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

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.