NRI: FND: Collaborative Navigation, Learning, and Collaboration in Fluids with Application to Ubiquitous Marine Co-Robots

NRI：FND：流体中的协作导航、学习和协作及其在无处不在的海洋协作机器人中的应用

• 批准号: 2024928
• 负责人: Zhuoyuan Song
• 金额: $ 39.48万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024928&HistoricalAwards=false
• 关键词: NRI; FND; Collaborative; Navigation; Learning

As the lifeblood of Earth, the ocean shapes and regulates global weather patterns, maintaining the perfect balance of chemistry and temperature to allow all Earth's life-forms to survive and thrive. Nonetheless, the current understanding of global ocean activities and ocean health is extremely inadequate due to the lack of sufficient observation data below the ocean surface. The gap between the large ocean volumes to explore and the number of existing sensors in subsurface regions remains astonishingly huge, leaving the majority of the oceans unexplored. Small-size autonomous underwater vehicles are becoming essential elements in persistent and pervasive ocean sensing and monitoring. Accurate localization is of utmost importance for these vehicles to perform intelligent sensing and control as well as for the users to properly interpret the vehicles' measurements. However, underwater localization is notoriously challenging since the ocean is opaque to radio frequency signals, rendering the satellite-based positioning systems unavailable underwater. To this end, this project will result in novel algorithms that enable teams of marine robots to persistently and collaboratively navigate the under-explored ocean volumes by utilizing ocean flows as localization references. This project will fundamentally increase the footprint and autonomy of mobile robots in fluid environments. The project outcomes will benefit several pertinent research areas including oceanography, marine ecology, and meteorology. Furthermore, the project will create unique opportunities for STEM (science, technology, engineering, and mathematics) students, especially Native Hawaiians, to recognize the great potentials of robotics, gain experience with marine robots, and participate in cross-disciplinary research activities.The project will result in a series of scalable algorithms that enable teams of mobile robots to collaboratively navigate and sample fluid environments with minimal infrastructural support. These novel algorithms are instantiated with the application of ubiquitous marine collaborative robots (co-robots). The research objectives include (i) a collaborative flow-aided navigation algorithm that improves the long-term inertial navigation performance by utilizing the knowledge about the dynamics of background flows; (ii) a physics-informed, data-driven fluid dynamics learning method based on in-situ flow observations by mobile robots; (iii) a fluid-based simultaneous localization and mapping (fluid-SLAM) scheme that enables concurrent flow-aided navigation and flow dynamics learning, and (iv) a decentralized cooperative fluid-SLAM algorithm for teams of co-robots. Field evaluations of the cooperative flow-aided navigation and flow dynamics learning algorithms will be conducted in ocean environments near Hawaii. The single-robot and co-robot fluid-SLAM algorithms will be evaluated in simulated scenarios using an indoor co-robot testbed consisting of a fleet of nano-quadrotors. The resulting co-robot algorithms will fundamentally advance the adaptability and robustness of mobile co-robots in distributed sensing and collaborative learning in uncertain and unstructured environments.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.

作为地球的命脉，海洋塑造和调节全球天气模式，保持化学和温度的完美平衡，使地球上所有生命形式都能生存和繁荣。尽管如此，由于缺乏足够的海面以下观测数据，目前对全球海洋活动和海洋健康的了解还极其不足。 待探索的大片海洋与地下区域现有传感器的数量之间的差距仍然惊人地巨大，导致大部分海洋尚未被探索。小型自主水下航行器正在成为持续和普遍的海洋传感和监测的重要组成部分。准确的定位对于这些车辆执行智能传感和控制以及用户正确解释车辆的测量结果至关重要。然而，水下定位是众所周知的挑战，因为海洋对射频信号不透明，导致基于卫星的定位系统在水下无法使用。为此，该项目将产生新颖的算法，使海洋机器人团队能够利用海洋流动作为定位参考，持续协作地导航未开发的海洋体积。该项目将从根本上增加移动机器人在流体环境中的占地面积和自主性。该项目的成果将有利于几个相关的研究领域，包括海洋学、海洋生态学和气象学。此外，该项目将为 STEM（科学、技术、工程和数学）学生，特别是夏威夷原住民，创造独特的机会，让他们认识到机器人技术的巨大潜力，获得海洋机器人的经验，并参与跨学科研究活动。该项目将产生一系列可扩展的算法，使移动机器人团队能够在最少的基础设施支持下协作导航和采样流体环境。这些新颖的算法通过无处不在的海洋协作机器人（co-robots）的应用来实例化。研究目标包括（i）一种协作流辅助导航算法，该算法利用有关背景流动力学的知识来提高长期惯性导航性能； (ii) 基于移动机器人现场流动观测的物理知识、数据驱动的流体动力学学习方法； (iii) 基于流体的同步定位和建图 (fluid-SLAM) 方案，可实现并发流动辅助导航和流动动力学学习，以及 (iv) 用于协作机器人团队的分散式协作流体 SLAM 算法。协同流动辅助导航和流动动力学学习算法的现场评估将在夏威夷附近的海洋环境中进行。单机器人和协作机器人流体 SLAM 算法将使用由一组纳米四旋翼飞行器组成的室内协作机器人测试台在模拟场景中进行评估。由此产生的协作机器人算法将从根本上提高移动协作机器人在不确定和非结构化环境中的分布式传感和协作学习中的适应性和鲁棒性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势进行评估，被认为值得支持以及更广泛的影响审查标准。

项目成果

Path Planning for Optimal Coverage of Areas with Nonuniform Importance

重要性不均匀区域最优覆盖的路径规划

• DOI: 10.2514/6.2022-2546
• 发表时间: 2021-10-19
• 期刊: AIAA SCITECH 2022 Forum
• 作者: Gregory F. Snyder;Sachin Shriwastav;Dylan Morrison;Zhuoyuan Song
• 通讯作者: Zhuoyuan Song

Finite-horizon, energy-efficient trajectories in unsteady flows

非定常流动中的有限视野、节能轨迹

• DOI: 10.1098/rspa.2021.0255
• 发表时间: 2022-03
• 期刊: Proceedings. Mathematical, Physical, and Engineering Sciences
• 作者: Krishna K;Song Z;Brunton SL
• 通讯作者: Brunton SL