Obute, SO, Kilby, P, Dogar, MR orcid.org/0000-0002-6896-5461 et al. (1 more author) (2020) RepAtt: Achieving Swarm Coordination through Chemotaxis. In: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE 2020). 16th International Conference on Automation Science and Engineering (CASE 2020), 20-21 Aug 2020, Online. Institute of Electrical and Electronics Engineers ISBN 978-1-7281-6905-7
Abstract
Swarm foraging is a common test case application for multi-robot systems. In this paper we present a novel algorithm for improving coordination of a robot swarm by selectively broadcasting repulsion and attraction signals. Robots use a chemotaxis-inspired search behaviour based on the temporal gradients of these signals in order to navigate towards more advantageous areas. Hardware experiments were used to model and validate realistic, noisy sound communication. We then show through extensive simulation studies that our chemotaxis-based coordination algorithm significantly improves swarm foraging time and robot efficiency.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © IEEE 2020. This is an author produced version of a paper published in 16th International Conference on Automation Science and Engineering (CASE 2020) Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Jul 2020 13:37 |
Last Modified: | 09 Dec 2020 06:28 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/CASE48305.2020.9216993 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163130 |