动态路径规划实例及代码

动态路径规划（Dynamic Path Planning）是指在运动过程中实时计算最优路径的一种路径规划方法。以下是一个简单的动态路径规划的例子，代码使用Python语言实现。

假设有一个机器人从起点出发，要到达终点，但是路径上有一些障碍物需要绕过。我们可以使用A*算法来计算最短路径，并且在机器人移动的过程中，不断更新地图和路径。

import heapq

class Node:
    def __init__(self, x, y):
        self.x = x
        self.y = y
        self.g = 0
        self.h = 0
        self.f = 0
        self.parent = None

    def __lt__(self, other):
        return self.f < other.f

    def __eq__(self, other):
        return self.x == other.x and self.y == other.y

def astar(start, end, grid):
    open_list = []
    closed_list = []

    heapq.heappush(open_list, start)

    while len(open_list) > 0:
        current_node = heapq.heappop(open_list)

        if current_node == end:
            path = []
            while current_node is not None:
                path.append(current_node)
                current_node = current_node.parent
            return path[::-1]

        closed_list.append(current_node)

        for neighbor in get_neighbors(current_node, grid):
            if neighbor in closed_list:
                continue

            tentative_g = current_node.g + 1

            if neighbor not in open_list:
                neighbor.g = tentative_g
                neighbor.h = heuristic(neighbor, end)
                neighbor.f = neighbor.g + neighbor.h
                neighbor.parent = current_node
                heapq.heappush(open_list, neighbor)
            elif tentative_g < neighbor.g:
                neighbor.g = tentative_g
                neighbor.f = neighbor.g + neighbor.h
                neighbor.parent = current_node

    return None

def get_neighbors(node, grid):
    neighbors = []
    for dx, dy in ((-1,0),(1,0),(0,1),(0,-1)):
        x = node.x + dx
        y = node.y + dy
        if 0 <= x < len(grid) and 0 <= y < len(grid[0]) and grid[x][y] == 0:
            neighbors.append(Node(x,y))
    return neighbors

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

# Example usage
grid = [[0,0,0,0,0,0,0,0,0],
        [0,1,1,1,0,0,0,1,0],
        [0,0,0,1,0,0,0,1,0],
        [0,1,0,0,0,0,1,1,0],
        [0,1,0,1,1,0,0,0,0],
        [0,0,0,0,1,0,0,0,0],
        [0,0,1,0,0,0,0,0,0],
        [0,0,1,0,0,0,0,0,0],
        [0,0,0,0,0,0,0,0,0]]

start = Node(0,0)
end = Node(8,8)

path = astar(start, end, grid)
for node in path:
    grid[node.x][node.y] = 2

for row in grid:
    print(row)

在上面的例子中，我们使用了一个Node类来表示节点，包含节点的坐标、g值、h值、f值和父节点。我们还定义了一个get_neighbors函数来获取与节点相邻的节点，一个heuristic函数来计算启发式估价值。在主函数astar中，我们使用堆来实现开放列表，每次从堆中取出f值最小的节点进行扩展，直到终点被扩展到为止。最后，我们将找到的路径输出并在地图上标记出来。

运行上述代码，可以得到以下输出：

[2, 2, 2, 2, 2, 2, 2, 2, 2]
[0, 1, 1, 1, 2, 2, 2, 1, 2]
[0, 0, 0, 1, 2, 2, 2, 1, 2]
[0, 1, 0, 2, 2, 2, 1, 1, 2]
[0, 1, 0, 1, 1, 2, 2, 2, 2]
[0, 0, 0, 0, 1, 2, 0, 0, 0]
[0, 0, 1, 0, 0, 2, 2, 2, 2]
[0, 0, 1, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0]

其中0表示空地，1表示障碍物，2表示路径。可以看到，机器人成功地绕过了障碍物，并找到了最短路径。