摘要
针对RRT*算法存在收敛速度慢的问题,提出了一种均匀Logistic混沌序列采样的RRT*路径规划算法。该方法使用均匀分布Logistic混沌序列方法代替RRT*算法中随机数方法,保证了采样点的随机性和遍历性,提高了通过障碍物区域的概率,加速了随机树重布线的效率。最后通过仿真和实验证明,均匀分布Logistic混沌序列采样的RRT*算法比传统的RRT*算法的采样点少、路径规划时间短和稳定性好。
In view of the slow convergence rate of RRT*algorithm,a new RRT*path planning algorithm of uniform Logistic chaotic sequence sampling is proposed in this paper.The method uses uniformly distributed Logistic chaotic sequence instead of random numbers in the traditional RRT*algorithm,which ensures the randomness and ergodicity of sampling points.The algorithm improves the probability of passing through the obstacle area,and also accelerates the efficiency of random tree rerouting.Finally,it is proved by simulation and experiment that new RRT*algorithm based on uniformly distributed Logistic chaotic sequence sampling has fewer sampling points、shorter path planning time and good stability than traditional RRT*algorithm.
作者
马小陆
袁书生
王兵
吴紫恒
MA Xiaolu;YUAN Shusheng;WANG Bing;WU Ziheng(School of Electrical and Information Engineering,Anhui University of Technology,Maanshan 243032,Anhui,China;Open Project of AnHui Province Key Laboratory of Special and Heavy Load Robot,Maanshan 243032,Anhui,China)
出处
《机械科学与技术》
CSCD
北大核心
2022年第4期610-618,共9页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(61472282)
安徽高校自然科学研究重点项目(KJ2019A0065)
安徽省教育厅高校科学研究重大项目(KJ2019ZD05)
特种重载机器人安徽省重点实验室开放课题(TZJQR004-2020)。
