Motion Guidance for Ocean Sampling by Underwater Vehicles using Autonomous Control and Oceanographic Models with Forecast Uncertainty

该项目解决了如何选择最佳位置来收集观测结果以及如何确保传感器平台在海洋等广阔的动态过程中沿着沿着信息路径行进到这些位置的基本问题。拟议研究的意义在于观察到气候过程发生在长时间尺度上。了解这些过程需要海洋模型和观测的结合，这些模型和观测可以由高续航力的自主潜艇舰队在大时空范围内收集，这些潜艇可以智能地转向，以最大限度地利用其测量结果。对海洋内部进行采样的水下航行器对于了解一般海洋过程非常重要，因为与大气中的天气预测不同，水下海洋环境很难远程采样。因此，该项目的长期目标是为无人驾驶的移动的传感器平台创建新的路径规划策略，以测量海洋中信息丰富但采样不足的动态过程。事实上，在这个项目中开发的方法将很容易转移到业务的数据同化systems.The研究的具体目标是应用工具，从数据同化，非线性控制，动力系统理论设计采样轨迹的准确估计和预测的环流系统的涡流。技术方法是（1）将基于确定性动力系统理论的拉格朗日分析与通过蒙特卡罗方法适当考虑不确定性的数据同化技术相结合;（2）使用基于主动奇异点的非线性反馈控制的流体启发策略构建理论上合理的多飞行器控制框架;以及（3）制定一个最佳采样框架，以利用流场几何形状的拉格朗日描述符生成长航时飞行器轨迹，同时使流场可观测性最大化。 知识的意义在于预期贡献的实践拉格朗日数据同化和非线性反馈控制的自主采样平台的指导，通过纳入不确定性。拉格朗日分析方法非常适合于对具有自主传感器平台的确定性动态系统进行采样，因为它们使用平台运动作为传感器测量，并且不需要板载流量传感器。通过放松观测系统的确定性假设，基于集合的概率规划方法与拉格朗日分析有望提高估计过程的预测精度。基于人工流动潜力的路径规划算法的原则性设计将允许采样平台尽可能利用洋流的潜在运动，以最大限度地提高耐力。

This project addresses fundamental questions on how to select the optimal locations to collect observations and how to ensure that the sensor platforms travel to these locations along informative paths in an expansive, dynamic process such as the ocean. The significance of the proposed research lies in the observation that climate processes occur on long time scales. Understanding these processes requires a combination of ocean models and observations, which can be collected over large space-time volumes by fleets of high-endurance autonomous submarines that steer intelligently to maximize the utility of their measurements. Underwater vehicles that sample the ocean interior are important for understanding ocean processes in general, because -- unlike weather prediction in the atmosphere -- the subsurface ocean environment is difficult to sample remotely. Thus, the long-term goal of this project is create new path-planning strategies for unmanned, mobile sensor platforms to measure information-rich but undersampled dynamic processes in the ocean. Indeed the methods developed in this project will be readily transferrable to operational data assimilation systems.The specific objective of the research is to apply tools from data assimilation, nonlinear control, and dynamical systems theory to design sampling trajectories for accurate estimation and prediction of circulating ocean currents represented by a system of vortices. The technical approach is to (1) synthesize Lagrangian analysis based on deterministic, dynamical systems theory with data assimilation techniques that properly account for uncertainty via a Monte Carlo approach; (2) construct a theoretically justified framework for multi-vehicle control using a fluids-inspired strategy based on the nonlinear feedback control of active singularities; and (3) formulate an optimal sampling framework to generate long-endurance vehicle trajectories using Lagrangian descriptors of the flow geometry while maximizing flowfield observability. The intellectual significance lies in the anticipated contributions to the practice of Lagrangian data assimilation and nonlinear feedback control for the guidance of autonomous sampling platforms by incorporating uncertainty. Lagrangian analysis methods are well suited for sampling deterministic dynamical systems with autonomous sensor platforms because they use platform motion as a sensor measurement and do not require onboard flow sensors. By relaxing the deterministic assumptions of the observing system, an ensemble-based, probabilistic approach to planning with Lagrangian analysis promises to improve the accuracy of the forecast of the estimated processes. A principled design of path-planning algorithms based on artificial flow potentials will allow sampling platforms to exploit whenever possible the underlying motion of ocean currents to maximize endurance.