dc.date.accessioned | 2023-03-01T17:42:26Z | |
dc.date.available | 2023-06-10T22:45:58Z | |
dc.date.created | 2023-01-27T16:49:30Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Holen, Martin Ruud, Else-Line Malene Warakagoda, Narada Dilp Granmo, Ole-Christoffer Engelstad, Paal E. Knausgård, Kristian Muri . Towards Using Reinforcement Learning for Autonomous Docking of Unmanned Surface Vehicles. Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science. 2022 Springer Nature | |
dc.identifier.uri | http://hdl.handle.net/10852/100544 | |
dc.description.abstract | Providing full autonomy to Unmanned Surface Vehicles (USV) is a challenging goal to achieve. Autonomous docking is a subtask that is particularly difficult. The vessel has to distinguish between obstacles and the dock, and the obstacles can be either static or moving. This paper developed a simulator using Reinforcement Learning (RL) to approach the problem. We studied several scenarios for the task of docking a USV in a simulator environment. The scenarios were defined with different sensor inputs and start-stop procedures but a simple shared reward function. The results show that the system solved the task when the IMU (Inertial Measurement Unit) and GNSS (Global Navigation Satellite System) sensors were used to estimate the state, despite the simplicity of the reward function. | |
dc.language | EN | |
dc.publisher | Springer Nature | |
dc.relation.ispartof | Communications in Computer and Information Science | |
dc.relation.ispartofseries | Communications in Computer and Information Science | |
dc.title | Towards Using Reinforcement Learning for Autonomous Docking of Unmanned Surface Vehicles | |
dc.title.alternative | ENEngelskEnglishTowards Using Reinforcement Learning for Autonomous Docking of Unmanned Surface Vehicles | |
dc.type | Chapter | |
dc.creator.author | Holen, Martin | |
dc.creator.author | Ruud, Else-Line Malene | |
dc.creator.author | Warakagoda, Narada Dilp | |
dc.creator.author | Granmo, Ole-Christoffer | |
dc.creator.author | Engelstad, Paal E. | |
dc.creator.author | Knausgård, Kristian Muri | |
cristin.unitcode | 185,15,30,30 | |
cristin.unitname | Seksjon for autonome systemer og sensorteknologier | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
dc.identifier.cristin | 2116935 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.btitle=Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science&rft.spage=&rft.date=2022 | |
dc.identifier.pagecount | 544 | |
dc.identifier.doi | https://doi.org/10.1007/978-3-031-08223-8_38 | |
dc.type.document | Bokkapittel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.isbn | 978-3-031-08223-8 | |
dc.type.version | AcceptedVersion | |
cristin.btitle | Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science | |