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Autonomous boat driving system using sample-efficient model predictive control-based reinforcement learning approach
http://hdl.handle.net/10061/0002000126
http://hdl.handle.net/10061/00020001269705ec53-4e63-4bd1-b464-b13bd1399c93
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||
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| 公開日 | 2024-02-16 | |||||||||
| タイトル | ||||||||||
| タイトル | Autonomous boat driving system using sample-efficient model predictive control-based reinforcement learning approach | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | learning | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | marine robotics | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ | journal article | |||||||||
| アクセス権 | ||||||||||
| アクセス権 | open access | |||||||||
| 著者 |
Cui, Yunduan
× Cui, Yunduan
× Osaki, Shigeki
× 松原, 崇充 |
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | In this article, we propose a novel reinforcement learning (RL) approach specialized for autonomous boats: sample-efficient probabilistic model predictive control (SPMPC), to iteratively learn control policies of boats in real ocean environments without human prior knowledge. SPMPC addresses difficulties arising from large uncertainties in this challenging application and the need for rapid adaptation to dynamic environmental conditions, and the extremely high cost of exploring and sampling with a real vessel. SPMPC combines a Gaussian process model and model predictive control under a model-based RL framework to iteratively model and quickly respond to uncertain ocean environments while maintaining sample efficiency. A SPMPC system is developed with features including quadrant-based action search rule, bias compensation, and parallel computing that contribute to better control capabilities. It successfully learns to control a full-sized single-engine boat equipped with sensors measuring GPS position, speed, direction, and wind, in a real-world position holding task without models from human demonstration. | |||||||||
| 書誌情報 |
en : Journal of Field Robotics 巻 38, 号 3, p. 331-354, 発行日 2020-09-25 |
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| 出版者 | ||||||||||
| 出版者 | Wiley | |||||||||
| ISSN | ||||||||||
| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 1556-4967 | |||||||||
| 出版者版DOI | ||||||||||
| 関連タイプ | isReplacedBy | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1002/rob.21990 | |||||||||
| 出版者版URI | ||||||||||
| 関連タイプ | isReplacedBy | |||||||||
| 識別子タイプ | URI | |||||||||
| 関連識別子 | https://onlinelibrary.wiley.com/doi/10.1002/rob.21990 | |||||||||
| 権利 | ||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||
| 権利情報 | $00A9 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,provided the original work is properly cited. | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | NA | |||||||||
