Abstract
Fault detection plays an important role in supervising the operation of robotic swarm systems. If faults are not detected, they can considerably affect the performance of the robot swarm. In this paper, we present a robust fault detection mechanism against noise and uncertainties in data, by merging the multiresolution representation of data using wavelets with the sensitivity to small changes of an exponentially weighted moving average scheme. Specifically, to monitor swarm robotics systems performing a virtual viscoelastic control model for circle formation task, the proposed mechanism is applied to the uncorrelated residuals form principal component analysis model. Monitoring results using a simulation data from ARGoS simulator demonstrate that the proposed method achieves improved fault detection performances compared with the conventional approach.
| Original language | English (US) |
|---|---|
| Title of host publication | 2018 6th International Conference on Control Engineering & Information Technology (CEIT) |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| ISBN (Print) | 9781538676417 |
| DOIs | |
| State | Published - Oct 2018 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): CRG4-258
Acknowledgements: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The work is done in collaboration with the LESIA Laboratory. Department of Computer Science, University of Mohamed Khider, Biskra, Algeria.