Python实现A*算法

下面是Python实现A*算法的示例代码：

import heapq

def heuristic(a, b):
    # 计算两点之间的曼哈顿距离
    return abs(a[0] - b[0]) + abs(a[1] - b[1])

def astar(array, start, goal):
    neighbors = [(0,1),(0,-1),(1,0),(-1,0),(1,1),(1,-1),(-1,1),(-1,-1)]

    close_set = set()
    came_from = {}
    gscore = {start:0}
    fscore = {start:heuristic(start, goal)}
    oheap = []

    heapq.heappush(oheap, (fscore[start], start))

    while oheap:

        current = heapq.heappop(oheap)[1]

        if current == goal:
            data = []
            while current in came_from:
                data.append(current)
                current = came_from[current]
            return data[::-1]

        close_set.add(current)
        for i, j in neighbors:
            neighbor = current[0] + i, current[1] + j
            tentative_g_score = gscore[current] + heuristic(current, neighbor)
            if 0 <= neighbor[0] < array.shape[0]:
                if 0 <= neighbor[1] < array.shape[1]:
                    if array[neighbor[0]][neighbor[1]] == 1:
                        continue
                else:
                    # 超出边界
                    continue
            else:
                # 超出边界
                continue

            if neighbor in close_set and tentative_g_score >= gscore.get(neighbor, 0):
                continue

            if tentative_g_score < gscore.get(neighbor, 0) or neighbor not in [i[1] for i in oheap]:
                came_from[neighbor] = current
                gscore[neighbor] = tentative_g_score
                fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal)
                heapq.heappush(oheap, (fscore[neighbor], neighbor))

    return False

在这个示例中，我们使用了一个二维数组表示地图，1表示障碍物，0表示可以通过。起始点和目标点分别用元组表示。`heuristic`函数计算两点之间的曼哈顿距离，`astar`函数实现了A*算法的核心部分。它使用堆来搜寻下一个搜索的节点，并计算每个节点到终点的距离，然后选择最短的路径。如果找到了路径，则返回路径，否则返回False。