Adaptive Sampling of Algal Blooms Using an Autonomous Underwater Vehicle and Satellite Imagery

Fonseca, Joana; Rocha, Alexandre; Aguiar, Miguel; Johansson, Karl H.

doi:10.1109/ccta54093.2023.10252251

2023 IEEE Conference on Control Technology and Applications (CCTA) 2023

DOI: 10.1109/ccta54093.2023.10252251

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Adaptive Sampling of Algal Blooms Using an Autonomous Underwater Vehicle and Satellite Imagery

Joana Fonseca,

Alexandre Rocha,

Miguel Aguiar

et al.

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2024

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Efficient 3D real-time adaptive AUV sampling of a river plume front

Berild,

Ge,

Eidsvik

et al. 2024

Front. Mar. Sci.

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The coastal environment faces multiple challenges due to climate change and human activities. Sustainable marine resource management necessitates knowledge, and development of efficient ocean sampling approaches is increasingly important for understanding the ocean processes. Currents, winds, and freshwater runoff make ocean variables such as salinity very heterogeneous, and standard statistical models can be unreasonable for describing such complex environments. We employ a class of Gaussian Markov random fields that learns complex spatial dependencies and variability from numerical ocean model data. The suggested model further benefits from fast computations using sparse matrices, and this facilitates real-time model updating and adaptive sampling routines on an autonomous underwater vehicle. To justify our approach, we compare its performance in a simulation experiment with a similar approach using a more standard statistical model. We show that our suggested modeling framework outperforms the current state of the art for modeling such spatial fields. Then, the approach is tested in a field experiment using two autonomous underwater vehicles for characterizing the three-dimensional fresh-/saltwater front in the sea outside Trondheim, Norway. One vehicle is running an adaptive path planning algorithm while the other runs a preprogrammed path. The objective of adaptive sampling is to reduce the variance of the excursion set to classify freshwater and more saline fjord water masses. Results show that the adaptive strategy conducts effective sampling of the frontal region of the river plume.

show abstract

Efficient 3D real-time adaptive AUV sampling of a river plume front

Berild,

Ge,

Eidsvik

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

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Adaptive Sampling of Algal Blooms Using an Autonomous Underwater Vehicle and Satellite Imagery

Cited by 1 publication

References 17 publications

Efficient 3D real-time adaptive AUV sampling of a river plume front

Efficient 3D real-time adaptive AUV sampling of a river plume front

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