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.

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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.

Item Type: Article
Subjects: Computing > Artificial Intelligence
Divisions: Faculty of Technology > School of Computer Science
Depositing User: Jonathan Timmis
Date Deposited: 24 Mar 2020 14:20
Last Modified: 24 Mar 2020 14:20
URI: http://sure.sunderland.ac.uk/id/eprint/11858

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