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dc.contributor.authorMeyer, Eivind
dc.contributor.authorHeiberg, Amalie
dc.contributor.authorRasheed, Adil
dc.contributor.authorSan, Omer
dc.date.accessioned2021-09-07T05:22:21Z
dc.date.available2021-09-07T05:22:21Z
dc.date.created2020-06-18T03:17:37Z
dc.date.issued2020
dc.identifier.citationIEEE Access. 2020, 8 165344-165364.en_US
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/11250/2773871
dc.description.abstractPath Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters that can only be determined experimentally. The rise of deep reinforcement learning in recent years suggests an alternative approach: end-to-end learning of the optimal guidance policy from scratch by means of a trial-and-error based approach. In this article, we explore the potential of Proximal Policy Optimization, a deep reinforcement learning algorithm with demonstrated state-of-the-art performance on Continuous Control tasks, when applied to the dual-objective problem of controlling an autonomous surface vehicle in a COLREGs compliant manner such that it follows an a priori known desired path while avoiding collisions with other vessels along the way. Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios where the ultimate success of the agent rests upon its ability to navigate non-uniform marine terrain while handling challenging, but realistic vessel encounters.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.urihttps://ieeexplore.ieee.org/document/9187823
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleCOLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learningen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber165344-165364en_US
dc.source.volume8en_US
dc.source.journalIEEE Accessen_US
dc.identifier.doi10.1109/access.2020.3022600
dc.identifier.cristin1816043
dc.relation.projectNorges forskningsråd: 295033en_US
cristin.ispublishedfalse
cristin.fulltextpostprint
cristin.qualitycode1


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