Abstract
We seek methods to model, control, and analyze robot teams performing environmental monitoring tasks. During environmental monitoring, the goal is to have teams of robots collect various data throughout a fixed region for extended periods of time. Standard bottom-up task assignment methods do not scale as the number of robots and task locations increases and require computationally expensive replanning. Alternatively, top-down methods have been used to combat computational complexity, but most have been limited to the analysis of methods which focus on transition times between tasks. In this work, we study a class of nonlinear macroscopic models which we use to control a time-varying distribution of robots performing different tasks throughout an environment. Our proposed ensemble model and control maintains desired time-varying populations of robots by leveraging naturally occurring interactions between robots performing tasks. We validate our approach at multiple fidelity levels including experimental results, suggesting the effectiveness of our approach to perform environmental monitoring.
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Acknowledgement
We gratefully acknowledge the support of ARL DCIST CRA W911NF-17-2-0181, Office of Naval Research (ONR) Award No. N00014-22-1-2157, and the National Defense Science & Engineering Graduate (NDSEG) Fellowship Program.
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Edwards, V., Silva, T.C., Hsieh, M.A. (2024). Stochastic Nonlinear Ensemble Modeling and Control for Robot Team Environmental Monitoring. In: Bourgeois, J., et al. Distributed Autonomous Robotic Systems. DARS 2022. Springer Proceedings in Advanced Robotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-031-51497-5_7
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