HMM-based anomaly interpretation for intelligent robots in Industry 4.0

We apply an anomaly detection method based on Hidden Markov Models and Hellinger distance to a Kairos mobile robot operating in the ICE lab, a research laboratory for Industry 4.0. Two main contributions are proposed: i) a decomposition of the Hellinger distance which allows to identify the causes of anomalous behaviours detected, ii) a graphical user interface that synchronously shows the robot movements in a map and the evolution of the Hellinger distance components, allowing a quick investigation of the causes of the detected anomalies. The tools are applied to a real-world dataset allowing to discover that an anomalous movement of the Kairos robot is caused by a wrong reading of the lidar from a window in the environment.