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

• DOI: 10.3389/fmars.2023.1319719
• Publication Date: 2024-01-17T13:08:47Z
• Authors: Martin Outzen Berild; Yaolin Ge; Jo Eidsvik; Geir-Arne Fuglstad; Ingrid Ellingsen
• Keywords: Adaptive sampling

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