Scalable Randomized Scheduling of Mobile Sensors with Observability Guarantees

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Autonomous aerial, ground, and underwater robots have emerged as promising platforms for a myriad of sensing applications. Aerial drones can monitor natural calamities like tornadoes and forest fires, and underwater robots can detect harmful algal blooms and track invasive fish species. The information to be gathered is typically governed by some nonlinear dynamic processes. A natural question to ask is, given a limited number of mobile sensors, how should they be dynamically placed to best observe the quantity of interest, especially with limited energy while requiring the robots to remain connected? The space of possible sensor placements is vast and constraints on energy and connectivity add to the challenges. This project will develop efficient algorithms to schedule the mobile sensors under these constraints and evaluate their performance in tracking a moving target through field experiments. The interdisciplinary nature of this research will be integrated with outreach and educational activities to broaden participation of K-12 and undergraduate students, especially from underrepresented groups.The project combines the investigators’ complementary expertise in control, network theory and fast randomized computation as the project will result in a fresh perspective and a generalizable, principled framework for scalable scheduling of mobile sensors with observability guarantees. The project goals will be realized through four integrated research thrusts that span theoretical investigation, algorithmic development, and experimental validation. Thrust 1 focuses on integrating a Gramian-based nonlinear observability metric with randomized sampling for efficient computation of near-optimal sensor placements under sensing and communication uncertainty. Thrust 2 extends the framework to accommodate energy and connectivity constraints, where special emphasis will be on distributed approaches for computation. Motivated by the fish-tracking application, the theory and algorithms developed in Thrusts 1 and 2 will be validated experimentally with a fleet of autonomous surface vehicles tracking a moving acoustic tag. Thrust 3 of the project involves the development of the experimental testbed, including the robots and their associated models and controllers, while Thrust 4 evaluates the developed mobile sensor scheduling algorithms via both simulation and field experiments in Higgins Lake, Michigan.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.