Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26990
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dc.contributor.authorHu, L-
dc.contributor.authorHu, H-
dc.contributor.authorNaeem, W-
dc.contributor.authorWang, Z-
dc.date.accessioned2023-08-18T12:49:38Z-
dc.date.available2022-12-09-
dc.date.available2023-08-18T12:49:38Z-
dc.date.issued2022-12-03-
dc.identifierORCID iD: Zidong Wang https://orcid.org/0000-0002-9576-7401-
dc.identifier100003-
dc.identifier.citationHu, L. et al. (2022) Journal of Automation and Intelligence, 1 (1), 100003, pp. 1 - 11. doi: 10.1016/j.jai.2022.100003.en_US
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/26990-
dc.description.abstractCopyright © 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.en_US
dc.description.sponsorshipThis work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the U.K., the Royal Society of the U.K.en_US
dc.format.extent1 - 11-
dc.languageEnglish-
dc.language.isoenen_US
dc.publisherElsevier on behalf of KeAi Communications Co. Ltd.en_US
dc.rightsCopyright © 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/).-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.subjectautonomous surface vehicleen_US
dc.subjectcollision avoidanceen_US
dc.subjectpath re-planningen_US
dc.subjectdeep reinforcement learningen_US
dc.titleA review on COLREGs-compliant navigation of autonomous surface vehicles: From traditional to learning-based approachesen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.jai.2022.100003-
dc.relation.isPartOfJournal of Automation and Intelligence-
pubs.issue1-
pubs.publication-statusPublished-
pubs.volume1-
dc.identifier.eissn2949-8554-
dc.rights.holderThe Authors-
Appears in Collections:Dept of Computer Science Research Papers

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