RII Track-4: Data-Driven Navigation, Path Planning, and Coordination of Mobile Robots in Fluids

自主移动的机器人，如无人驾驶航空器和水下航行器，正在成为越来越多的服务于国家利益的应用中的一个基本要素。这些智能平台可以为天气预报和持续监测大尺度动力事件的形成和发展提供关键的现场测量数据。导航和感测动态流体环境的能力是小型移动的机器人在强地球物理流（例如飓风或洋流）中的基础。这些能力需要机器人感知，探索和理解背景流动动力学，这些动力学通常不符合基于第一原理的经验模型，挑战了现有的机器人自主方法。本计画旨在利用资料驱动的系统动力学建模、评估与控制，以提升动态流体环境中移动的机器人的导航、路径规划与协调能力。该奖学金项目的目标是通过在华盛顿大学进行数据驱动动力学建模和控制技术的强化培训，理顺PI的机器人研究计划，并为夏威夷大学数据科学和机器人系统的研究和教育能力带来长期持续的改进。该项目推进了机器人导航，路径规划和流体环境中的协调，这是全球海洋传感和天气预报的基础。所提出的研究有助于机器人自主性的基础相结合的物理信息，数据驱动的建模与经典的控制和估计的基础上的第一原理。研究目标是：（1）利用概率推理、动态压缩传感和非线性动力学稀疏识别为基于流体的同时定位和映射建立理论基础，（2）利用模型预测控制揭示非定常流场中有限时域最优轨迹与底层相干流结构之间的联系，以及（3）使用数据驱动的动力学学习来识别非稳态流体中的最优群集规律和涌现群集动力学。培训和协同目标是建立相互的学生共同咨询关系，为夏威夷大学开发新的本科课程“工程师数据科学”，并与华盛顿大学的电子科学研究所和应用物理实验室开展新的合作。预期成果包括但不限于PI和主机之间的长期合作，联合期刊出版物，新课程的Lightboard讲座视频系列，以及在夏威夷共同开发开放式海洋机器人测试平台的战略计划。该项目的影响力将通过联合出版物、合作提案开发、学生共同建议以及夏威夷大学和华盛顿大学在数据科学研究和教育方面的合作来维持。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响力审查标准进行评估来支持。

Autonomous mobile robots, such as unmanned aerial and underwater vehicles, are becoming an essential element in an increasing number of applications that serve the national interest. These intelligent platforms can provide critical in-situ measurement data for weather forecasting and persistent monitoring of the forming and developing of large-scale dynamic events. The capabilities to navigate and sense dynamic fluid environments are fundamental to miniature mobile robots in strong geophysical flows such as hurricanes or ocean currents. These capabilities require the robots to sense, explore, and understand background flow dynamics that are often not amenable to empirical models based on first principles, challenging the existing approaches for robot autonomy. This project aims at enhancing the navigation, path planning, and coordination of mobile robots in dynamic fluid environments by using data-driven system dynamics modeling, estimation, and control. The goals of this fellowship project are to straighten the PI’s robotics research program with intensive training on data-driven dynamics modeling and control techniques at the University of Washington, and bring long-term sustained improvements to the research and education capacity of the University of Hawai‘i system on data science and robotics. This project advances robot navigation, path planning, and coordination in fluid environments, which are fundamental for global ocean sensing and weather forecasting. The proposed research contributes to the foundation of robot autonomy by combing physics-informed, data-driven modeling with classical control and estimation based on first principles. The research objectives are to (1) build the theoretical foundation for fluid-based simultaneous localization and mapping using probabilistic inference, dynamic compressed sensing, and sparse identification of nonlinear dynamics, (2) uncover the connection between finite-horizon optimal trajectories in unsteady flow fields and the underlying coherent flow structures using model predictive control, and (3) identify the optimal swarming laws and emergent swarm dynamics in unsteady fluids using data-driven dynamics learning. The training and synergistic objectives are to establish a mutual student co-advising relationship, develop a new undergraduate course on “Data Science for Engineers” for the University of Hawai‘i, and initiate new collaborations with the eScience Institute and the Applied Physics Laboratory at the University of Washington. The expected outcomes include but are not limited to long-lasting collaborations between the PI and the host, joint journal publications, lightboard lecture videos series for the new course, and strategic plans for co-developing an open-access marine robotics testbed in Hawai‘i. The project impact will be sustained through joint publications, collaborative proposal development, student co-advising, and collaborations between the University of Hawai‘i and the University of Washington on data science research and education.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.