This paper considers the anomaly detection of mobile robots in the consensus control, whose objective is to that states of all agents in a multi-agents system converge to common state value by exchanging information over a network. In the presented scheme, one class support vector machine, that is one of unsupervised learning approaches, is used and multiple classifiers are designed to deal with two cases: i) a failed robot does not move from the initial position and ii) a failed robot continues to move in a certain direction. Simulation and experiments with omnidirectional mobile robots show that anomaly detection accuracy is improved by using both two classifiers.
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