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We provide the first step toward developing a hierarchical control-estimationframework to actively plan robot trajectories for anomaly detection in confinedspaces. The space is represented globally using a directed region graph, wherea region is a landmark that needs to be visited (inspected). We devise a fastmixing Markov chain to find an ergodic route that traverses this graph so thatthe region visitation frequency is proportional to its anomaly detectionuncertainty, while satisfying the edge directionality (region transition)constraint(s). Preliminary simulation results show fast convergence to theergodic solution and confident estimation of the presence of anomalies in theinspected regions.

Active Anomaly Detection in Confined Spaces Using Ergodic Traversal of Directed Region Graphs