A*（A星）路径搜索算法属于启发式搜索算法范式。

下面是一个简单的Python代码示例，展示如何使用A*算法搜索最短路径：

class Node:
    def __init__(self, x, y, cost=0, heuristic=0, parent=None):
        self.x = x
        self.y = y
        self.cost = cost
        self.heuristic = heuristic
        self.parent = parent

    def total_cost(self):
        return self.cost + self.heuristic


def heuristic(node, goal):
    return abs(node.x - goal.x) + abs(node.y - goal.y)


def astar_search(start, goal, grid):
    open_list = []
    closed_list = []

    open_list.append(start)

    while open_list:
        current_node = min(open_list, key=lambda node: node.total_cost())

        if current_node == goal:
            path = []
            while current_node:
                path.append((current_node.x, current_node.y))
                current_node = current_node.parent
            return path[::-1]

        open_list.remove(current_node)
        closed_list.append(current_node)

        neighbors = [(current_node.x - 1, current_node.y),
                     (current_node.x + 1, current_node.y),
                     (current_node.x, current_node.y - 1),
                     (current_node.x, current_node.y + 1)]

        for neighbor_x, neighbor_y in neighbors:
            if (neighbor_x < 0 or neighbor_x >= len(grid) or
                    neighbor_y < 0 or neighbor_y >= len(grid[0]) or
                    grid[neighbor_x][neighbor_y] == 1):
                continue

            neighbor = Node(neighbor_x, neighbor_y)
            neighbor.cost = current_node.cost + 1
            neighbor.heuristic = heuristic(neighbor, goal)
            neighbor.parent = current_node

            if neighbor in closed_list:
                continue

            if neighbor in open_list:
                if neighbor.cost < current_node.cost:
                    neighbor.parent = current_node
            else:
                open_list.append(neighbor)

    return None


# 示例使用
grid = [[0, 0, 1, 0, 0],
        [0, 0, 1, 0, 0],
        [0, 0, 0, 0, 0],
        [0, 0, 1, 0, 0],
        [0, 0, 0, 0, 0]]

start = Node(0, 0)
goal = Node(4, 4)

path = astar_search(start, goal, grid)
print(path)

该示例中，Node类表示搜索中的节点，heuristic函数计算节点和目标节点之间的启发式估计值，astar_search函数执行A*搜索算法。在示例中，我们使用一个5x5的网格来表示地图，其中0表示可以通过的路径，1表示障碍物。起始节点为(0, 0)，目标节点为(4, 4)。最后，通过调用astar_search函数，我们得到了最短路径的坐标序列。