A-Star算法探索和实现（一）

怼烎@ 2024-03-29 14:30 62阅读 0赞

**提醒：A-Star算法探索和实现系列博文不具备参考价值，仅仅作为个人探索和尝试的记录！**

##### **什么是A-Star算法？** #####

A\*搜寻算法俗称A星算法，又叫A-Star算法。A\*算法是比较流行的[启发式搜索算法][Link 1]之一，被广泛应用于路径优化领域（引用自百度百科）。在游戏开发中，我们可以将A\*算法作为一种敌人的寻路算法，在伊庭齐志所著的《AI游戏开发和深度学习进阶》中有关于A\*算法在各种游戏中的运用。如果对A-Star算法不是很了解，推荐浏览这两篇博文，一篇是英文原著，一篇是针对该英文原著的汉译版本。

[英文原著][Link 2] [英文原著汉译版本][Link 3]

##### **A-Star算法的思路** #####

A\*算法要求寻找到从起始点到终点的最佳路径，从起始点开始，首先以起始点作为当前点，开始收集当前点下一步可前往的点，保存为一个点集并对这些点的权值进行计算，然后以权值最小的点作为下一步要前往的点，将该点设为当前点再重复上述过程，直至到达终点。

##### 示例代码UML图 #####

![189729a94d1e9c15d8c27754016d8816.jpeg][]

##### 代码示例（Python） #####

Grid.py

import matplotlib.pyplot as pp
    import numpy as np
    from matplotlib.pyplot import MultipleLocator
    from myCodes import SharedData as sd
    from myCodes import Node as node
    
    
    # 视图的构建和绘制
    class Grid:
        # 横纵坐标轴的刻度标记
        __xLabels = []
        __yLabels = []
        # 网格单元格的信息
        __data = None
        # 是否完成初始化
        __isInit = False
    
        def __init__(self, p_rowsCount, p_colsCount):
            if self.__isInit is False:
                # 网格的行列数
                self.__rowsCount = p_rowsCount
                self.__colsCount = p_colsCount
                # Node工厂
                self.__nodeFactory = node.NodeFactory()
                # 将网格的行列数设置为共享信息
                sd.SharedData.rowsCount = self.__rowsCount
                sd.SharedData.colsCount = self.__colsCount
                # 将终点Node设置为共享信息
                v_endNode = self.__nodeFactory.GetEndNode(self.__rowsCount, self.__colsCount)
                sd.SharedData.endNode = v_endNode
                self.__isInit = True
    
        # 绘制图形
        def Draw(self):
            # 获取坐标轴实例
            v_fig, v_ax = pp.subplots()
            self._AxisSet(v_ax)
            self._GridValueSet(v_ax)
            # 将数据以二维图片的形式进行显示
            v_ax.imshow(self.__data, cmap='Accent', vmin=0, vmax=255)
            # 将网格和刻度线显示在多数artists上方
            v_ax.set_axisbelow(False)
            # 标记起始点和终点
            self._StartPointSet(self.__nodeFactory.GetStartNode())
            self._PathNodeSet()
            self._EndPointSet(sd.SharedData.endNode)
            # 布置网格线
            pp.grid(visible=True, color='w')
            # 采用紧凑型布局
            v_fig.tight_layout()
            pp.show()
    
        # 标记起始点
        def _StartPointSet(self, p_node: node.Node):
            self._Mark(p_node, 30, 'yo')
    
        # 标记终点
        def _EndPointSet(self, p_node: node.Node):
            self._Mark(p_node, 30, 'ro')
    
        # 标记所查找到的路径
        def _PathNodeSet(self):
            v_pathNodes = sd.SharedData.pathNodes
            v_length = len(v_pathNodes)
            for i in range(v_length):
                if 0 < i < v_length - 1:
                    self._Mark(v_pathNodes[i], 10, 'bo')
                    self._Arrow(v_pathNodes[i - 1], v_pathNodes[i])
                elif i == v_length - 1:
                    self._Arrow(v_pathNodes[i - 1], v_pathNodes[i])
    
