Academic Journal
An Adaptive Multi-Target RRT* Algorithm via Reachability-Aware Hierarchical Sampling.
| Title: | An Adaptive Multi-Target RRT* Algorithm via Reachability-Aware Hierarchical Sampling. |
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| Authors: | Yang, Zecheng1 (AUTHOR) 20231083510930@buu.edu.cn, Ma, Nan2 (AUTHOR) manan123@bjut.edu.cn, Yang, Yajue2 (AUTHOR) yangyajue@bjut.edu.cn |
| Source: | International Journal of Pattern Recognition & Artificial Intelligence. Sep2026, Vol. 40 Issue 12, p1-18. 18p. |
| Subject Terms: | *Warehousing & storage, Robotic path planning, Sampling (Process), Optimization algorithms, Mobile robots |
| Abstract: | In warehousing logistics, Autonomous Mobile Robots (AMRs) are frequently tasked with visiting a dense set of target locations, such as storage bins, in a single deployment. The primary computational bottleneck in these missions lies in generating collision-free trajectories amidst narrow aisles and static racking. Conventional strategies, which either treat target connections as independent queries or initialize search forests from all targets simultaneously, often suffer from computational redundancy in such cluttered environments. In this paper, we propose the Adaptive Multi-Target RRT* (AMT-RRT*). Unlike forest-based approaches, our method maintains a single, persistent search tree rooted at the robot's initial configuration. We introduce a Reachability-Aware Hierarchical Sampling strategy that dynamically guides the tree's expansion through narrow passages toward the most accessible targets, thereby avoiding ineffective exploration in blocked regions. Experimental results demonstrate that this strategy significantly reduces the computation time required to connect the complete goal set compared to state-of-the-art planners. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Business Source Index |
| FullText | Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Adaptive Multi-Target RRT* Algorithm via Reachability-Aware Hierarchical Sampling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Zecheng%22">Yang, Zecheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 20231083510930@buu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Ma%2C+Nan%22">Ma, Nan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> manan123@bjut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Yajue%22">Yang, Yajue</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> yangyajue@bjut.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Pattern+Recognition+%26+Artificial+Intelligence%22">International Journal of Pattern Recognition & Artificial Intelligence</searchLink>. Sep2026, Vol. 40 Issue 12, p1-18. 18p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Warehousing+%26+storage%22">Warehousing & storage</searchLink><br /><searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+%28Process%29%22">Sampling (Process)</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+robots%22">Mobile robots</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In warehousing logistics, Autonomous Mobile Robots (AMRs) are frequently tasked with visiting a dense set of target locations, such as storage bins, in a single deployment. The primary computational bottleneck in these missions lies in generating collision-free trajectories amidst narrow aisles and static racking. Conventional strategies, which either treat target connections as independent queries or initialize search forests from all targets simultaneously, often suffer from computational redundancy in such cluttered environments. In this paper, we propose the Adaptive Multi-Target RRT* (AMT-RRT*). Unlike forest-based approaches, our method maintains a single, persistent search tree rooted at the robot's initial configuration. We introduce a Reachability-Aware Hierarchical Sampling strategy that dynamically guides the tree's expansion through narrow passages toward the most accessible targets, thereby avoiding ineffective exploration in blocked regions. Experimental results demonstrate that this strategy significantly reduces the computation time required to connect the complete goal set compared to state-of-the-art planners. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0218001426590238 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: Warehousing & storage Type: general – SubjectFull: Robotic path planning Type: general – SubjectFull: Sampling (Process) Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Mobile robots Type: general Titles: – TitleFull: An Adaptive Multi-Target RRT* Algorithm via Reachability-Aware Hierarchical Sampling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Zecheng – PersonEntity: Name: NameFull: Ma, Nan – PersonEntity: Name: NameFull: Yang, Yajue IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02180014 Numbering: – Type: volume Value: 40 – Type: issue Value: 12 Titles: – TitleFull: International Journal of Pattern Recognition & Artificial Intelligence Type: main |
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