Adaptive Sampling and Reduced-Order Modeling of Dynamic Processes by Robot Teams

Abstract

This letter presents a strategy to enable a team of mobile robots to adaptively sample and track a dynamic process. We propose a distributed strategy, where robots collect sparse sensor measurements, create a reduced-order model of a spatio-temporal process, and use this model to estimate field values for areas without sensor measurements of the dynamic process. The robots then use these estimates of the field, or inferences about the process, to adapt the model and reconfigure their sensing locations. The key contributions of this process are twofold: first, leveraging the dynamics of the process of interest to determine where to sample and how to estimate the process; and second, maintaining fully distributed models, sensor measurements, and estimates of the time-varying process. We illustrate the application of the proposed solution in simulation and compare it to centralized and global approaches. We also test our approach with physical marine robots sampling a process in a water tank.