Adaptive Online Fault Diagnosis in Autonomous Robot Swarms

O’Keeffe, James, Tarapore, Danesh, Millard, Alan and Timmis, Jon (2018) Adaptive Online Fault Diagnosis in Autonomous Robot Swarms. Frontiers in Robotics and AI, 5 . p. 131. ISSN 2296-9144

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Item Type:Article
Item Status:Live Archive


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.

Keywords:swarm robotics, fault diagnosis, adaptive, autonomous, unsupervised learning
Divisions:College of Science > School of Computer Science
ID Code:43299
Deposited On:08 Dec 2020 10:58

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