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

该项目解决了一些基本问题，如如何选择最佳位置来收集观测数据，以及如何确保传感器平台在广阔的动态过程（如海洋）中沿着信息路径到达这些位置。提出的研究的意义在于观察到气候过程发生在很长的时间尺度上。了解这些过程需要结合海洋模型和观测结果，这些数据可以由高续航能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引导，以最大限度地利用它们的测量结果。一般来说，对海洋内部进行采样的水下航行器对了解海洋过程很重要，因为与大气中的天气预报不同，地下海洋环境很难进行远程采样。因此，该项目的长期目标是为无人驾驶的移动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，在这个项目中发展的方法将很容易转移到业务数据同化系统。研究的具体目标是应用数据同化、非线性控制和动力系统理论等工具设计采样轨迹，以精确估计和预测以涡旋系统为代表的环流。技术方法是：(1)综合基于确定性的拉格朗日分析，动态系统理论与数据同化技术，通过蒙特卡罗方法适当地解释不确定性；(2)基于主动奇异点非线性反馈控制的流体激励策略，构建了理论上合理的多飞行器控制框架；(3)在最大化流场可观测性的前提下，利用流几何的拉格朗日描述符制定最佳采样框架以生成长航时飞行器轨迹。其智力意义在于对拉格朗日数据同化和非线性反馈控制的实践的预期贡献，这些控制通过纳入不确定性来指导自主采样平台。拉格朗日分析方法非常适合采样具有自主传感器平台的确定性动力系统，因为它们使用平台运动作为传感器测量，而不需要机载流量传感器。通过放宽观测系统的确定性假设，一种基于集合的、概率的方法来规划与拉格朗日分析有望提高估计过程预测的准确性。基于人工流势的路径规划算法的原则设计将允许采样平台尽可能地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.