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Using an autonomous underwater vehicle with onboard stochastic advection-diffusion models to map excursion sets of environmental variables
Environmetrics ( IF 1.7 ) Pub Date : 2021-08-18 , DOI: 10.1002/env.2702
Karine Hagesæther Foss 1 , Gunhild Elisabeth Berget 2, 3 , Jo Eidsvik 1
Affiliation  

New robotic sensor platforms have computing resources that enable a rich set of tasks for adaptive monitoring of the environment. But to substantially augment the toolbox of environmental sensing, such platforms must be embedded with realistic statistical models and coherent methodologies for designing experiments and assimilating the data. In this article, we develop myopic and hybrid strategies for autonomous underwater vehicle sampling in space and time. These strategies are based on a stochastic advection-diffusion Gaussian process model for the mine tailings concentration in a Norwegian fjord, and the goal is to monitor the excursion set (ES) of high concentrations. Closed form expressions for the expected misclassification probabilities of the ES enable real-time operation on board the autonomous vehicle, and this is used to guide the spatio-temporal sampling. Simulation studies show that the suggested strategies outperform other approaches that either (i) simplify the models for spatio-temporal variation, or (ii) simplify the design criterion. A field test shows how autonomous underwater sampling is useful for refining an initial stochastic advection-diffusion model. These experiments further show that the vehicle can adapt to focus on regions with intermediate concentrations where it is natural to improve the ES prediction.

中文翻译:

使用带有机载随机平流扩散模型的自主水下航行器来绘制环境变量的偏移集

新的机器人传感器平台具有计算资源,可以为环境的自适应监测提供丰富的任务集。但要大幅增加环境传感工具箱,此类平台必须嵌入现实的统计模型和用于设计实验和吸收数据的连贯方法。在本文中,我们为自主水下航行器在空间和时间上的采样制定了近视和混合策略。这些策略基于挪威峡湾尾矿浓度的随机平流-扩散高斯过程模型,目标是监测高浓度的偏移集 (ES)。ES 的预期错误分类概率的封闭形式表达式能够在自动驾驶车辆上进行实时操作,这用于指导时空采样。模拟研究表明,建议的策略优于其他方法,即(i)简化时空变化模型,或(ii)简化设计标准。现场测试显示了自主水下采样如何有助于改进初始随机平流扩散模型。这些实验进一步表明,车辆可以适应专注于中等浓度的区域,在这些区域可以自然地改善 ES 预测。现场测试显示了自主水下采样如何有助于改进初始随机平流扩散模型。这些实验进一步表明,车辆可以适应专注于中等浓度的区域,在这些区域可以自然地改善 ES 预测。现场测试显示了自主水下采样如何有助于改进初始随机平流扩散模型。这些实验进一步表明,车辆可以适应专注于中等浓度的区域,在这些区域可以自然地改善 ES 预测。
更新日期:2021-08-18
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