用Python编写代码在此代码下利用Astar算法寻路，其中p为随机生成障碍物的概率：class randomMAP: def init(self, size, start, goal, p): self.size = size self.start = start self.goal = goal self.p = p def creatmap(self): self.map =np.zeros(self.size, dtype='int') for i in range(self.map.shape[0]): for j in range(self.map.shape[1]): if (i != self.start[0] or j != self.start[1]) and (i != self.goal[0] or j != self.goal[1]) and random.random() < self.p: self.map[i][j] = 5 map1 = randomMAP((20, 20), (0, 0), (19, 19), 0.3) map1.creatmap() print(map1.map) plt.matshow(map1.map) plt.show()

这段代码创建了一个随机地图，其中包含随机生成的障碍物。使用A*算法寻路需要先定义节点类，然后实现A*算法的具体逻辑。下面是一个简单的实现，可以根据自己的需求进行修改：

import numpy as np
import random
import heapq
import matplotlib.pyplot as plt

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

class AStar:
    def __init__(self, start, goal, map):
        self.start = start
        self.goal = goal
        self.map = map

    def heuristic(self, a, b):
        # 曼哈顿距离作为启发函数
        return abs(a.x - b.x) + abs(a.y - b.y)

    def get_neighbors(self, node):
        neighbors = []
        for i in range(-1, 2):
            for j in range(-1, 2):
                if i == 0 and j == 0:
                    continue
                x = node.x + i
                y = node.y + j
                if x < 0 or x >= self.map.shape[0] or y < 0 or y >= self.map.shape[1]:
                    continue
                if self.map[x][y] == 5:
                    continue
                neighbors.append(Node(x, y))
        return neighbors

    def search(self):
        open_list = []
        closed_list = []
        start_node = Node(self.start[0], self.start[1])
        goal_node = Node(self.goal[0], self.goal[1])
        heapq.heappush(open_list, start_node)

        while len(open_list) > 0:
            current_node = heapq.heappop(open_list)
            if current_node.x == goal_node.x and current_node.y == goal_node.y:
                path = []
                while current_node is not None:
                    path.append((current_node.x, current_node.y))
                    current_node = current_node.parent
                return path[::-1]
            closed_list.append(current_node)

            for neighbor in self.get_neighbors(current_node):
                if neighbor in closed_list:
                    continue
                neighbor.g = current_node.g + 1
                neighbor.h = self.heuristic(neighbor, goal_node)
                neighbor.f = neighbor.g + neighbor.h
                neighbor.parent = current_node

                if neighbor not in open_list:
                    heapq.heappush(open_list, neighbor)

        return None

class randomMAP:
    def __init__(self, size, start, goal, p):
        self.size = size
        self.start = start
        self.goal = goal
        self.p = p

    def creatmap(self):
        self.map = np.zeros(self.size, dtype='int')
        for i in range(self.map.shape[0]):
            for j in range(self.map.shape[1]):
                if (i != self.start[0] or j != self.start[1]) and (i != self.goal[0] or j != self.goal[1]) and random.random() < self.p:
                    self.map[i][j] = 5

map1 = randomMAP((20, 20), (0, 0), (19, 19), 0.3)
map1.creatmap()
print(map1.map)

astar = AStar(map1.start, map1.goal, map1.map)
path = astar.search()
if path is None:
    print("No path found")
else:
    print(path)

plt.matshow(map1.map)
if path is not None:
    path_x = [x for (x, y) in path]
    path_y = [y for (x, y) in path]
    plt.plot(path_y, path_x, 'r')
plt.show()

这个实现中，为了方便起见，启发函数使用的是曼哈顿距离。如果需要更高的精度，可以使用欧几里得距离或其他启发函数。