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首页python 实现A*算法的示例代码
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
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python 实现实现A*算法的示例代码算法的示例代码
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)










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