**：通过检查邻居节点是否超出边界或者对应网格单元是否有障碍物（标记为 `1`），可以有效避开不可通行区域。 - **路径回溯机制**：一旦到达目标节点，则沿着记录下来的父节点链表逆向追踪直至起始点形成最终路径[^7]。 --- ###