        # 标记方法
        def _Mark(self, p_node: node.Node, p_marksize: int, p_fmt: str):
            v_x = p_node.nodePos.x
            v_y = p_node.nodePos.y
            pp.plot(v_x, v_y, p_fmt, markersize=p_marksize, zorder=1)
    
        # 箭头指向方法
        def _Arrow(self, p_firstNode, p_secondNode):
            v_dx = p_secondNode.nodePos.x - p_firstNode.nodePos.x
            v_dy = p_secondNode.nodePos.y - p_firstNode.nodePos.y
            pp.arrow(p_firstNode.nodePos.x, p_firstNode.nodePos.y, v_dx, v_dy, color='orange', width=0.01, head_width=0.08,
                     zorder=3)
    
        # 坐标轴设置
        def _AxisSet(self, p_ax):
            v_ax = p_ax
            for i in range(1, self.__rowsCount + 1):
                self.__xLabels.append(str(i))
            for i in range(1, self.__colsCount + 1):
                self.__yLabels.append(str(i))
            # 设置横坐标轴为bottom，纵坐标轴为left
            v_ax.xaxis.set_ticks_position('bottom')
            v_ax.yaxis.set_ticks_position('left')
            # 隐藏边框
            v_ax.spines['bottom'].set(visible=False)
            v_ax.spines['right'].set(visible=False)
            v_ax.spines['top'].set(visible=False)
            v_ax.spines['left'].set(visible=False)
            # 隐藏刻度线
            v_ax.tick_params(left=False, bottom=False, top=False, right=False)
            # 坐标轴的刻度间隔实例
            v_xMinorLocator = MultipleLocator(1)
            v_yMinorLocator = MultipleLocator(1)
            v_low = 1
            if self.__rowsCount > self.__colsCount:
                v_high = self.__rowsCount
            else:
                v_high = self.__colsCount
            self.__data = np.random.randint(v_low, v_high, size=(self.__rowsCount + 1, self.__colsCount + 1))
            # 设置横纵坐标轴的范围
            pp.xlim(1, self.__rowsCount)
            pp.ylim(1, self.__colsCount)
            # 设置坐标轴的刻度标记
            v_ax.set_xticks(np.arange(self.__rowsCount), labels=self.__xLabels, visible=False)
            v_ax.set_yticks(np.arange(self.__colsCount), labels=self.__yLabels, visible=False)
            # 设置坐标轴的次要刻度间隔
            v_ax.xaxis.set_minor_locator(v_xMinorLocator)
            v_ax.yaxis.set_minor_locator(v_yMinorLocator)
            # 设置坐标轴的横纵轴比例相等
            v_ax.set_aspect('equal')
    
        # 网格内容设置
        def _GridValueSet(self, p_ax):
            v_ax = p_ax
            for i in range(self.__rowsCount + 1):
                for j in range(self.__colsCount + 1):
                    v_str = '(' + str(i + 0.5) + ',' + str(j + 0.5) + ')'
                    v_ax.text(i + 0.5, j + 0.5, v_str, ha='center', va='center', color='w')

Node.py

import random
    
    
    # Node坐标类
    class NodePosition:
        def __init__(self, p_x: float = 1, p_y: float = 1):
            self.x = p_x
            self.y = p_y
    
        def __str__(self):
            return '[' + str(self.x) + ',' + str(self.y) + ']'
    
    
    # Node类
    class Node:
        def __init__(self, p_nodeName: str, p_nodePos: NodePosition):
            self.nodeName = p_nodeName
            self.nodePos = p_nodePos
            # f=g+h，g代表从上一个点到该点的代价和，h代表从该点到终点的代价和，f代表总权值weight
            self.f: int = 0
            self.g: int = 0
            self.h: int = 0
    
