CAREER: A New Paradigm in Control and Coordination of Robot Teams in Geophysical Flows

职业：地球物理流中机器人团队控制和协调的新范式

• 批准号: 1253917
• 负责人: Mongying Hsieh
• 金额: $ 25万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1253917&HistoricalAwards=false
• 关键词: CAREER; New; Paradigm; Control; Coordination

Little prior work in unmanned underwater vehicles (UUVs) has addressed the tight coupling that is inherent between the fluid dynamics of the body of water and the vehicle itself. In fact, the fluid dynamics of large, open bodies of water can be quite complex and this proposal addresses large coherent structures that naturally occur in the flow structure of these large bodies of water. The goals of this project are to extend the PI's ONR YIP award into the realm of 3-D, expanding the mathematical and control framework for distributed autonomous sensing and tracking of geophysical fluid dynamics and to understand the long-term impact of geophysical fluid dynamics to improve the autonomy of underwater vehicles. The key idea exploits the capability of the team to cover large regions to increase the spatio-temporal sampling resolution of the flow field. The data will then be processed in a distributed fashion to obtain a global description of the flow dynamics that can be maintained and updated in real-time. The specific objectives that expand prior proposals include not only the 3-D modeling, but the development of an energy efficient stochastic pulse controller for tracking the ridges of the coherent structures and expanding the estimation of the structures through stochastic approaches that allow the fusing of larger teams of sensing entities. The intellectual merit of the proposed work stems from the synthesis of nonlinear dynamical systems theory, transport theory, and robotics to develop a modeling, control, and analysis framework for collaborative unmanned systems operating in dynamic and uncertain environments. The information gleaned from the coherent structures will be used to refine motion control and resource allocation strategies to determine minimum-effort stochastic control policies for long-term operation in GFD environments. To the PI's knowledge, this is the first attempt to use robots to track and map unstable coherent structures in the ocean, and to exploit knowledge of them to improve the autonomy of AUVs/ASVs.Broader Impact: Success of these endeavors will improve the forecast of weather-climate systems, underwater transport dynamics, and the modeling and prediction of various other physical phenomena in geophysical flow environments. Since the proposed methods are very general and developed for continued operation in dynamic and uncertain environments, success of the proposed activities will likely increase the maneuverability and energy-efficiency of existing AUVs/ASVs; enable teams of AUVs/ASVs to continuously adapt to changing environmental conditions as they execute their assigned tasks; and provide greater situational awareness for various scientific, commercial, and military applications in the ocean. In addition, the proposed educational activities include a comprehensive plan to integrate the study of GFD into robotics through online educational modules for general K-12 audiences; an interdisciplinary undergraduate and graduate curriculum; and contributions to the robotics community in the form of open source software and hardware development tools.

无人水下航行器(UUV)以前的工作很少涉及水体的流体动力学和航行器本身之间固有的紧密耦合。事实上，大型开放水体的流体动力学可能相当复杂，这项建议解决了自然出现在这些大型水体的流动结构中的大型连贯结构。该项目的目标是将PI的ONR Yip奖扩展到3-D领域，扩大地球物理流体动力学分布式自主传感和跟踪的数学和控制框架，并了解地球物理流体动力学对提高水下机器人自主性的长期影响。关键思想是利用团队覆盖大区域的能力来提高流场的时空采样分辨率。然后，将以分布式方式处理数据，以获得可实时维护和更新的流动动态的全局描述。扩展先前建议的具体目标不仅包括3-D建模，还包括开发一种能量高效的随机脉冲控制器，用于跟踪相干结构的脊线，并通过允许融合更大的传感实体团队的随机方法来扩展对结构的估计。这项工作的智能优点来自于综合非线性动力系统理论、运输理论和机器人理论，为动态和不确定环境中的协作无人系统开发了一个建模、控制和分析框架。从相干结构中收集的信息将被用于改进运动控制和资源分配策略，以确定在GFD环境中长期运行的最小努力随机控制策略。据PI所知，这是第一次尝试使用机器人跟踪和绘制海洋中不稳定的相干结构，并利用这些结构的知识来提高AUV/ASV的自主性。广泛的影响：这些努力的成功将改善天气-气候系统的预测，水下运输动力学，以及地球物理流动环境中各种其他物理现象的建模和预测。由于拟议的方法非常通用，而且是为在动态和不确定的环境中继续运行而开发的，拟议活动的成功可能会增加现有AUV/ASV的机动性和能效；使AUV/ASV团队在执行其指定任务时能够不断适应不断变化的环境条件；并为海洋中的各种科学、商业和军事应用提供更好的态势感知。此外，拟议的教育活动包括一项全面计划，通过面向普通K-12受众的在线教育模块，将GFD的学习纳入机器人学；一个跨学科的本科生和研究生课程；以及以开放源码软件和硬件开发工具的形式向机器人界作出贡献。