Talamali, M.S., Saha, A. orcid.org/0000-0002-1685-4057, Marshall, J.A.R. orcid.org/0000-0002-1506-167X et al. (1 more author) (2021) When less is more: Robot swarms adapt better to changes with constrained communication. Science Robotics, 6 (56). eabf1416. ISSN 2470-9476
Abstract
To effectively perform collective monitoring of dynamic environments, a robot swarm needs to adapt to changes by processing the latest information and discarding outdated beliefs. We show that in a swarm composed of robots relying on local sensing, adaptation is better achieved if the robots have a shorter rather than longer communication range. This result is in contrast with the widespread belief that more communication links always improve the information exchange on a network. We tasked robots with reaching agreement on the best option currently available in their operating environment. We propose a variety of behaviors composed of reactive rules to process environmental and social information. Our study focuses on simple behaviors based on the voter model—a well-known minimal protocol to regulate social interactions—that can be implemented in minimalistic machines. Although different from each other, all behaviors confirm the general result: The ability of the swarm to adapt improves when robots have fewer communication links. The average number of links per robot reduces when the individual communication range or the robot density decreases. The analysis of the swarm dynamics via mean-field models suggests that our results generalize to other systems based on the voter model. Model predictions are confirmed by results of multiagent simulations and experiments with 50 Kilobot robots. Limiting the communication to a local neighborhood is a cheap decentralized solution to allow robot swarms to adapt to previously unknown information that is locally observed by a minority of the robots.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. This is an author-produced version of a paper subsequently published in Science Robotics. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 647704 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Oct 2021 11:16 |
Last Modified: | 19 Oct 2021 11:16 |
Status: | Published |
Publisher: | American Association for the Advancement of Science (AAAS) |
Refereed: | Yes |
Identification Number: | 10.1126/scirobotics.abf1416 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179392 |