dc.contributor.author | Fossum, Trygve Olav | |
dc.contributor.author | Eidsvik, Jo | |
dc.contributor.author | Ellingsen, Ingrid H. | |
dc.contributor.author | Alver, Morten | |
dc.contributor.author | Fragoso, Glaucia Moreira | |
dc.contributor.author | Johnsen, Geir | |
dc.contributor.author | mendes, renato | |
dc.contributor.author | Ludvigsen, Martin | |
dc.contributor.author | Rajan, Kanna | |
dc.date.accessioned | 2018-08-08T11:24:47Z | |
dc.date.available | 2018-08-08T11:24:47Z | |
dc.date.created | 2018-07-29T05:28:48Z | |
dc.date.issued | 2018-06-08 | |
dc.identifier.citation | Wiley online library | nb_NO |
dc.identifier.issn | 1556-4959 | |
dc.identifier.uri | http://hdl.handle.net/11250/2508043 | |
dc.description.abstract | Efficient sampling of coastal ocean processes, especially mechanisms such as upwelling and internal waves and their influence on primary production, is critical for understanding our changing oceans. Coupling robotic sampling with ocean models provides an effective approach to adaptively sample such features. We present methods that capitalize on information from ocean models and in situ measurements, using Gaussian process modeling and objective functions, allowing sampling efforts to be concentrated to regions with high scientific interest. We demonstrate how to combine and correlate marine data from autonomous underwater vehicles, model forecasts, remote sensing satellite, buoy, and ship‐based measurements, as a means to cross‐validate and improve ocean model accuracy, in addition to resolving upper water‐column interactions. Our work is focused on the west coast of Mid‐Norway where significant influx of Atlantic Water produces a rich and complex physical–biological coupling, which is hard to measure and characterize due to the harsh environmental conditions. Results from both simulation and full‐scale sea trials are presented. | nb_NO |
dc.description.sponsorship | Nansen Legacy Program, Grant/AwardNumber:27272; Senter for Autonome Marine Operasjoner og Systemer,Grant/Award Number: 223254; Norges Forskningsråd,Grant/Award Number: 255303/E40; European Union's Seventh Framework Programme(FP7/2007–2013), Grant/Award Number: 270180 | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Wiley Periodicals, Inc. | nb_NO |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.subject | Gaussian processes | nb_NO |
dc.subject | Marine robotics | nb_NO |
dc.subject | Ocean modeling | nb_NO |
dc.subject | Ocean sampling | nb_NO |
dc.subject | Robotic sampling | nb_NO |
dc.title | Information-driven robotic sampling in the coastal ocean | nb_NO |
dc.title.alternative | Information-driven robotic sampling in the coastal ocean | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | publishedVersion | nb_NO |
dc.rights.holder | © The Authors 2018. Journal of Field Robotics published by Wiley Periodicals, Inc. | nb_NO |
dc.source.journal | Journal of Field Robotics | nb_NO |
dc.identifier.doi | 10.1002/rob.21805 | |
dc.identifier.cristin | 1598879 | |
cristin.unitcode | 7566,6,0,0 | |
cristin.unitname | Miljø og nye ressurser | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |