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 . ISSN 2296-9144


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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