Miyauchi, G. orcid.org/0000-0002-3349-6765, Talamali, M.S. and Gross, R. (2024) A comparative study of energy replenishment strategies for robot swarms. In: Hamann, H., Dorigo, M., Cáceres, L.P., Reina, A., Kuckling, J, Kaiser, T.K., Soorati, M., Hasselmann, K. and Buss, E., (eds.) Swarm Intelligence: 14th International Conference, ANTS 2024, Konstanz, Germany, October 9–11, 2024, Proceedings. International Conference on Swarm Intelligence (ANTS 2024), 09-11 Oct 2024, Konstanz, Germany. Lecture Notes in Computer Science, LNCS 14987 . Springer , pp. 3-15. ISBN 9783031709319
Abstract
To enable long-term operations of swarms of energy-constrained robots, they need to manage both their in-flow and out-flow of energy. We consider two strategies for doing so: In the first strategy, all robots work at a remote location but due to their limited storage capacity must return to charge. In the second strategy, dedicated mobile chargers with finite storage capacity deliver energy to the remote location, substantially shortening the worker robots' commute. We compare the work performed and the energy efficiency of these strategies using physics-based simulations and reveal conditions under which their performance is close to theoretically derived upper bounds. We assess several factors, including the number of mobile chargers, their storage capacity, transfer losses, and the ratio of energy expended while working and traveling. Our findings confirm that mobile chargers can help increase the work performed, and even overall energy efficiency provided that their energy storage is larger than that of workers.
Metadata
Item Type: | Proceedings Paper |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. Except as otherwise noted, this author-accepted version of a paper published in Swarm Intelligence: 14th International Conference, ANTS 2024, Konstanz, Germany, October 9–11, 2024, Proceedings is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number UK RESEARCH AND INNOVATION 10048272 101093046 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Jul 2024 10:24 |
Last Modified: | 16 Sep 2024 16:15 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-70932-6_1 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214864 |