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.

中文翻译：

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在静态海洋环境中使用自主水下航行器进行自适应采样

本文探讨了配备传感器的自动水下航行器（AUV）的使用，以构建水质模型以帮助评估重要的环境危害，例如与点源污染物或局部低氧区域有关的环境危害。我们的重点是需要实时自主发现和密集采样关键问题的问题，由于AUV的强大功能和限制任务持续时间的计算限制，因此标准（例如，基于网格的）策略不可行。为此，我们考虑对标量场的高斯过程（GP）随机模型进行自适应采样策略，以将采样重点放在最有前途和信息量最大的区域。具体来说，这项研究采用GP上限置信区间作为优化标准，以适应性地规划在勘探与开发之间权衡取舍的采样路径。提出了两种基于（i）分支定界技术和（ii）交叉熵优化的信息路径规划算法，用于在考虑采样平台的运动约束的同时选择未来的采样位置。在模拟标量场中探索提出的方法的有效性，以识别三维环境中的多个感兴趣区域。使用AUV进行的野外实验，使用已知标量场上的虚拟测量值和原位溶解氧测量值来研究低氧区域，验证了该方法能够快速探索给定区域，