摘要
针对快速扩展随机树(rapidly exploring random tree,RRT)算法在移动机器人路径规划过程中存在盲目搜索、内存计算量大和冗余点较多等问题,提出了改进的RRT算法。首先,随机点进行扩展时引入动态目标采样率,引导随机点向目标点方向扩展;其次,融合A*算法中代价函数策略,在加入不同权重因子之后,选取代价值合适的节点作为待扩展节点;然后,针对初始路径过长并存在过多冗余点的问题,提出反向搜索剪枝方法,对裁剪后的路径进行三次样条插值平滑处理来改善路径质量;最后,利用Pycharm对改进的RRT算法进行仿真验证。仿真结果表明,改进的RRT算法相较于传统RRT算法、RRT*算法和基于概率P的RRT算法(P-RRT),在路径的规划长度、规划时间和扩展节点数上都具有明显优势,提高了机器人的路径规划效率。
This paper presents an improved RRT algorithm to solve the problems of blind search,large amount of memory calculation and many redundancy points in the path planning of the mobile robot using rapidly exploring random tree(RRT)algorithm.Firstly,dynamic target sampling rate is introduced when the random point is expanded to guide the random point to expand in the direction toward the target point.Secondly,the cost function strategy in the A*algorithm is integrated,and the node with suitable value is selected as the node to be expanded after adding different weight factors.Thirdly,aiming at the problem that the initial path is too long and there are too many redundant points,a reverse search pruning method is proposed,and the cubic spline interpolation smoothing process is performed on the clipped path to improve the path quality.Finally,Pycharm is used to simulate and verify the improved RRT algorithm.The simulations show that the improved RRT algorithm has obvious advantages in path planning length,planning time and number of path nodes compared to the traditional RRT algorithm,RRT*algorithm and RRT algorithm based on probability P(P-RRT),and improves the planning efficiency of the robot.
作者
周瑞红
李彩虹
张耀玉
张国胜
梁振英
ZHOU Ruihong;LI Caihong;ZHANG Yaoyu;ZHANG Guosheng;LIANG Zhenying(School of Computer Science and Technology,Shandong University of Technology,Zibo 255049,China)
出处
《山东理工大学学报（自然科学版）》
CAS
2024年第5期54-60,共7页
Journal of Shandong University of Technology:Natural Science Edition
基金
山东省自然科学基金项目(ZR2021MF072)。
