Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and Monitoring
This work addresses the challenge of efficient anomaly detection in environmental monitoring for autonomous systems, offering an incremental improvement over existing methods.
The paper tackled the problem of guiding an unmanned autonomous vehicle to detect extreme anomalies in unknown environments by showing that common criteria are ill-suited and introducing novel criteria that leverage previous measurements. The approach demonstrated superiority in applications like seafloor topography reconstruction and dynamic anomaly tracking, with the ability to overcome adversarial conditions.
An unmanned autonomous vehicle (UAV) is sent on a mission to explore and reconstruct an unknown environment from a series of measurements collected by Bayesian optimization. The success of the mission is judged by the UAV's ability to faithfully reconstruct any anomalous features present in the environment, with emphasis on the extremes (e.g., extreme topographic depressions or abnormal chemical concentrations). We show that the criteria commonly used for determining which locations the UAV should visit are ill-suited for this task. We introduce a number of novel criteria that guide the UAV towards regions of strong anomalies by leveraging previously collected information in a mathematically elegant and computationally tractable manner. We demonstrate superiority of the proposed approach in several applications, including reconstruction of seafloor topography from real-world bathymetry data, as well as tracking of dynamic anomalies. A particularly attractive property of our approach is its ability to overcome adversarial conditions, that is, situations in which prior beliefs about the locations of the extremes are imprecise or erroneous.