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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

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
Depositing User: Jonathan Timmis
URI: https://sure.sunderland.ac.uk/id/eprint/11858

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11858

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Date Deposited
24 Mar 2020 14:20
Last Modified
04 Jun 2025 14:59

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

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