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Title: | A review on COLREGs-compliant navigation of autonomous surface vehicles: From traditional to learning-based approaches |
Authors: | Hu, L Hu, H Naeem, W Wang, Z |
Keywords: | autonomous surface vehicle;collision avoidance;path re-planning;deep reinforcement learning |
Issue Date: | 3-Dec-2022 |
Publisher: | Elsevier on behalf of KeAi Communications Co. Ltd. |
Citation: | Hu, L. et al. (2022) Journal of Automation and Intelligence, 1 (1), 100003, pp. 1 - 11. doi: 10.1016/j.jai.2022.100003. |
Abstract: | Copyright © 2022 The Authors. 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. |
URI: | http://bura.brunel.ac.uk/handle/2438/26990 |
DOI: | http://dx.doi.org/10.1016/j.jai.2022.100003 |
Other Identifiers: | ORCID iD: Zidong Wang https://orcid.org/0000-0002-9576-7401 100003 |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | Copyright © 2022 The Authors. Published by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). | 1.06 MB | Adobe PDF | View/Open |
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