a算法和路径查找a算法是同一种算法。a*算法是一种基于贪心的寻路算法，其核心思想是尽可能地沿着最短路径前进，同时考虑到启发式函数的估值，以达到更加高效的寻路目的。

例如，下面的示例展示了一种简单的a*算法实现：

def heuristic_cost_estimate(start, goal):
    # 计算启发式函数的估值
    return abs(start[0] - goal[0]) + abs(start[1] - goal[1])

def reconstruct_path(came_from, current):
    # 通过反向追溯映射表，重新构建路径
    path = [current]
    while current in came_from:
        current = came_from[current]
        path.append(current)
    return path[::-1]

def a_star(start, goal, neighbor_nodes, distance, heuristic_cost_estimate):
    # 初始化数据结构
    closed_set = set()
    open_set = set([start])
    came_from = {}
    g_score = {start: 0}
    f_score = {start: heuristic_cost_estimate(start, goal)}

    while open_set:
        # 选择当前fscore最小的点，作为下一个扩展的点
        current = min(open_set, key=lambda o: f_score[o])
        # 如果当前点是目标点，那么返回查询过程中的路径
        if current == goal:
            return reconstruct_path(came_from, goal)

        open_set.remove(current)
        closed_set.add(current)

        # 扩展当前点邻居节点
        for neighbor in neighbor_nodes(current):
            if neighbor in closed_set:
                continue

            g = g_score[current] + distance(current, neighbor)
            if neighbor not in open_set:
                open_set.add(neighbor)
            elif g >= g_score[neighbor]:
                continue

            # 更新映射表
            came_from[neighbor] = current
            g_score[neighbor] = g