A Data-Driven Method for Metric Extraction to Detect Faults in Robot Swarms

Suet Lee*, Emma Milner, Sabine Hauert*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

10 Citations (Scopus)
159 Downloads (Pure)

Abstract

Robot swarms are increasingly deployed in real-world applications. Making swarms safe will be critical to improve adoption and trust. Fault detection is a useful component in systems which require a level of safety: a key element of which are metrics that allow us to differentiate between faulty and normal (non-faulty) robots - metrics which are measurable on-board the individual robots for self-detection of faults. In this paper, we develop a method for identifying and evaluating such metrics and discuss how these metrics may be used in building a model for fault detection. We demonstrate this method for real-time error detection in a realistic use-case: intralogistics using swarms. We show that we are able to identify metrics of large effect size for various faults, demonstrating the potency of metrics selected in this way with a simple fault detection model.
Original languageEnglish
Pages (from-to)10746-10753
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number4
Early online date11 Jul 2022
DOIs
Publication statusPublished - 1 Oct 2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • swarm robotics
  • probability and statistical methods
  • fault detection
  • metric extraction

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