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.
