Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-64107-2_31 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Fault diagnosis,Feature vector,Behaviour characterisation,Swarm robotics | Feature vector,Fault detection and isolation,Robustness (computer science),Artificial intelligence,Engineering,Robot,Swarm robotics,Scalability | Conference |
Volume | ISSN | Citations |
10454 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James O'Keeffe | 1 | 1 | 1.04 |
| Danesh Tarapore | 2 | 169 | 10.76 |
| Alan G. Millard | 3 | 24 | 5.94 |
| Jon Timmis | 4 | 1237 | 120.32 |