An Adaptive Rapidly-Exploring Random Tree

作     者：Binghui Li Badong Chen Binghui Li;Badong Chen

作者机构：Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’an 710049China IEEE 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2022年第9卷第2期

页      面：283-294页

核心收录：

学科分类：08[工学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported in part by the National Science Foundation of China(61976175,91648208) the Key Project of Natural Science Basic Research Plan in Shaanxi Province of China(2019JZ-05) 

主　　题：Narrow passage path planning rapidly-exploring random tree(RRT)-Connect sampling-based algorithm 

摘      要：Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning(MP)problems,in which rapidly-exploring random tree(RRT)and the faster bidirectional RRT(named RRT-Connect)algorithms have achieved good results in many planning ***,sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow ***,several algorithms have been proposed to overcome these *** one of the improved algorithms,Rapidlyexploring random vines(RRV)can achieve better results,but it may perform worse in cluttered environments and has a certain environmental *** this paper,we present a new improved planning method based on RRT-Connect and RRV,named adaptive RRT-Connect(ARRT-Connect),which deals well with the narrow passage environments while retaining the ability of RRT algorithms to plan paths in other *** proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.