Adaptive online fault diagnosis in autonomous robot swarms

James O'Keeffe*, Danesh Tarapore, Alan G. Millard, Jon Timmis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (SciVal)

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.

Original languageEnglish
Article number131
JournalFrontiers Robotics AI
Volume5
Issue numberNOV
DOIs
Publication statusPublished - 01 Nov 2018

Keywords

  • Adaptive
  • Autonomous
  • Fault diagnosis
  • Swarm robotics
  • Unsupervised learning

Fingerprint

Dive into the research topics of 'Adaptive online fault diagnosis in autonomous robot swarms'. Together they form a unique fingerprint.

Cite this