Statistical detection of faults in swarm robots under noisy conditions

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.