Multi-vehicle adaptive 3D mapping for targeted ocean sampling

• Published in: PloS one 2024-05, Vol.19 (5), p.e0302514-e0302514
• Main Authors: Mo-Bjørkelund, Tore, Majaneva, Sanna, Fragoso, Glaucia Moreira, Johnsen, Geir, Ludvigsen, Martin
• Format: Article
• Language: English
• Subjects: Adaptive sampling; Algorithms; Analysis; Autonomous underwater vehicles; Autonomous vehicles; Biology and Life Sciences; Biomass; Biosensors; Chlorophyll; Chlorophyll - analysis; Chlorophyll A - analysis; Control; Data exchange; Decision making; Earth Sciences; Engineering and Technology; Environmental Monitoring - methods; Fluorescence; Health aspects; Identification and classification; Imaging, Three-Dimensional - methods; Mapping; Oceans; Oceans and Seas; Physical Sciences; Phytoplankton; Plankton; Plankton nets; Planning; Real time; Remote submersibles; Research and Analysis Methods; Robotics; Samplers; Sampling; Sampling methods; Satellite communications; Sensors; Simulation; Spatial distribution; Temporal distribution; Temporal variability; Time measurement; Uncertainty; Underwater vehicles; Utility functions; Water analysis; Water sampling
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0302514

Expanding spatial presentation from two-dimensional profile transects to three-dimensional ocean mapping is key for a better understanding of ocean processes. Phytoplankton distributions can be highly patchy and the accurate identification of these patches with the context, variability, and uncertainty of measurements on relevant scales is difficult to achieve. Traditional sampling methods, such as plankton nets, water samplers and in-situ vertical sensors, provide a snapshot and often miss the fine-scale horizontal and temporal variability. Here, we show how two autonomous underwater vehicles measured, adapted to, and reported real-time chlorophyll a measurements, giving insights into the spatiotemporal distribution of phytoplankton biomass and patchiness. To gain the maximum available information within their sensing scope, the vehicles moved in an adaptive fashion, looking for the regions of the highest predicted chlorophyll a concentration, the greatest uncertainty, and the least possibility of collision with other underwater vehicles and ships. The vehicles collaborated by exchanging data with each other and operators via satellite, using a common segmentation of the area to maximize information exchange over the limited bandwidth of the satellite. Importantly, the use of multiple autonomous underwater vehicles reporting real-time data combined with targeted sampling can provide better match with sampling towards understanding of plankton patchiness and ocean processes.