基于节点到障碍物距离的自适应扩展RRT^(*)路径规划算法  

Adaptive extended RRT^(*)path planning algorithm based on node-to-obstacle distance

作　　者：王蔡琪 崔西宁 熊毅 伍世虔 WANG Caiqi;CUI Xining;XIONG Yi;WU Shiqian(School of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan Hubei 430081,China)

机构地区：[1]武汉科技大学信息科学与工程学院,武汉430081

出　　处：《计算机应用》2025年第3期920-927,共8页journal of Computer Applications

基　　金：湖北省技术创新专项(ZDCX2019000025)。

摘　　要：快速扩展随机树星(RRT^(*))因具有渐近最优性和概率完备性,在机器人路径规划领域有广泛的应用。然而,RRT^(*)及其改进算法仍存在初始路径质量差、路径收敛慢和探索效率低等缺陷。针对这些问题,提出一种基于节点到障碍物距离的自适应扩展RRT^(*)算法——AE-RRT^(*)。为提高探索效率,采用基于节点到障碍物距离的动态目标偏置采样策略和动态步长策略,从而在更短的时间内获得初始路径。为提高路径的质量,提出一种更精确的选择父节点的方法MA-ChooseParent,从而扩大选择父节点的集合。此外,为加快路径收敛,在路径收敛阶段采用基于节点到障碍物距离的自适应高斯采样方法和全局高斯采样方法AG-Gaussian Sample。通过Matlab中的仿真实验将AE-RRT^(*)与RRT^(*)、Quick-RRT^(*)、Bi-RRT^(*)、Informed-RRT^(*)和Smart-RRT^(*)进行对比。实验结果表明,与RRT^(*)相比,AE-RRT^(*)在二维环境中找到初始路径的时间、初始路径的长度和收敛至全局次优路径的时间分别减少了63.78%、6.55%和71.93%;在三维环境中的3个指标分别减少了59.44%、18.26%和79.58%。Rapidly-exploring Random Tree star(RRT^(*))is widely used in the robot path planning field owing to its asymptotic optimality and probabilistic completeness.However,RRT^(*)and its improved algorithms still suffer from several limitations such as poor initial path quality,slow path convergence,and low search efficiency.In response to these challenges,an adaptive extended RRT^(*)algorithm based on node-to-obstacle distance,namely AE-RRT^(*),was proposed.To improve the search efficiency,a dynamic goal-biased sampling strategy and a dynamic step size strategy based on the node-toobstacle distance were adopted.Furthermore,to improve the path quality,a more accurate parent node choice method MAChooseParent was proposed,which broadened the set of potential parent nodes.In addition,to speed up path convergence,an adaptive Gaussian sampling method and a global Gaussian sampling method AG-Gaussian Sample based on the node-toobstacle distance were adopted.Through simulation in Matlab,AE-RRT^(*)was compared with RRT^(*),Quick-RRT^(*),Bi-RRT^(*),Informed-RRT^(*),and Smart-RRT^(*).Experimental results demonstrate that compared to RRT^(*),AE-RRT^(*)achieves reductions of 63.78%,6.55%,and 71.93%,respectively,in the time taken to find the initial path,the length of the initial path,and the time to converge to a global sub-optimal path in 2D environments.In 3D environments,AE-RRT^(*)achieves reductions of 59.44%,18.26%,and 79.58%,respectively,in the three indicators.

关 键 词：快速扩展随机树 动态目标偏置采样 动态步长策略 自适应高斯采样 路径规划 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]