The coastal ocean is a dynamic and complex environment due to the confluence of atmospheric, oceanographic, estuarine/riverine, and land-sea interactions. Yet it continues to be undersampled, resulting in poor understanding of dynamic, episodic, and complex phenomena such as harmful algal blooms, anoxic zones, coastal plumes, thin layers, and frontal zones. Often these phenomena have no viable biological or computational models that can provide guidance for sampling. Returning targeted water samples for analysis becomes critical for biologists to assimilate data for model synthesis. In our work, the scientific emphasis on building a species distribution model necessitates spatially distributed sample collection from within hotspots in a large volume of a dynamic field of interest. To do so, we propose an autonomous approach to sample acquisition based on an online calculation of sample utility. A series of reward functions provide a balance between temporal and spatial scales of oceanographic sampling and do so in such a way that science preferences or evolving knowledge about the feature of interest can be incorporated in the decision process. This utility calculation is undertaken onboard a powered autonomous underwater vehicle (AUV) with specialized water samplers for the upper water column. For validation, we provide experimental results using archival AUV data along with an at-sea demonstration in Monterey Bay, CA.
- Autonomous underwater vehicles (AUVs)
ASJC Scopus subject areas
- Ocean Engineering
- Mechanical Engineering
- Electrical and Electronic Engineering