@inproceedings{bb8e6e7f11c9495d94113df4473f754c,
title = "Towards fault diagnosis in robot swarms: An online behaviour characterisation approach",
abstract = "Although robustness has been cited as an inherent advantage of swarm robotics systems, it has been shown that this is not always the case. Fault diagnosis will be critical for future swarm robotics systems if they are to retain their advantages (robustness, flexibility and scalability). In this paper, existing work on fault detection is used as a foundation to propose a novel approach for fault diagnosis in swarms based on a behavioural feature vector approach. Initial results show that behavioural feature vectors can be used to reliably diagnose common electro-mechanical fault types in most cases tested.",
keywords = "Behaviour characterisation, Fault diagnosis, Feature vector, Swarm robotics",
author = "James O{\textquoteright}Keeffe and Danesh Tarapore and Millard, {Alan G.} and Jon Timmis",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017 ; Conference date: 19-07-2017 Through 21-07-2017",
year = "2017",
doi = "10.1007/978-3-319-64107-2_31",
language = "English",
isbn = "9783319641065",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "393--407",
editor = "Yang Gao and Saber Fallah and Yaochu Jin and Constantina Lekakou",
booktitle = "Towards Autonomous Robotic Systems - 18th Annual Conference, TAROS 2017, Proceedings",
address = "Switzerland",
}