Timmis, Jonathan Ian orcid.org/0000-0003-1055-0471, Ismail, Amelia Ritahani Binti, Bjerknes, Jan D. et al. (1 more author) (2016) An Immune-Inspired Swarm Aggregation Algorithm for Self-Healing Swarm Robotic System. Biosystems. pp. 60-76. ISSN 0303-2647
Abstract
Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we propose an approach to self-healing swarm robotic systems and take inspiration from the process of granuloma formation, a process of containment and repair found in the immune system. We use a case study of a swarm performing team work where previous works have demonstrated that partially failed robots have the most detrimental effect on overall swarm behaviour. We have developed an immune inspired approach that permits the recovery from certain failure modes during operation of the swarm, overcoming issues that effect swarm behaviour associated with partially failed robots.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Elsevier Ireland Ltd. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Keywords: | Artificial immune systems,Self-repair,Swarm robotics |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Funding Information: | Funder Grant number THE ROYAL SOCIETY WM100041 EPSRC EP/E053505/1 |
Depositing User: | Pure (York) |
Date Deposited: | 26 May 2016 11:56 |
Last Modified: | 26 Nov 2024 00:31 |
Published Version: | https://doi.org/10.1016/j.biosystems.2016.04.001 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1016/j.biosystems.2016.04.001 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:100189 |