Bayesian Learning for the Robust Verification of Autonomous Robots
This addresses the need for safe and effective verification of autonomous robots in critical missions like infrastructure inspection and space exploration, though it appears incremental as it builds on existing Bayesian and Markov model approaches.
The paper tackles the problem of runtime verification for autonomous robots operating in dynamic environments by developing a Bayesian learning framework that learns expected ranges for event occurrence rates and analyzes interval continuous-time Markov models to obtain expected variation intervals for system properties like mission duration and success probability. The framework was applied to an underwater infrastructure inspection and repair mission, with formal proofs and experiments showing it produces results that reflect real-world uncertainty, enabling robust verification under parametric uncertainty.
Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments. As such, they must continually verify their ability to complete the tasks associated with these missions safely and effectively. Here we present a Bayesian learning framework that enables this runtime verification of autonomous robots. The framework uses prior knowledge and observations of the verified robot to learn expected ranges for the occurrence rates of regular and singular (e.g., catastrophic failure) events. Interval continuous-time Markov models defined using these ranges are then analysed to obtain expected intervals of variation for system properties such as mission duration and success probability. We apply the framework to an autonomous robotic mission for underwater infrastructure inspection and repair. The formal proofs and experiments presented in the paper show that our framework produces results that reflect the uncertainty intrinsic to many real-world systems, enabling the robust verification of their quantitative properties under parametric uncertainty.