Mobile robot path planning based on improved RRT* algorithm

被引：0
|
作者：
Zhang W. [1 ]
Fu S. [1 ]
机构：
[1] School of Mechanical and Electronic Information, China University of Geosciences, Wuhan
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2021年 / 49卷 / 01期
关键词：
Asymptotically optimal rapidly-exploring random tree (RRT*) algorithm; Biased extending; Constrained sampling; Cubic B-spline; Mobile robot; Path planning;
D O I：
10.13245/j.hust.210101
中图分类号：
学科分类号：
摘要：
An improved asymptotically optimal rapidly-exploring random tree (RRT*) algorithm was proposed based on goal-biased constrained sampling and goal-biased extending,to address the problems of much memory usage,low planning efficiency of path planning in RRT* algorithm under special environments,such as narrow passages.First,a goal-biased strategy was applied to sampling,and the position constraint was placed in each sample,so as to make sampling more goal-oriented than existing algorithms.Second,discarding the idea that the existing algorithm simply extended towards the sampling point on the new point extension,by assigning different weights to the sampling point and goal point,each extension could be decided by the sampling point and goal point at the same time,thus speeding up the search speed.Then,the cubic B-spline curve was used to smooth the searched path,ensuring the path feasibility.Finally,the 2D and 3D comparison experiments were conducted for the RRT* algorithm and the improved RRT* algorithm based on Matlab and V-REP respectively,and experiment results verified the superiority and effectiveness of the improved RRT* algorithm. © 2021, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：31 / 36
页数：5
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