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Target-biased informed trees: sampling-based method for optimal motion planning in complex environments

Xianpeng Wang, Xinglu Ma, Xiaoxu Li, Xiaoyu Ma, Chunxu Li Orcid Logo

Journal of Computational Design and Engineering, Volume: 9, Issue: 2, Pages: 755 - 771

Swansea University Author: Chunxu Li Orcid Logo

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DOI (Published version): 10.1093/jcde/qwac025

Abstract

Aiming at the problem that the progressively optimized Rapidly-exploring Random Trees Star (RRT*) algorithm generates a large number of redundant nodes, which causes slow convergence and low search efficiency in high-dimensional and complex environments. In this paper we present Target-biased Inform...

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Published in: Journal of Computational Design and Engineering
ISSN: 2288-5048
Published: Oxford University Press (OUP) 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa65998
first_indexed 2024-04-10T08:21:28Z
last_indexed 2024-11-25T14:17:17Z
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spelling 2024-05-22T16:27:27.7583150 v2 65998 2024-04-09 Target-biased informed trees: sampling-based method for optimal motion planning in complex environments e6ed70d02c25b05ab52340312559d684 0000-0001-7851-0260 Chunxu Li Chunxu Li true false 2024-04-09 ACEM Aiming at the problem that the progressively optimized Rapidly-exploring Random Trees Star (RRT*) algorithm generates a large number of redundant nodes, which causes slow convergence and low search efficiency in high-dimensional and complex environments. In this paper we present Target-biased Informed Trees (TBIT*), an improved RRT* path planning algorithm based on target-biased sampling strategy and heuristic optimization strategy. The algorithm adopts a combined target bias strategy in the search phase of finding the initial path to guide the random tree to grow rapidly toward the target direction, thereby reducing the generation of redundant nodes and improving the search efficiency of the algorithm; after the initial path is searched, heuristic sampling is used to optimize the initial path instead of optimizing the random tree, which can benefit from reducing useless calculations, and improve the convergence capability of the algorithm. The experimental results show that the algorithm proposed in this article changes the randomness of the algorithm to a certain extent, and the search efficiency and convergence capability in complex environments have been significantly improved, indicating that the improved algorithm is feasible and efficient. Journal Article Journal of Computational Design and Engineering 9 2 755 771 Oxford University Press (OUP) 2288-5048 path planning, rapidly exploring random trees, improved RRT*, target bias, heuristic 14 4 2022 2022-04-14 10.1093/jcde/qwac025 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University Another institution paid the OA fee 2024-05-22T16:27:27.7583150 2024-04-09T20:05:11.7831247 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Xianpeng Wang 1 Xinglu Ma 2 Xiaoxu Li 3 Xiaoyu Ma 4 Chunxu Li 0000-0001-7851-0260 5 65998__30444__45e0e7842e364b61850d7bda64e66a9c.pdf 65998.VoR.pdf 2024-05-22T16:25:48.4523698 Output 6612981 application/pdf Version of Record true Copyright: TheAuthor(s) 2022. This is an Open Access article distributed under the terms of the Creative Commons Attribution License. true eng https://creativecommons.org/licenses/by/4.0/
title Target-biased informed trees: sampling-based method for optimal motion planning in complex environments
spellingShingle Target-biased informed trees: sampling-based method for optimal motion planning in complex environments
Chunxu Li
title_short Target-biased informed trees: sampling-based method for optimal motion planning in complex environments
title_full Target-biased informed trees: sampling-based method for optimal motion planning in complex environments
title_fullStr Target-biased informed trees: sampling-based method for optimal motion planning in complex environments
title_full_unstemmed Target-biased informed trees: sampling-based method for optimal motion planning in complex environments
title_sort Target-biased informed trees: sampling-based method for optimal motion planning in complex environments
author_id_str_mv e6ed70d02c25b05ab52340312559d684
author_id_fullname_str_mv e6ed70d02c25b05ab52340312559d684_***_Chunxu Li
author Chunxu Li
author2 Xianpeng Wang
Xinglu Ma
Xiaoxu Li
Xiaoyu Ma
Chunxu Li
format Journal article
container_title Journal of Computational Design and Engineering
container_volume 9
container_issue 2
container_start_page 755
publishDate 2022
institution Swansea University
issn 2288-5048
doi_str_mv 10.1093/jcde/qwac025
publisher Oxford University Press (OUP)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description Aiming at the problem that the progressively optimized Rapidly-exploring Random Trees Star (RRT*) algorithm generates a large number of redundant nodes, which causes slow convergence and low search efficiency in high-dimensional and complex environments. In this paper we present Target-biased Informed Trees (TBIT*), an improved RRT* path planning algorithm based on target-biased sampling strategy and heuristic optimization strategy. The algorithm adopts a combined target bias strategy in the search phase of finding the initial path to guide the random tree to grow rapidly toward the target direction, thereby reducing the generation of redundant nodes and improving the search efficiency of the algorithm; after the initial path is searched, heuristic sampling is used to optimize the initial path instead of optimizing the random tree, which can benefit from reducing useless calculations, and improve the convergence capability of the algorithm. The experimental results show that the algorithm proposed in this article changes the randomness of the algorithm to a certain extent, and the search efficiency and convergence capability in complex environments have been significantly improved, indicating that the improved algorithm is feasible and efficient.
published_date 2022-04-14T05:33:48Z
_version_ 1821382413669892096
score 11.3749895

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