Adaptive sampling algorithms for multiple autonomous underwater vehicles

TLDR

Sampling underwater phenomena with AUVs requires optimal path planning and resource‑efficient strategies to effectively capture spatial distributions of variables such as salinity, temperature, and dissolved oxygen. The authors developed adaptive sampling algorithms that use information measures, estimation theory, and potential fields to guide robots toward high‑information locations, and validated them in the MATCON simulation, a solar‑powered AUV platform, and a wheeled‑robot testbed.

Abstract

Sampling is a critical problem in the observation of underwater phenomena using single or multiple AUV platforms. The determination of optimal paths and sampling strategies that effectively utilize available resources is critical to these missions. Recent work performed jointly at RPI and AUSI on the development of adaptive sampling algorithms (ASA) utilizes information measures, estimation theory, and potential fields to direct the robots to the locations in space most likely to yield information about the sensed field variable of interest. Typical sensory information can consist of spatial distribution of one or more field variables, such as salinity, dissolved oxygen, temperature, current, etc. In order to test our algorithms we have created the MATCON simulation environment, an underwater experimental platform using solar AUVs, and a land-based experimental testbed using inexpensive wheeled robots.