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 language | English |
---|---|
Pages (from-to) | 10746-10753 |
Number of pages | 8 |
Journal | IEEE Robotics and Automation Letters |
Volume | 7 |
Issue number | 4 |
Early online date | 11 Jul 2022 |
DOIs | |
Publication status | Published - 1 Oct 2022 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- swarm robotics
- probability and statistical methods
- fault detection
- metric extraction