Anomaly Detection in Multi-robot Systems Exploiting Self-Awareness

Self-awareness is intelligent agents’ capability to become aware of new experiences using their sensory data. This paper focuses on anomaly detection following principles of self-awareness and proposes coupled hierarchical dynamic Bayesian networks (DBN) as causal–temporal models to learn cooperative multi-robot behaviors from sensory data. These trained models allow anomaly detection whenever an observed behavior deviates from the learned behavior. We evaluated our approach with a two-drone leader–follower setup where the drones with GPS and LIDAR sensors conduct different maneuvers. Our simulation study shows that coupled DBNs trained with independent sensory data achieve better anomaly detection than DBNs trained with aggregated sensory data.