        # 设置Node的权值
        def SetWeight(self, p_f: int, p_g: int, p_h: int):
            self.f = p_f
            self.g = p_g
            self.h = p_h
    
        def __str__(self):
            return '{nodeName:' + self.nodeName + ',nodePos:' + str(self.nodePos) + ',f=' + str(self.f) + ',g=' + str(
                self.g) + ',h=' + str(self.h) + '}'
    
    
    # Node工厂，用来创建和获取起始点Node和终点Node
    class NodeFactory:
        __isCreateEndNode = False
        __isCreateStartNode = False
        __startNode: Node
        __endNode: Node
    
        def __init__(self):
            pass
    
        # 获取起始点Node
        def GetStartNode(self):
            if self.__isCreateStartNode is False:
                self._CreateStartNode()
            return self.__startNode
    
        def _CreateStartNode(self):
            v_nodePos = NodePosition(0.5, 0.5)
            self.__startNode = Node('StartNode', v_nodePos)
            self.__isCreateStartNode = True
    
        # 获取终点Node
        def GetEndNode(self, p_rowsCount: int, p_colsCount: int):
            if self.__isCreateEndNode is False:
                self._CreateEndNode(p_rowsCount, p_colsCount)
            return self.__endNode
    
        def _CreateEndNode(self, p_rowsCount, p_colsCount):
            v_x = 0.5
            v_y = 0.5
            while v_x == 0.5 and v_y == 0.5:
                v_i = random.randint(0, p_rowsCount - 1)
                v_x = v_i + 0.5
                v_i = random.randint(0, p_colsCount - 1)
                v_y = v_i + 0.5
            v_nodePos = NodePosition(v_x, v_y)
            self.__endNode = Node('EndNode', v_nodePos)
            self.__isCreateEndNode = True

SearchPath.py

from myCodes import SharedData as sd
    from myCodes import Node as node
    
    
    # 查找最佳路径
    class SearchPath:
        # 对角线移动一格的代价
        __diagonalCost = 14
        # 上下或左右移动一格的代价
        __nonDiagonalCost = 10
        __nodeFactory: node.NodeFactory
        __currentNode: node.Node
        # 是否完成了初始化
        __isInit = False
        __openList = []
        __closeList = []
        # 网格行列数
        __rowsCount = 0
        __colsCount = 0
        # Node名称索引
        __nameIndex = 1
        # x和y的最小值
        __minX = 0.5
        __minY = 0.5
        # x和y的最大值
        __maxX = 0
        __maxY = 0
        # 终点Node
        __endNode: node.Node
    
        def __init__(self):
            if self.__isInit is False:
                self.__nodeFactory = node.NodeFactory()
                self.__currentNode = self.__nodeFactory.GetStartNode()
                self.__openList.append(self.__currentNode)
                self.__rowsCount = sd.SharedData.rowsCount
                self.__colsCount = sd.SharedData.colsCount
                self.__maxX = self.__rowsCount - 0.5
                self.__maxY = self.__colsCount - 0.5
                self.__endNode = sd.SharedData.endNode
                self.__isInit = True
    
        # 查找最佳路径
        def Search(self):
            while self._UpdateCurrentNode():
                v_list = self._GetNextNodes()
                v_list = self._NodeCheck(v_list)
                v_list = self._CalculateWeight(v_list)
                v_list = self._SortNode(v_list)
                self._AddNode(v_list)
            sd.SharedData.pathNodes = self.__closeList
            self._PrintPath()
    
