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Training and Research Experiences in Nonlinear Dynamics
Motion Guidance for Underwater Vehicles Using Autonomous Control and Oceanographic Models with Forecast Uncertainty
This project addresses fundamental questions on how to select the optimal locations to collect observations and how to ensure that the sensor platforms travel to these locations along informative paths in an expansive, dynamic system such as the ocean. The significance of the proposed research lies in the observation that climate processes occur on long timescales. Understanding these processes requires a combinatio of ocean models and observations, which can be collected over large space-time volumes by fleets of high-endurance, autonomous submarines that steer intelligently to maximize the utility of their measurements. Underwater vehicles that sample the ocean's interior are important for understanding the ocean processes in general because - unlike weather prediction in the atmosphere - the subsurface ocean environment is difficult to sample remotely. Thus, the long-term goal of this project is to create new path-planning strategies for unmanned, mobile sensor platforms to measure information-rich but under-sampled dynamic processes in the ocean. Participants will apply tools from data assimilation, nonlinear control, and dynamical systems theory to design sampling trajectories for the accurate estimation and prediction of circulating ocean currents.
Professor Paley has worked with over a dozen undergraduate students in his Collective Dynamics and Control Laboratory, including three TREND students.