ID | 64381 |
フルテキストURL | |
著者 |
Kawabe, Tomoya
Graduate School of Natural Science and Technology, Okayama University
Nishi, Tatsushi
Graduate School of Natural Science and Technology, Okayama University
ORCID
Kaken ID
researchmap
Liu, Ziang
Graduate School of Natural Science and Technology, Okayama University
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抄録 | The use of multiple mobile robots has grown significantly over the past few years in logistics, manufacturing and public services. Conflict-free route planning is one of the major research challenges for such mobile robots. Optimization methods such as graph search algorithms are used extensively to solve route planning problems. Those methods can assure the quality of solutions, however, they are not flexible to deal with unexpected situations. In this article, we propose a flexible route planning method that combines the reinforcement learning algorithm and a graph search algorithm for conflict-free route planning problems for multiple robots. In the proposed method, Q-learning, a reinforcement algorithm, is applied to avoid collisions using off-line learning with a limited state space to reduce the total learning time. Each vehicle independently finds the shortest route using the A* algorithm, and Q-learning is used to avoid collisions. The effectiveness of the proposed method is examined by comparing it with conventional methods in terms of computation time and the quality of solutions. Computational results show that for dynamic transportation problems, the proposed method can generate the solutions with approximately 10% of the computation time compared to the conventional Q-learning approach. We found that the required computation time is linearly increased with respect to the number of vehicles and nodes in the problems.
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キーワード | Automated Guided Vehicles
route planning
Q-learning
reinforcement learning
transportation
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発行日 | 2023-01-31
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出版物タイトル |
Applied Sciences
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巻 | 13巻
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号 | 3号
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出版者 | MDPI
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開始ページ | 1879
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ISSN | 2076-3417
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資料タイプ |
学術雑誌論文
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言語 |
英語
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OAI-PMH Set |
岡山大学
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著作権者 | © 2023 by the authors.
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論文のバージョン | publisher
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DOI | |
Web of Science KeyUT | |
関連URL | isVersionOf https://doi.org/10.3390/app13031879
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ライセンス | https://creativecommons.org/licenses/by/4.0/
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助成機関名 |
Japan Society for the Promotion of Science
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助成番号 | 22H01714
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