人工智能-A*算法与卷积神经网络（CNN）结合实现路径规划

以下是一个将 A* 算法与卷积神经网络（CNN）结合实现路径规划的代码示例。主要思路是使用 A* 算法生成训练数据，然后用这些数据训练一个 CNN 模型，让 CNN 学习如何预测路径，最后使用训练好的 CNN 模型进行路径规划。

代码实现

import numpy as np
import heapq
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

# A* 算法实现
class Node:
    def __init__(self, x, y, g=float('inf'), h=float('inf'), parent=None):
        self.x = x
        self.y = y
        self.g = g
        self.h = h
        self.f = g + h
        self.parent = parent

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

def heuristic(current, goal):
    return abs(current[0] - goal[0]) + abs(current[1] - goal[1])

def astar(grid, start, goal):
    rows, cols = grid.shape
    open_list = []
    closed_set = set()

    start_node = Node(start[0], start[1], g=0, h=heuristic(start, goal))
    heapq.heappush(open_list, start_node)

    while open_list:
        current_node = heapq.heappop(open_list)

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

        closed_set.add((current_node.x, current_node.y