Immune-Inspired Fault Diagnosis for Robot Swarms

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 behaviours. 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 this thesis proposes that fault diagnosis is a feature of active fault tolerance that is necessary if robot swarms are to achieve long-term autonomy. This thesis presents a novel method for fault diagnosis in robot swarms that attempts to imitate some of the observed functions of natural immune system. The experimental results presented in this thesis, which were obtained in software simulation and with actual robot hardware, show that the proposed fault diagnosis system is flexible, scalable, and improves swarm tolerance to various electro-mechanical faults in the cases examined.