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Mission Planning and Decision Support for Underwater Glider Networks: A Sampling on-Demand Approach

机译:水下滑翔机网络的任务计划和决策支持:按需采样方法

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This paper describes an optimal sampling approach to support glider fleet operators and marine scientists during the complex task of planning the missions of fleets of underwater gliders. Optimal sampling, which has gained considerable attention in the last decade, consists in planning the paths of gliders to minimize a specific criterion pertinent to the phenomenon under investigation. Different criteria (e.g., A, G, or E optimality), used in geosciences to obtain an optimum design, lead to different sampling strategies. In particular, the A criterion produces paths for the gliders that minimize the overall level of uncertainty over the area of interest. However, there are commonly operative situations in which the marine scientists may prefer not to minimize the overall uncertainty of a certain area, but instead they may be interested in achieving an acceptable uncertainty sufficient for the scientific or operational needs of the mission. We propose and discuss here an approach named sampling on-demand that explicitly addresses this need. In our approach the user provides an objective map, setting both the amount and the geographic distribution of the uncertainty to be achieved after assimilating the information gathered by the fleet. A novel optimality criterion, called A η , is proposed and the resulting minimization problem is solved by using a Simulated Annealing based optimizer that takes into account the constraints imposed by the glider navigation features, the desired geometry of the paths and the problems of reachability caused by ocean currents. This planning strategy has been implemented in a Matlab toolbox called SoDDS (Sampling on-Demand and Decision Support). The tool is able to automatically download the ocean fields data from MyOcean repository and also provides graphical user interfaces to ease the input process of mission parameters and targets. The results obtained by running SoDDS on three different scenarios are provided and show that SoDDS, which is currently used at NATO STO Centre for Maritime Research and Experimentation (CMRE), can represent a step forward towards a systematic mission planning of glider fleets, dramatically reducing the efforts of glider operators.
机译:本文介绍了在计划水下滑翔机船队任务这一复杂任务期间,为滑翔机船队操作者和海洋科学家提供支持的最佳采样方法。最佳采样在过去十年中引起了广泛关注,其中包括规划滑翔机的路径,以最大程度地减少与所研究现象有关的特定标准。在地球科学中用于获得最佳设计的不同标准(例如A,G或E最优性)导致了不同的采样策略。尤其是,A准则为滑翔机提供了使感兴趣区域内的总体不确定性水平最小化的路径。但是,在通常的操作情况下,海洋科学家可能不希望不降低某个区域的总体不确定性,而是可能会对获得足以满足任务的科学或操作需求的可接受的不确定性感兴趣。我们在这里提出并讨论一种名为“按需采样”的方法,该方法可以明确解决这一需求。在我们的方法中,用户提供了一个客观地图,设置了吸收车队收集的信息后要实现的不确定性的数量和地理分布。提出了一种称为Aη的新的最优性准则,并通过使用基于模拟退火的优化器解决了最小化问题,该优化器考虑了滑翔机导航功能所施加的约束,所需的路径几何形状以及所引起的可达性问题通过洋流。该计划策略已在名为SoDDS(按需采样和决策支持)的Matlab工具箱中实现。该工具能够从MyOcean信息库自动下载海洋数据,还提供图形用户界面,以简化任务参数和目标的输入过程。提供了通过在三种不同情况下运行SoDDS所获得的结果,并表明,目前在北约STO海事研究与实验中心(CMRE)使用的SoDDS可以代表朝着滑翔机队的系统任务计划迈出的一步,从而大大减少了滑行机队滑翔机操作员的努力。

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