        # 获取currentNode下一步可以前往的Node，并将它们保存在一个临时列表v_list中
        def _GetNextNodes(self):
            if self.__currentNode is not None:
                v_p = self.__currentNode.nodePos
                v_posList = [(v_p.x - 1, v_p.y), (v_p.x - 1, v_p.y + 1), (v_p.x, v_p.y + 1), (v_p.x + 1, v_p.y + 1),
                             (v_p.x + 1, v_p.y), (v_p.x + 1, v_p.y - 1), (v_p.x, v_p.y - 1), (v_p.x - 1, v_p.y - 1)]
                v_nameList = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']
                v_list = []
                for i in range(len(v_posList)):
                    v_nodeName = v_nameList[i] + str(self.__nameIndex)
                    v_nodePos = node.NodePosition(v_posList[i][0], v_posList[i][1])
                    v_node = node.Node(v_nodeName, v_nodePos)
                    if self._isInOpenList(v_node) is False and self._isInCloseList(v_node) is False:
                        v_list.append(v_node)
                self.__nameIndex += 1
                return v_list
            return None
    
        # 检查临时列表p_list中哪些Node不符合要求，保留符合要求的节点，删除不符合要求的节点
        def _NodeCheck(self, p_list):
            if p_list is not None:
                v_list = []
                for i in range(len(p_list)):
                    v_x = p_list[i].nodePos.x
                    v_y = p_list[i].nodePos.y
                    if self.__minX <= v_x <= self.__maxX and self.__minY <= v_y <= self.__maxY:
                        v_list.append(p_list[i])
                return v_list
            return None
    
        # 计算临时列表p_list中每个Node的权值
        def _CalculateWeight(self, p_list):
            if p_list is not None:
                v_list = p_list
                v_startNodeX = self.__currentNode.nodePos.x
                v_startNodeY = self.__currentNode.nodePos.y
                v_endNodeX = self.__endNode.nodePos.x
                v_endNodeY = self.__endNode.nodePos.y
                for n in v_list:
                    v_x = n.nodePos.x
                    v_y = n.nodePos.y
                    if v_x == v_y:
                        v_g = abs((v_x - v_startNodeX)) * self.__diagonalCost
                        if v_endNodeX == v_endNodeY:
                            v_h = abs((v_endNodeX - v_x)) * self.__diagonalCost
                        else:
                            v_h = abs((v_endNodeX - v_x)) * self.__nonDiagonalCost + abs(
                                (v_endNodeY - v_y)) * self.__nonDiagonalCost
                    else:
                        v_g = abs((v_x - v_startNodeX)) * self.__nonDiagonalCost + abs(
                            (v_y - v_startNodeY)) * self.__nonDiagonalCost
                        v_h = abs((v_endNodeX - v_x)) * self.__nonDiagonalCost + abs(
                            (v_endNodeY - v_y)) * self.__nonDiagonalCost
                    v_f = v_g + v_h
                    n.SetWeight(v_f, v_g, v_h)
                return v_list
            return None
    
        # 根据临时列表p_list中每个Node的权值进行排序，权值越小越接近列表尾
        def _SortNode(self, p_list):
            if p_list is not None:
                v_list = p_list
                for i in range(0, len(v_list)):
                    for j in range(i + 1, len(v_list)):
                        if v_list[i].f > v_list[j].f:
                            v_node = v_list[i]
                            v_list[i] = v_list[j]
                            v_list[j] = v_node
                return v_list
            return None
    
        # 将临时列表p_list拼接在openList的列表尾
        def _AddNode(self, p_list):
            if p_list is not None:
                v_list = []
                for i in range(len(p_list)):
                    v_n = p_list[i]
                    if self._isInOpenList(v_n) is False and self._isInCloseList(v_n) is False:
                        v_list.append(v_n)
                self.__openList.append(v_list[0])
                return True
            return False
    
        # 判断p_node是否为终点Node
        def _ReachEndNodeCheck(self, p_node: node.Node):
            v_x = p_node.nodePos.x
            v_y = p_node.nodePos.y
            if v_x == self.__endNode.nodePos.x and v_y == self.__endNode.nodePos.y:
                return True
            return False
    
