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: 1923940
• 负责人: Mongying Hsieh
• 金额: $ 1.68万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923940&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.

此前有关无人水下航行器 (UUV) 的工作几乎没有解决水体流体动力学与航行器本身之间固有的紧密耦合问题。事实上，大型开放水体的流体动力学可能非常复杂，该提案解决了这些大型水体的流动结构中自然出现的大型连贯结构。该项目的目标是将 PI 的 ONR YIP 奖项扩展到 3D 领域，扩展用于分布式自主传感和地球物理流体动力学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航行器的自主性。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，数据将以分布式方式进行处理，以获得可以实时维护和更新的流动动力学的全局描述。扩展先前提案的具体目标不仅包括 3D 建模，还包括开发节能随机脉冲控制器，用于跟踪相干结构的脊，并通过随机方法扩展结构的估计，从而允许融合更大的结构。传感实体团队。该工作的智力价值源于非线性动力系统理论、运输理论和机器人技术的综合，为动态和不确定环境中运行的协作无人系统开发建模、控制和分析框架。从连贯结构中收集的信息将用于完善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，这是首次尝试使用机器人跟踪和绘制海洋中不稳定的相干结构，并利用它们的知识来提高 AUV/ASV 的自主性。 更广泛的影响：这些努力的成功将改善预测天气气候系统、水下输运动力学以及地球物理流环境中各种其他物理现象的建模和预测。由于所提出的方法非常通用，并且是为在动态和不确定的环境中持续运行而开发的，因此所提出的活动的成功可能会提高现有 AUV/ASV 的机动性和能源效率；使 AUV/ASV 团队能够在执行分配的任务时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强的态势感知能力。此外，拟议的教育活动包括一项综合计划，通过面向一般 K-12 受众的在线教育模块，将 GFD 研究融入机器人技术；跨学科的本科生和研究生课程；并以开源软件和硬件开发工具的形式为机器人社区做出贡献。