摘要
渐进最优快速探索随机树(RRT*)算法是快速扩展随机树(RRT)算法的改进版本,具有渐进最优性,但存在收敛速度慢、初始路径代价高、算法效率低等缺点。针对这些问题,文中提出了一种多策略融合优化的快速探索随机树(RRT*)路径规划算法,该算法引入自适应目标偏置策略、父节点重选策略和分层冗余节点去除策略,同时具备路径质量高,路径生成与收敛速度快的优势。为了验证算法的有效性,设计了不同环境下与RRT*、BGRRT*和RRT*-Smart算法生成的初始路径和收敛速度的对比实验。结果表明,提出的算法能够生成更优的初始解,并具有更高的稳定性和更快的收敛速度。
As an enhanced version of the rapidly-exploring random tree(RRT)algorithm,the RRT^(*)algorithm with progressive optimality has garnered significant attention.However,it still exhibits certain limitations,such as slow convergence speed,high initial path cost,and relatively low algorithm efficiency.To address these challenges,a novel RRT^(*)-based path planning algorithm that incorporates multi-strategy fusion optimization was proposed.Specifically,the algorithm integrates three key strategies:adaptive target bias,parent node reselection,and hierarchical redundant node removal.These strategies collectively contribute to the algorithm’s superior performance,characterized by high path quality,rapid path generation,and accelerated convergence.To validate the effectiveness of the proposed algorithm,a series of experiments were meticulously designed to compare the initial path quality and convergence speed of three prominent algorithms-RRT^(*),BG-RRT^(*),and RRT^(*)-Smart-in diverse environmental settings.The results demonstrate that the proposed algorithm not only generates superior initial solutions but also exhibits higher stability and faster convergence speed,which underscores its enhanced performance and reliability.
作者
郭荣秋
吴敬兵
戚海洲
Guo Rongqiu;Wu Jingbing;Qi Haizhou
出处
《起重运输机械》
2025年第6期41-47,共7页
Hoisting and Conveying Machinery
关键词
快速探索随机树
目标偏置
冗余节点
多策略融合
rapidly-exploring random tree
target bias
redundant nodes
multi-strategy fusion