        # 判断p_node是否在openList中
        def _isInOpenList(self, p_node: node.Node):
            v_x = p_node.nodePos.x
            v_y = p_node.nodePos.y
            for n in self.__openList:
                if v_x == n.nodePos.x and v_y == n.nodePos.y:
                    return True
            return False
    
        # 判断p_node是否在closeList中
        def _isInCloseList(self, p_node: node.Node):
            v_x = p_node.nodePos.x
            v_y = p_node.nodePos.y
            for n in self.__closeList:
                if v_x == n.nodePos.x and v_y == n.nodePos.y:
                    return True
            return False
    
        # 打印最佳路径
        def _PrintPath(self):
            v_str = ''
            v_length = len(self.__closeList)
            for i in range(v_length):
                if i < v_length - 1:
                    v_str += str(self.__closeList[i]) + '-->'
                else:
                    v_str += str(self.__closeList[i])
            print(v_str)
    
        # 从openList中获取列表尾的元素，将之作为currentNode并加入closeList中，然后将其从openList中移除
        def _UpdateCurrentNode(self):
            if len(self.__openList) > 0:
                self.__currentNode = self.__openList[0]
                if self._isInCloseList(self.__currentNode) is False:
                    self.__closeList.append(self.__currentNode)
                    del self.__openList[0]
                    if self._ReachEndNodeCheck(self.__currentNode):
                        return False
                    return True
            return False

SharedData.py

from myCodes import Node as node
    
    
    # 共享信息类
    class SharedData:
        # 网格行列数
        rowsCount = 0
        colsCount = 0
        # 终点Node
        endNode: node.Node
        # 所查找的路径Node集
        pathNodes = []

Main.py

from myCodes import SearchPath as sp
    from myCodes import Grid as grid
    
    g = grid.Grid(5, 5)
    sp.SearchPath().Search()
    g.Draw()

##### 运行截图 #####

![59795daf00c8082ac611d7c4a78c9251.jpeg][]

![9a2038201709cb77433517631d937c29.jpeg][]

##### 代码解说 #####

在这个示例中，我们分为两个层，一个是视图层，一个是数据层，视图层包括Grid类，数据层包括Node类、NodePosition类、NodeFactory类、SharedData类、SearchPath类。Grid类负责对A\_Star算法进行可视化展现，包括基础的网格、文本和标记的构建和显示；Node类作为节点类，对应着视图层的每一个网格单元；NodePosition类用于记录节点的坐标信息；NodeFactory类用于创建和获取起始点Node和终点Node；SharedData类用于全局信息的共享；SearchPath类是路径查找类，该类中主要编写了A\_Star算法实现的逻辑，包括获取下一步可前往的Node集、对Node集中的Node进行筛选、计算Node的权值、对Node集进行排序、将Node集添加至OpenList中以及从OpenList中读取Node等。主要的调用顺序：1.在Main方法中创建Grid的实例，并且传递网格的行列数；2.执行SearchPath中的Search方法查找最佳路径；3.调用Grid中的Draw方法对最佳路径进行显示。

**下一篇将讲解添加障碍物后的A-Star算法探索和实现**

**如果这篇文章对你有帮助，请给作者点个赞吧！**

[Link 1]: https://baike.baidu.com/item/%E5%90%AF%E5%8F%91%E5%BC%8F%E6%90%9C%E7%B4%A2%E7%AE%97%E6%B3%95/7020552?fromModule=lemma_inlink
[Link 2]: https://www.gamedev.net/reference/articles/article2003.asp
[Link 3]: http://www.cppblog.com/mythit/archive/2009/04/19/80492.aspx
[189729a94d1e9c15d8c27754016d8816.jpeg]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/28/49091272595e45ec9833492290b81cb9.png
[59795daf00c8082ac611d7c4a78c9251.jpeg]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/28/b2599d354995488296ab03a3073d632d.png
[9a2038201709cb77433517631d937c29.jpeg]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/28/eb7b11951d55469aa2981bc4ba8fc557.png