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

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，使地球上所有的生命形式得以生存和繁荣。尽管如此，由于缺乏足够的海洋表面以下观测数据，目前对全球海洋活动和海洋健康的了解极为不足。 有待勘探的海洋面积与地下区域现有传感器数量之间的差距仍然非常巨大，使得大多数海洋尚未勘探。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基本要素。准确定位对于这些车辆执行智能感测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是不透明的，使得基于卫星的定位系统在水下不可用。为此，该项目将产生新的算法，使海洋机器人团队能够通过利用洋流作为定位参考，持续和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的机器人在流体环境中的足迹和自主性。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创造独特的机会（科学，技术，工程和数学）学生，特别是夏威夷土著人，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-该项目将产生一系列可扩展的算法，使移动的机器人团队能够协作导航和采样流体环境，基础设施支持。这些新的算法实例化无处不在的海洋协作机器人（合作机器人）的应用。研究目标包括：（i）协同流辅助导航算法，通过利用背景流的动力学知识来改善长期惯性导航性能;（ii）基于移动的机器人的现场流观测的物理信息、数据驱动的流体动力学学习方法;（iii）基于流体的同时定位和绘图（fluid-SLAM）方案，其能够实现并行的流辅助导航和流动力学学习，以及（iv）用于协作机器人团队的分散式协作流体SLAM算法。将在夏威夷附近的海洋环境中对合作流动辅助导航和流动动力学学习算法进行实地评估。单机器人和合作机器人流体SLAM算法将在模拟场景中使用室内合作机器人试验台进行评估，该试验台由一组纳米四旋翼组成。由此产生的协作机器人算法将从根本上提高移动的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价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.