摘要
针对RRT^(*)算法在路径规划中存在的效率低、节点冗余、路径不平滑等问题,提出了基于Halton采样-先验RRT^(*)算法的机器人路径规划方法。对障碍物进行膨化处理,并给出了机器人的碰撞检测方法。对RRT^(*)算法原理及存在问题进行了分析,基于先验信息给出了节点扩展的目标偏置策略,有效降低了扩展盲目性和随机性;基于Halton采样方法给出了候选点集策略,并建立了从点集中选择最优采样点的评价函数;最后通过剪枝和三次B样条曲线剪除了多余节点,并平滑了路径。经2维和3维仿真验证可知,与标准RRT^(*)、文献[12]改进RRT^(*)规划路径相比,Halton采样-先验RRT^(*)路径长度最小、规划耗时最少、扩展的节点也最少。实验结果表明,Halton采样-先验RRT^(*)算法有效提高了路径规划效率、减少了路径长度。
Aiming at the problems of low efficiency,node redundancy and unsmooth path of RRT^(*)algorithm in path planning,a robot path planning method based on Halton sampling-priori RRT^(*)algorithm is proposed.The expansion strategy is used to deal with the obstacles,and the collision detection method of robot is provided.The principle and existing problems of RRT^(*)algorithm are analyzed,and the target offset strategy of node expansion is presented based on a priori information,which effectively reduces the blindness and randomness of expansion;Based on Halton sampling method,the candidate point set strategy is applied,and the evaluation function of selecting the optimal sampling point from the point set is constructed;Finally,the redundant nodes are cut off and the path is smoothed by pruning and cubic B-spline curve.According to the two-dimensional and three-dimensional simulation,compared with the standard RRT^(*)and the improved RRT^(*)planning path in paper[12],the path by Halton sampling-priori RRT^(*)has the smallest length,the least planning time and the least expanded nodes.The experimental results show that the Halton sampling-priori RRT^(*)algorithm effectively improves the efficiency of path planning and reduces the path length.
作者
陈娟
梅占勇
马晓慧
CHEN Juan;MEI Zhan-yong;MA Xiao-hui(Shanxi Vocational University of Engineering Science and Technology,Taiyuan 030031,China;College of Computer Science and Cyber Security,Chengdu University of Technology,Chengdu 610059,China)
出处
《组合机床与自动化加工技术》
北大核心
2022年第8期1-5,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
2020年度山西省高等学校教学改革创新项目(J2020440)
2019年教育部第二批产学合作协同育人项目(201902167003)。
作者简介
陈娟(1979—),女,副教授,硕士,研究方向为计算机智能算法分析及应用,(E-mail)chenjuanan1979@163.com。
