路径规划算法仿真：A星算法改进版，高效搜索与路径优化，带梯度下降及S-G滤波器处理，Matlab实现，可定量比较不同算法效果,改进A*算法：权重系数提升搜索效率、冗余拐角优化及路径平滑处理（Matla
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路径规划算法仿真：A星算法改进版，高效搜索与路径优化，带梯度下降及S-G滤波器处理，Matlab实现，可定量比较不同算法效果,改进A*算法：权重系数提升搜索效率、冗余拐角优化及路径平滑处理（Matlab实现）,路径规划算法仿真 A星算法传统A*(Astar)算法+改进后的A*算法 Matlab代码 可以固定栅格地图与起点终点 可以进行定量比较改进：①提升搜索效率（引入权重系数）②冗余拐角优化（可显示拐角优化次数）③路径平滑处理（引入梯度下降算法配合S-G滤波器）代码含注释 ,核心关键词：路径规划算法仿真; A星算法; 传统A*算法; 改进A*算法; Matlab代码; 固定栅格地图; 起点终点; 定量比较; 搜索效率; 权重系数; 冗余拐角优化; 拐角优化次数; 路径平滑处理; 梯度下降算法; S-G滤波器; 代码注释。,A星算法与改进路径规划算法的Matlab仿真：效率提升与平滑处理 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90405399/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90405399/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">路径规划算法仿真与<span class="_ _0"> </span><span class="ff2">A*</span>算法的改进</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">摘要<span class="ff3">：</span>路径规划是自动驾驶<span class="ff4">、</span>机器人导航等领域中的关键技术之一<span class="ff4">。</span>本文介绍了路径规划算法中的经</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">典算法<span class="_ _0"> </span><span class="ff2">A*<span class="ff3">（</span>Astar<span class="ff3">）</span></span>算法<span class="ff3">，</span>并对其进行了改进<span class="ff4">。</span>通过引入权重系数<span class="ff4">、</span>冗余拐角优化和路径平滑处理<span class="ff3">，</span></div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">改进后的<span class="_ _0"> </span><span class="ff2">A*</span>算法在搜索效率<span class="ff4">、</span>路径质量和平滑度等方面有明显提升<span class="ff4">。</span>文章还附带了基于<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _1"> </span></span>的</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">代码实现<span class="ff3">，</span>可以对固定栅格地图中的起点和终点进行路径规划<span class="ff3">，</span>并进行定量比较<span class="ff4">。</span></div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">关键词<span class="ff3">：</span>路径规划<span class="ff4">、<span class="ff2">A*</span></span>算法<span class="ff4">、</span>改进<span class="ff4">、</span>搜索效率<span class="ff4">、</span>拐角优化<span class="ff4">、</span>路径平滑<span class="ff4">、<span class="ff2">Matlab<span class="_ _1"> </span></span></span>代码</div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff1">引言</span></div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">路径规划是自动驾驶<span class="ff4">、</span>机器人导航等领域中的重要技术<span class="ff3">，</span>它可以帮助车辆或机器人在复杂环境中找到</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">最优路径<span class="ff3">，</span>并实现自主导航<span class="ff4">。<span class="ff2">A*</span></span>算法作为一种经典的路径规划算法<span class="ff3">，</span>具有广泛的应用前景<span class="ff4">。</span>然而<span class="ff3">，</span>传</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">统的<span class="_ _0"> </span><span class="ff2">A*</span>算法在搜索效率<span class="ff4">、</span>路径质量和平滑度等方面还存在一些问题<span class="ff4">。</span>因此<span class="ff3">，</span>本文对<span class="_ _0"> </span><span class="ff2">A*</span>算法进行了改</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">进<span class="ff3">，</span>以提升路径规划的效果和性能<span class="ff4">。</span></div><div class="t m0 x1 h2 yc ff2 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span>A*<span class="ff1">算法的原理与流程</span></div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">A*<span class="ff1">算法是一种基于图搜索的启发式搜索算法<span class="ff3">，</span>它通过评估每个节点的代价函数<span class="ff3">，</span>来确定下一步要访问</span></div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">的节点<span class="ff4">。</span>其基本原理可以简述如下<span class="ff3">：</span></div><div class="t m0 x1 h2 yf ff3 fs0 fc0 sc0 ls0 ws0">（<span class="ff2">1</span>）<span class="ff1">初始化起点和终点</span>，<span class="ff1">并将起点加入待访问节点集合<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y10 ff3 fs0 fc0 sc0 ls0 ws0">（<span class="ff2">2</span>）<span class="ff1">从待访问节点集合选取代价函数最小的节点作为当前节点<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y11 ff3 fs0 fc0 sc0 ls0 ws0">（<span class="ff2">3</span>）<span class="ff1">计算该节点到终点的启发式估计值</span>（<span class="ff1">通常使用欧氏距离或曼哈顿距离</span>）<span class="ff4">。</span></div><div class="t m0 x1 h2 y12 ff3 fs0 fc0 sc0 ls0 ws0">（<span class="ff2">4</span>）<span class="ff1">更新当前节点周围的相邻节点的代价函数和路径<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y13 ff3 fs0 fc0 sc0 ls0 ws0">（<span class="ff2">5</span>）<span class="ff1">重复步骤</span>（<span class="ff2">2</span>）<span class="ff1">到步骤</span>（<span class="ff2">4</span>），<span class="ff1">直到找到终点或待访问节点集合为空<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span>A*<span class="ff1">算法的改进</span></div><div class="t m0 x1 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">为了提升<span class="_ _0"> </span><span class="ff2">A*</span>算法的搜索效率<span class="ff4">、</span>路径质量和平滑度<span class="ff3">，</span>本文对其进行了以下改进<span class="ff4">。</span></div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">3.1.<span class="_"> </span><span class="ff1">提升搜索效率</span></div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">传统<span class="_ _0"> </span><span class="ff2">A*</span>算法的搜索效率受到启发式估计函数的影响<span class="ff3">，</span>当启发式估计值与实际代价函数相差较大时<span class="ff3">，</span>可</div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">能会导致搜索路径的不准确<span class="ff4">。</span>为了提升搜索效率<span class="ff3">，</span>我们引入了权重系数<span class="ff3">，</span>将启发式估计值乘以权重系</div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">数<span class="ff4">。</span>通过调整权重系数的大小<span class="ff3">，</span>可以在搜索速度和路径质量之间进行权衡<span class="ff4">。</span></div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">3.2.<span class="_"> </span><span class="ff1">冗余拐角优化</span></div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">传统<span class="_ _0"> </span><span class="ff2">A*</span>算法在路径规划时未考虑拐角的优化问题<span class="ff3">，</span>导致路径可能存在冗余拐角<span class="ff4">。</span>为了优化路径的质量</div><div class="t m0 x1 h2 y1c ff3 fs0 fc0 sc0 ls0 ws0">，<span class="ff1">我们对<span class="_ _0"> </span><span class="ff2">A*</span>算法进行了改进</span>，<span class="ff1">引入了冗余拐角优化<span class="ff4">。</span>在搜索过程中</span>，<span class="ff1">当发现节点周围存在不必要的拐</span></div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">角时<span class="ff3">，</span>我们会进行优化<span class="ff3">，</span>将路径拐角数量减少<span class="ff4">。</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>