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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Adaptive Online Fault Diagnosis in Autonomous Robot Swarms

O'Keeffe, James, Tarapore, Danesh, Millard, Alan G. and Timmis, Jonathan (2018) Adaptive Online Fault Diagnosis in Autonomous Robot Swarms. Frontiers in Robotics and AI, 5. p. 131.

Item Type: Article

Abstract

Previous work has shown that robot swarms are not always tolerant to the failure of individual robots, particularly those that have only partially failed and continue to contribute to collective behaviors. A case has been made for an active approach to fault tolerance in swarm robotic systems, whereby the swarm can identify and resolve faults that occur during operation. Existing approaches to active fault tolerance in swarms have so far omitted fault diagnosis, however we propose that diagnosis is a feature of active fault tolerance that is necessary if swarms are to obtain long-term autonomy. This paper presents a novel method for fault diagnosis that attempts to imitate some of the observed functions of natural immune system. The results of our simulated experiments show that our system is flexible, scalable, and improves swarm tolerance to various electro-mechanical faults in the cases examined.

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More Information

Depositing User: Jonathan Timmis

Identifiers

Item ID: 11858
URI: http://sure.sunderland.ac.uk/id/eprint/11858
Official URL: https://www.frontiersin.org/article/10.3389/frobt....

Users with ORCIDS

Catalogue record

Date Deposited: 24 Mar 2020 14:20
Last Modified: 24 Mar 2020 14:20

Contributors

Author: James O'Keeffe
Author: Danesh Tarapore
Author: Alan G. Millard
Author: Jonathan Timmis

University Divisions

Faculty of Technology > School of Computer Science

Subjects

Computing > Artificial Intelligence

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