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python 实现A*算法的示例代码

2018年08月18日  | 移动技术网IT编程  | 我要评论

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a*作为最常用的路径搜索算法，值得我们去深刻的研究。路径规划项目。先看一下维基百科给的算法解释：https://en.wikipedia.org/wiki/a*_search_algorithm

a *是最佳优先搜索它通过在解决方案的所有可能路径（目标）中搜索导致成本最小（行进距离最短，时间最短等）的问题来解决问题。 ），并且在这些路径中，它首先考虑那些似乎最快速地引导到解决方案的路径。它是根据加权图制定的：从图的特定节点开始，它构造从该节点开始的路径树，一次一步地扩展路径，直到其一个路径在预定目标节点处结束。

在其主循环的每次迭代中，a *需要确定将其部分路径中的哪些扩展为一个或多个更长的路径。它是基于成本（总重量）的估计仍然到达目标节点。具体而言，a *选择最小化的路径

f（n）= g（n）+ h（n）

其中n是路径上的最后一个节点，g（n）是从起始节点到n的路径的开销，h（n）是一个启发式，用于估计从n到目标的最便宜路径的开销。启发式是特定于问题的。为了找到实际最短路径的算法，启发函数必须是可接受的，这意味着它永远不会高估实际成本到达最近的目标节点。

维基百科给出的伪代码：

function a*(start, goal)
  // the set of nodes already evaluated
  closedset := {}

  // the set of currently discovered nodes that are not evaluated yet.
  // initially, only the start node is known.
  openset := {start}

  // for each node, which node it can most efficiently be reached from.
  // if a node can be reached from many nodes, camefrom will eventually contain the
  // most efficient previous step.
  camefrom := an empty map

  // for each node, the cost of getting from the start node to that node.
  gscore := map with default value of infinity

  // the cost of going from start to start is zero.
  gscore[start] := 0

  // for each node, the total cost of getting from the start node to the goal
  // by passing by that node. that value is partly known, partly heuristic.
  fscore := map with default value of infinity

  // for the first node, that value is completely heuristic.
  fscore[start] := heuristic_cost_estimate(start, goal)

  while openset is not empty
    current := the node in openset having the lowest fscore[] value
    if current = goal
      return reconstruct_path(camefrom, current)

    openset.remove(current)
    closedset.add(current)

    for each neighbor of current
      if neighbor in closedset
        continue // ignore the neighbor which is already evaluated.

      if neighbor not in openset // discover a new node
        openset.add(neighbor)
      
      // the distance from start to a neighbor
      //the "dist_between" function may vary as per the solution requirements.
      tentative_gscore := gscore[current] + dist_between(current, neighbor)
      if tentative_gscore >= gscore[neighbor]
        continue // this is not a better path.

      // this path is the best until now. record it!
      camefrom[neighbor] := current
      gscore[neighbor] := tentative_gscore
      fscore[neighbor] := gscore[neighbor] + heuristic_cost_estimate(neighbor, goal) 

  return failure

function reconstruct_path(camefrom, current)
  total_path := {current}
  while current in camefrom.keys:
    current := camefrom[current]
    total_path.append(current)
  return total_path

下面是udacity课程中路径规划项目，结合上面的伪代码，用python 实现a* 

import math
def shortest_path(m,start,goal):
  sx=m.intersections[start][0]
  sy=m.intersections[start][1]
  gx=m.intersections[goal][0]
  gy=m.intersections[goal][1] 
  h=math.sqrt((sx-gx)*(sx-gx)+(sy-gy)*(sy-gy))
  closedset=set()
  openset=set()
  openset.add(start)
  gscore={}
  gscore[start]=0
  fscore={}
  fscore[start]=h
  camefrom={}
  sumg=0
  new=0
  bool=false
  while len(openset)!=0: 
    max=1000
    for new in openset:
      print("new",new)
      if fscore[new]<max:
        max=fscore[new]
        #print("max=",max)
        new=new
    current=new
    print("current=",current)
    if current==goal:
      return reconstruct_path(camefrom,current)
    openset.remove(current)
    closedset.add(current)
    #dafult=m.roads(current)
    for neighbor in m.roads[current]:
      bool=false
      print("key=",neighbor)
      a={neighbor}
      if len(a&closedset)>0:
        continue
      print("key is not in closeset")
      if len(a&openset)==0:
        openset.add(neighbor)  
      else:
        bool=true
      x= m.intersections[current][0]
      y= m.intersections[current][1]
      x1=m.intersections[neighbor][0]
      y1=m.intersections[neighbor][1]
      g=math.sqrt((x-x1)*(x-x1)+(y-y1)*(y-y1))
      h=math.sqrt((x1-gx)*(x1-gx)+(y1-gy)*(y1-gy)) 
      
      new_gscore=gscore[current]+g
      if bool==true:
        if new_gscore>=gscore[neighbor]:
          continue
      print("new_gscore",new_gscore) 
      camefrom[neighbor]=current
      gscore[neighbor]=new_gscore     
      fscore[neighbor] = new_gscore+h
      print("fscore",neighbor,"is",new_gscore+h)
      print("fscore=",new_gscore+h)
      
    print("__________++--------------++_________")
                   
def reconstruct_path(camefrom,current):
  print("已到达lllll")
  total_path=[]
  total_path.append(current)
  for key,value in camefrom.items():
    print("key",key,":","value",value)
    
  while current in camefrom.keys():
    
    current=camefrom[current]
    total_path.append(current)
  total_path=list(reversed(total_path))  
  return total_path

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