3-D Adaptive AUV Sampling for Classification of Water Masses

Autonomous underwater vehicles with onboard computing units foster innovative approaches for sampling oceanographic phenomena. Feedback of observations via the onboard model for planning algorithms enable adaptive sampling for such robotic units. In this work, we develop, implement, and test an adap...

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Bibliographic Details
Published in:IEEE journal of oceanic engineering 2023-07, Vol.48 (3), p.1-14
Main Authors: Ge, Yaolin, Eidsvik, Jo, Mo-Bjorkelund, Tore
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive algorithms
Adaptive sampling
Algorithms
Autonomous underwater vehicles
autonomous underwater vehicles (AUVs)
Computational modeling
Data models
excursion sets (ES)
Fields (mathematics)
Mathematical analysis
Numerical models
Ocean models
Oceans
path planning
Plankton
river plume
River plumes
Rivers
Salinity
Salinity (geophysical)
Underwater vehicles
Water masses
Workflow
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Summary:Autonomous underwater vehicles with onboard computing units foster innovative approaches for sampling oceanographic phenomena. Feedback of observations via the onboard model for planning algorithms enable adaptive sampling for such robotic units. In this work, we develop, implement, and test an adaptive sampling algorithm for efficient sampling of water masses in a 3-D frontal system. Focusing on a river plume, salinity variations are used to characterize the water masses. A threshold in salinity is assumed to distinguish the ocean and river waters, so that excursions below the threshold define river waters. The onboard model builds on a Gaussian random field representation of the salinity variations in (north, east, depth) coordinates. This model is initially trained from numerical ocean model data, and then updated with data gathered by the vehicle sensor. The Gaussian random field model further allows closed-form expressions of the expected spatially integrated Bernoulli variance of the salinity excursion set, which is used to reward sampling efforts. Combining these results with forward-looking planning algorithms, we suggest a workflow for 3-D adaptive sampling to map river plume systems. Simulation studies are used to compare the suggested approach with others. Results of field trials in the Nidelva river plume in Norway are presented and discussed.
ISSN:0364-9059
1558-1691
DOI:10.1109/JOE.2023.3252641