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Heterogeneous robot teams for modeling and prediction of multiscale environmental processes

Heterogeneous robot teams for modeling and prediction of multiscale environmental processes This paper presents a framework to enable a team of heterogeneous mobile robots to model and sense a multiscale system. We propose a coupled strategy, where robots of one type collect high-fidelity measurements at a slow time scale and robots of another type collect low-fidelity measurements at a fast time scale, for the purpose of fusing measurements together. The multiscale measurements are fused to create a model of a complex, nonlinear spatiotemporal process. The model helps determine optimal sensing locations and predict the evolution of the process. Key contributions are: (i) consolidation of multiple types of data into one cohesive model, (ii) fast determination of optimal sensing locations for mobile robots, and (iii) adaptation of models online for various monitoring scenarios. We illustrate the proposed framework by modeling and predicting the evolution of an artificial plasma cloud. We test our approach using physical marine robots adaptively sampling a process in a water tank. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

Heterogeneous robot teams for modeling and prediction of multiscale environmental processes

Salam, Tahiya; Hsieh, M. Ani
Autonomous Robots , Volume 47 (4) – Apr 1, 2023
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References (64)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1007/s10514-023-10089-6
Publisher site
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Abstract

This paper presents a framework to enable a team of heterogeneous mobile robots to model and sense a multiscale system. We propose a coupled strategy, where robots of one type collect high-fidelity measurements at a slow time scale and robots of another type collect low-fidelity measurements at a fast time scale, for the purpose of fusing measurements together. The multiscale measurements are fused to create a model of a complex, nonlinear spatiotemporal process. The model helps determine optimal sensing locations and predict the evolution of the process. Key contributions are: (i) consolidation of multiple types of data into one cohesive model, (ii) fast determination of optimal sensing locations for mobile robots, and (iii) adaptation of models online for various monitoring scenarios. We illustrate the proposed framework by modeling and predicting the evolution of an artificial plasma cloud. We test our approach using physical marine robots adaptively sampling a process in a water tank.

Journal

Autonomous Robots – Springer Journals

Published: Apr 1, 2023

Keywords: Environment monitoring; Heterogeneity; Multi-robot systems; Marine robots

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