改进A*算法的路径规划代码
直接上代码！先看传统A*算法的核心实现：
```python
def astar(start, goal):
open_set = PriorityQueue()
open_set.put(start)
came_from = {}
g_score = {node: float('inf') for node in graph}
g_score[start] = 0
while not open_set.empty():
current = open_set.get()
if current == goal:
return reconstruct_path(came_from, current)
for neighbor in get_neighbors(current):
tentative_g = g_score[current] + distance(current, neighbor)
if tentative_g < g_score[neighbor]:
came_from[neighbor] = current
g_score[neighbor] = tentative_g
f_score = tentative_g + heuristic(neighbor, goal)
if neighbor not in open_set:
open_set.put(neighbor)
return None
```
这段代码的问题在于启发函数`heuristic`如果设计不好，容易导致搜索节点爆炸。有个骚操作是
把欧式距离改成带权重的混合启发式：
```python
def heuristic(a, b):
dx = abs(a.x - b.x)
