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

使用具有预测不确定性的自主控制和海洋模型的水下航行器海洋采样运动指导

• 批准号: 1362837
• 负责人: Derek Paley
• 金额: $ 50.46万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1362837&HistoricalAwards=false
• 关键词: Motion; Guidance; Ocean; Sampling; Underwater

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.

该项目涉及以下基本问题：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海洋等广阔、动态的过程中沿着信息丰富的路径到达这些位置。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。理解这些过程需要海洋模型和观测的结合，这些数据可以由高耐力自主潜艇舰队在大的时空体积上收集，这些潜艇智能地驾驶，以最大限度地发挥其测量的效用。对海洋内部进行采样的水下航行器对了解总体上的海洋过程很重要，因为--与大气中的天气预报不同--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移动传感器平台创建新的路径规划战略，以测量海洋中信息丰富但采样不足的动态过程。这项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的工具来设计采样轨迹，以准确地估计和预报以涡系表示的循环洋流。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数据同化技术相结合，通过蒙特卡罗方法适当地解释不确定性；(2)使用基于有源奇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车辆控制框架；以及(3)利用流动几何的拉格朗日描述符，在最大化流场可观测性的同时，制定最优采样框架，以生成长期持续的车辆轨迹。其理论意义在于，通过引入不确定性，期望对拉格朗日数据同化和非线性反馈控制的实践做出贡献，以指导自主采样平台的工作。拉格朗日分析方法适用于具有自主传感器平台的确定性动态系统的采样，因为它们使用平台运动作为传感器测量，并且不需要机载流量传感器。通过放松观测系统的确定性假设，基于系综的概率规划方法和拉格朗日分析有望提高对估计过程的预测精度。基于人工流势的路径规划算法的原则性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。