Academic Journal
An improved Q-learning algorithm for search and rescue UGV path planning in unknown environments.
| Τίτλος: | An improved Q-learning algorithm for search and rescue UGV path planning in unknown environments. |
|---|---|
| Συγγραφείς: | Zhang, Shanfan1 (AUTHOR), Zeng, Qingshuang1 (AUTHOR), Zeng, Yi2 (AUTHOR) yi.zeng@hit.edu.cn |
| Πηγή: | International Journal of Systems Science. Jun2026, Vol. 57 Issue 8, p2360-2375. 16p. |
| Θεματικοί όροι: | *Emergency management, Search & rescue operations, Robotic path planning, Autonomous vehicles, Uncertain systems, Reinforcement learning, Online algorithms |
| Περίληψη: | To minimise risks associated with tasks, unmanned ground vehicles are deployed in hazardous areas to search for targets. However, in non-cooperative environments, such as disaster-stricken mines, communication signals may be compromised, making it challenging to locate trapped individuals accurately. To increase the success rate of search and rescue operations, this article proposes an online path planning algorithm that integrates grey system theory with reinforcement learning. The algorithm's dual approach enables real-time systems to make decisions with greater resistance to interference. To generate reliable state predictions, it extends the grey residual error correction model to the environmental data analysis algorithm, improving the accuracy of data analysis. Additionally, the algorithm introduces a decision weighting scheme for multi-objective decision processes, allowing for flexible adjustments to the decisions made, and helps mitigate the influence of erroneous data. The algorithm also adaptively optimises the Q-Learning action selection algorithm, emphasising key parameters and reward functions to accelerate the stable convergence of the method. Finally, various search and rescue scenarios are simulated, and the proposed algorithm is compared with several existing online planning methods to demonstrate its effectiveness. The results show that this innovative search planning method can successfully navigate and capture targets within the specified search area. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Systems Science is the property of Taylor & Francis Ltd 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.) | |
| Βάση Δεδομένων: | Business Source Index |
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| Items | – Name: Title Label: Title Group: Ti Data: An improved Q-learning algorithm for search and rescue UGV path planning in unknown environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Shanfan%22">Zhang, Shanfan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zeng%2C+Qingshuang%22">Zeng, Qingshuang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zeng%2C+Yi%22">Zeng, Yi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> yi.zeng@hit.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Systems+Science%22">International Journal of Systems Science</searchLink>. Jun2026, Vol. 57 Issue 8, p2360-2375. 16p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Emergency+management%22">Emergency management</searchLink><br /><searchLink fieldCode="DE" term="%22Search+%26+rescue+operations%22">Search & rescue operations</searchLink><br /><searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertain+systems%22">Uncertain systems</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Online+algorithms%22">Online algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To minimise risks associated with tasks, unmanned ground vehicles are deployed in hazardous areas to search for targets. However, in non-cooperative environments, such as disaster-stricken mines, communication signals may be compromised, making it challenging to locate trapped individuals accurately. To increase the success rate of search and rescue operations, this article proposes an online path planning algorithm that integrates grey system theory with reinforcement learning. The algorithm's dual approach enables real-time systems to make decisions with greater resistance to interference. To generate reliable state predictions, it extends the grey residual error correction model to the environmental data analysis algorithm, improving the accuracy of data analysis. Additionally, the algorithm introduces a decision weighting scheme for multi-objective decision processes, allowing for flexible adjustments to the decisions made, and helps mitigate the influence of erroneous data. The algorithm also adaptively optimises the Q-Learning action selection algorithm, emphasising key parameters and reward functions to accelerate the stable convergence of the method. Finally, various search and rescue scenarios are simulated, and the proposed algorithm is compared with several existing online planning methods to demonstrate its effectiveness. The results show that this innovative search planning method can successfully navigate and capture targets within the specified search area. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Systems Science is the property of Taylor & Francis Ltd 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: 10.1080/00207721.2025.2558132 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 2360 Subjects: – SubjectFull: Emergency management Type: general – SubjectFull: Search & rescue operations Type: general – SubjectFull: Robotic path planning Type: general – SubjectFull: Autonomous vehicles Type: general – SubjectFull: Uncertain systems Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Online algorithms Type: general Titles: – TitleFull: An improved Q-learning algorithm for search and rescue UGV path planning in unknown environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Shanfan – PersonEntity: Name: NameFull: Zeng, Qingshuang – PersonEntity: Name: NameFull: Zeng, Yi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00207721 Numbering: – Type: volume Value: 57 – Type: issue Value: 8 Titles: – TitleFull: International Journal of Systems Science Type: main |
| ResultId | 1 |