Adaptive sampling with an autonomous underwater vehicle in static marine environments

摘要

This paper explores the use of autonomous underwater vehicles (AUVs) equipped with sensors to construct water quality models to aid in the assessment of important environmental hazards, for instance related to point-source pollutants or localized hypoxic regions. Our focus is on problems requiring the autonomous discovery and dense sampling of critical areas of interest in real-time, for which standard (e.g., grid-based) strategies are not practical due to AUV power and computing constraints that limit mission duration. To this end, we consider adaptive sampling strategies on Gaussian process (GP) stochastic models of the measured scalar field to focus sampling on the most promising and informative regions. Specifically, this study employs the GP upper confidence bound as the optimization criteria to adaptively plan sampling paths that balance a trade-off between exploration and exploitation. Two informative path planning algorithms based on (ⅰ) branch-and-bound techniques and (ⅱ) cross-entropy optimization are presented for choosing future sampling locations while considering the motion constraints of the sampling platform. The effectiveness of the proposed methods are explored in simulated scalar fields for identifying multiple regions of interest within a three-dimensional environment. Field experiments with an AUV using both virtual measurements on a known scalar field and in situ dissolved oxygen measurements for studying hypoxic zones validate the approach's capability to quickly explore the given area, and then subsequently increase the sampling density around regions of interest without sacrificing model fidelity of the full sampling area.