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dc.contributor.authorMartinsen, Andreas Bell
dc.contributor.authorLekkas, Anastasios M.
dc.contributor.authorGros, Sebastien
dc.date.accessioned2020-11-13T12:24:45Z
dc.date.available2020-11-13T12:24:45Z
dc.date.created2020-11-06T09:37:26Z
dc.date.issued2020
dc.identifier.citationFrontiers in Robotics and AI. 2020, .en_US
dc.identifier.issn2296-9144
dc.identifier.urihttps://hdl.handle.net/11250/2687805
dc.description.abstractWe present a reinforcement learning-based (RL) control scheme for trajectory tracking of fully-actuated surface vessels. The proposed method learns online both a model-based feedforward controller, as well an optimizing feedback policy in order to follow a desired trajectory under the influence of environmental forces. The method's efficiency is evaluated via simulations and sea trials, with the unmanned surface vehicle (USV) ReVolt performing three different tracking tasks: The four corner DP test, straight-path tracking and curved-path tracking. The results demonstrate the method's ability to accomplish the control objectives and a good agreement between the performance achieved in the Revolt Digital Twin and the sea trials. Finally, we include an section with considerations about assurance for RL-based methods and where our approach stands in terms of the main challenges.en_US
dc.language.isoengen_US
dc.publisherFrontiers Mediaen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleReinforcement Learning-Based Tracking Control of USVs in Varying Operational Conditionsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber16en_US
dc.source.journalFrontiers in Robotics and AIen_US
dc.identifier.doi10.3389/frobt.2020.00032
dc.identifier.cristin1845498
dc.description.localcodeCopyright © 2020 Martinsen, Lekkas, Gros, Glomsrud and Pedersen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
cristin.ispublishedtrue
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Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal