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Information‐driven robotic sampling in the coastal ocean
Author(s) -
Fossum Trygve Olav,
Eidsvik Jo,
Ellingsen Ingrid,
Alver Morten Omholt,
Fragoso Glaucia Moreira,
Johnsen Geir,
Mendes Renato,
Ludvigsen Martin,
Rajan Kanna
Publication year - 2018
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.21805
Subject(s) - sampling (signal processing) , buoy , underwater , upwelling , water column , environmental science , oceanography , coupling (piping) , computer science , remote sensing , meteorology , marine engineering , geology , engineering , geography , telecommunications , mechanical engineering , detector
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.