Statistical detection of faults in swarm robots under noisy conditions
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberCRG4-258
Permanent link to this recordhttp://hdl.handle.net/10754/656138
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AbstractFault 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.
CitationHarrou, F., Khaldi, B., Sun, Y., & Cherif, F. (2018). Statistical detection of faults in swarm robots under noisy conditions. 2018 6th International Conference on Control Engineering & Information Technology (CEIT). doi:10.1109/ceit.2018.8751862
SponsorsThis 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.
Conference/Event name2018 6th International Conference on Control Engineering & Information Technology (CEIT)