Pinciroli, C., Talamali, M.S., Reina, A. et al. (2 more authors) (2018) Simulating Kilobots within ARGoS: models and experimental validation. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A., Reina, A. and Trianni, V., (eds.) Swarm Intelligence (ANTS 2018). 11th International Conference on Swarm Intelligence (ANTS 2018), 29-31 Oct 2018, Rome, Italy. Lecture Notes in Computer Science , 11172 . Springer , pp. 176-187. ISBN 978-3-030-00532-0
Abstract
The Kilobot is a popular platform for swarm robotics research due to its low cost and ease of manufacturing. Despite this, the effort to bootstrap the design of new behaviours and the time necessary to develop and debug new behaviours is considerable. To make this process less burdensome, high-performing and flexible simulation tools are important. In this paper, we present a plugin for the ARGoS simulator designed to simplify and accelerate experimentation with Kilobots. First, the plugin supports cross-compiling against the real robot platform, removing the need to translate algorithms across different languages. Second, it is highly configurable to match the real robot behaviour. Third, it is fast and allows running simulations with several hundreds of Kilobots in a fraction of real time. We present the design choices that drove our work and report on experiments with physical robots performed to validate simulated behaviours.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2018 Springer. This is an author produced version of a paper subsequently published in Swarm Intelligence (LNCS 11172). 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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Jul 2018 15:10 |
Last Modified: | 16 Nov 2018 14:41 |
Published Version: | https://doi.org/10.1007/978-3-030-00533-7_14 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-030-00533-7_14 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133194 |