An Adaptive Rapidly-Exploring Random Tree

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 tasks. However, sampling-based m...

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Bibliographic Details
Published in:IEEE/CAA journal of automatica sinica Vol. 9; no. 2; pp. 283 - 294
Main Authors: Li, Binghui, Chen, Badong
Format: Journal Article
Language:English
Published: Piscataway Chinese Association of Automation (CAA) 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Artificial Intelligence and Robotics,Xi'an Jiaotong University,Xi'an 710049,China
Subjects:
Adaptive sampling
Algorithms
Motion planning
Narrow passage
Path planning
Planning
rapidly-exploring random tree (RRT)-Connect
sampling-based algorithm
Selectivity
Surface treatment
Task analysis
sampling-based algorithm
Narrow passage
path planning
rapidly-exploring random tree (RRT)-Connect
ISSN:2329-9266, 2329-9274
Online Access:Get full text
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Summary: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 tasks. However, sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow passages. Therefore, several algorithms have been proposed to overcome these drawbacks. As one of the improved algorithms, Rapidly-exploring random vines (RRV) can achieve better results, but it may perform worse in cluttered environments and has a certain environmental selectivity. In 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 environments. The proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.
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ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2021.1004252