A*算法是一种启发式算法，也是路径规划中应用最为普遍的算法之一，相比其他路径规划算法，如遗传算法、蚁群算法等，其算法过程较为简单、易于理解，运行速度快，且应用A*的路径规划结果也不错，是性价比较高的一种路径规划算法[^2]。 有亲测免费的路径规划A*算法Python实现资源，该资源旨在帮助开发者和学习者理解并应用经典的A*（A星）算法来进行高效路径寻找，项目地址为：https://gitcode.com/open-source-toolkit/ea117 [^1]。 以下是一个简单的Python实现A*算法进行路径规划的示例代码： ```python import heapq 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(a, b): return abs(a[0] - b[0]) + abs(a[1] - b[1]) def a_star(grid, start, goal): rows, cols = len(grid), len(grid[0]) open_list = [] closed_set = set() start_node = Node(start[0], start[1], 0, heuristic(start, goal)) heapq.heappush(open_list, start_node) while open_list: current = heapq.heappop(open_list) if (current.x, current.y) == goal: path = [] while current: path.append((current.x, current.y)) current = current.parent return path[::-1] closed_set.add((current.x, current.y)) neighbors = [(current.x + dx, current.y + dy) for dx, dy in [(0, 1), (0, -1), (1, 0), (-1, 0)] if 0 <= current.x + dx < rows and 0 <= current.y + dy < cols and grid[current.x + dx][current.y + dy] == 0] for neighbor in neighbors: if neighbor in closed_set: continue tentative_g = current.g + 1 neighbor_node = Node(neighbor[0], neighbor[1], tentative_g, heuristic(neighbor, goal), current) found = False for i, node in enumerate(open_list): if node.x == neighbor[0] and node.y == neighbor[1]: if tentative_g < node.g: open_list[i] = neighbor_node heapq.heapify(open_list) found = True break if not found: heapq.heappush(open_list, neighbor_node) return None # 示例使用 grid = [ [0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 0, 0], [0, 0, 0, 0] ] start = (0, 0) goal = (3, 3) path = a_star(grid, start, goal) print(path) ```