Hu, Liang and Hu, Huosheng and Naeem, Wasif and Wang, Zidong (2022) A review on COLREGs-compliant navigation of autonomous surface vehicles: From traditional to learning-based approaches. Journal of Automation and Intelligence, 1 (1). pp. 1-11. DOI https://doi.org/10.1016/j.jai.2022.100003
Hu, Liang and Hu, Huosheng and Naeem, Wasif and Wang, Zidong (2022) A review on COLREGs-compliant navigation of autonomous surface vehicles: From traditional to learning-based approaches. Journal of Automation and Intelligence, 1 (1). pp. 1-11. DOI https://doi.org/10.1016/j.jai.2022.100003
Hu, Liang and Hu, Huosheng and Naeem, Wasif and Wang, Zidong (2022) A review on COLREGs-compliant navigation of autonomous surface vehicles: From traditional to learning-based approaches. Journal of Automation and Intelligence, 1 (1). pp. 1-11. DOI https://doi.org/10.1016/j.jai.2022.100003
Abstract
A growing interest in developing autonomous surface vehicles (ASVs) has been witnessed during the past two decades, including COLREGs-compliant navigation to ensure safe autonomy of ASVs operating in complex waterways. This paper reviews the recent progress in COLREGs-compliant navigation of ASVs from traditional to learning-based approaches. It features a holistic viewpoint of ASV safe navigation, namely from collision detection to decision making and then to path replanning. The existing methods in all these three stages are classified according to various criteria. An in-time overview of the recently-developed learning-based methods in motion prediction and path replanning is provided, with a discussion on ASV navigation scenarios and tasks where learning-based methods may be needed. Finally, more general challenges and future directions of ASV navigation are highlighted.
Item Type: | Article |
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Uncontrolled Keywords: | Autonomous surface vehicle; Collision avoidance; Path re-planning; Deep reinforcement learning |
Divisions: | Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 05 Dec 2024 10:41 |
Last Modified: | 05 Dec 2024 10:41 |
URI: | http://repository.essex.ac.uk/id/eprint/38992 |
Available files
Filename: J Automation and Intelligence-2022-100003.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0