基于改进RRT-Connect算法的移动机器人路径规划
Path Planning for Mobile Robot Based on Improved RRT-Connect Algorithm
朱波 1姜官武 2王旭亮 3王旭1
作者信息
- 1. 西南科技大学信息工程学院,绵阳 621010
- 2. 西南科技大学信息工程学院,绵阳 621010;数字化学习技术集成与应用教育部工程研究中心,北京 100039
- 3. 数字化学习技术集成与应用教育部工程研究中心,北京 100039
- 折叠
摘要
针对双向快速扩展随机树(RRT-Connect)算法在路径规划过程中采样随机性大、目标导向差、搜索效率低等问题,提出了一种基于区域约束采样的RRT-Connect算法.首先,该算法在进行节点扩展的过程中,将采样点限制在由对方搜索树末端所形成的约束区域,提高目标导向性,同时根据该区域形成反向次采样区域,用于逃脱局部震荡;其次,构建变转向角策略,减少随机采样过程中出现的逆生长现象;最后,利用贪婪策略消除路径冗余点以及二阶贝塞尔曲线进行路径平滑.通过多组仿真实验表明,改进RRT-Connect算法在平均路径长度、平均规划时间、有效节点利用率上均有较大改善,证明了改进机制的有效性.
Abstract
To solve the problems of high sampling randomness,poor target orientation,and low search effi-ciency in the path planning process of the bidirectional fast expanding random tree(RRT-Connect)algo-rithm,a region constrained sampling based RRT-Connect algorithm is proposed.Firstly,in the process of node expansion,the algorithm restricts the sampling points to the constraint area formed by the end of the other side′s search tree,to improve target orientation.At the same time,a reverse sub-sampling area is formed based on the area to escape local oscillations;Secondly,a variable steering angle strategy is con-structed to reduce the reverse growth phenomenon that occurs during the random sampling process;Finally,greedy strategy is used to eliminate redundant points in the path and second-order Bessel curve is used for path smoothing.Multiple simulation experiments have shown that the improved RRT-Connect algorithm has significant improvements in average path length,average planning time,and effective node utilization,which demonstrates the effectiveness of the improved mechanism.
关键词
路径规划/区域约束采样/变转向角策略/路径优化Key words
path planning/regionally constrained sampling/variable steering angle strategy/path optimization引用本文复制引用
基金项目
数字化学习技术集成与应用教育部工程研究中心创新项目(1331009)
出版年
2024
NETL
NSTL
万方数据