用于三维空间避障的HDCS-RRT^(*)算法机械臂运动规划研究  

Research on Manipulators Motion Planning with HDCS-RRT^(*) Algorithm Applied to Obstacle Avoidance in 3D Space

作　　者：刘春[1] 罗继祥 LIU Chun;LUO Jixiang(School of Computer Science,Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]湖北工业大学计算机学院,武汉430068

出　　处：《机械科学与技术》2024年第12期2105-2113,共9页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家自然科学基金项目(61902116)。

摘　　要：针对RRT^(*)算法应用于多自由度机械臂三维空间避障运动规划过程中存在的规划效率低、内存消耗大、规划效果不稳定等问题,提出一种启发式动态约束采样的RRT^(*)(Heuristic dynamic constrained sampling-RRT^(*),HDCS-RRT^(*))算法。制定启发式动态采样域策略,通过目标偏向有界性的思想,提高算法收敛速度;设计自适应节点生成机制,根据障碍物环境调节新节点的生成方式,提高算法稳定性;通过冗余点检测,进一步优化路径成本。仿真与实验结果表明,相比于RRT^(*)算法,改进后的算法规划速度更快,在狭窄区域的适应度良好,能够以低内存消耗和低时间成本生成较优的运动路径。To solve the problems in the application of RRT^(*)algorithm in the process of obstacle avoidance motion planning for multi-DOF manipulators in 3D space,including low planning efficiency,large memory consumption and unstable planning result,a heuristic dynamic constrained sampling RRT^(*)algorithm(HDCS-RRT^(*))is proposed in this paper.The algorithm devises a strategy of heuristic dynamic sampling domain,introduces the idea of goal-biased approach towards bounded to improve the convergence speed;it develops an adaptive node generation mechanism to adjust the generation mode of new nodes according to the obstacle environment,improves the stability of the algorithm;and the path costs are further optimized through redundant point detection.Simulation and experimental results show that the improved algorithm has faster planning,good adaptability in narrow areas,and can generate better motion paths with lower memory consumption and shorter path planning time than the RRT^(*)algorithm.

关 键 词：运动规划 HDCS-RRT^(*) 启发式动态采样域 自适应节点生成机制 冗余点检测 

分 类 号：TP242.2[自动化与计算机技术—检测技术与自动化装置]