Optimal path planning for a ship in coastal waters with deep Q network
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Title
- Optimal path planning for a ship in coastal waters with deep Q network
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Author(s)
- Lee, Hyeong-Tak; Kim, Min Kyu
- KIOST Author(s)
- Lee, Hyeong-Tak(이형탁); Kim, Min Kyu(김민규)
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Alternative Author(s)
- 이형탁; 김민규
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Publication Year
- 2024-09
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Abstract
- With the development of autonomous ships, complete automation of passage planning has become an imminent priority. However, existing A* algorithms have the disadvantage of generating paths close to land because they prioritize minimizing the navigation distance. Therefore, this study proposes a method based on a deep Q network to effectively implement reward-and-penalty strategies considering required navigation areas and non-navigable areas throughout the port-to-port distance. First, the Busan and Gwangyang Ports were selected as the target areas and a container ship was selected as the target ship. Initially, non-navigable and reward areas were designated based on the water depth and electronic navigational chart information. Thereafter, we conducted experiments using algorithms in three types of environments: normal conditions, turbulent weather, and obstacle-involved environments. Furthermore, we employed the Douglas–Peucker algorithm to eliminate excessive waypoints. Experimental results demonstrated that the path planning of a ship obtained using a deep Q network involved more efficient and safer decisions for ship navigation. Furthermore, the navigation distance was reduced by 1.77% compared to the passage plan used by actual ships. The proposed approach is advantageous for automatically deriving the optimal mid-range path of ships and can thus contribute toward improving maritime safety and efficiency.
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ISSN
- 0029-8018
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URI
- https://sciwatch.kiost.ac.kr/handle/2020.kiost/45563
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DOI
- 10.1016/j.oceaneng.2024.118193
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Bibliographic Citation
- Ocean Engineering, v.307, 2024
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Publisher
- Pergamon Press Ltd.
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Keywords
- ShipOptimal path planningCoastal watersDeep Q network
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Type
- Article
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Language
- English
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