自律ロボットのロバスト検証のためのベイズ学習
Bayesian Learning for the Robust Verification of Autonomous Robots
ベイズ学習を用いて自律ロボットの実行時検証を行い、事象発生率の不確実性を考慮してミッション成功率や所要時間の変動範囲を推定する枠組みを提案し、水中インフラ点検ミッションで有効性を示した。
著者: Xingyu Zhao, Simos Gerasimou, Radu Calinescu, Calum Imrie, Valentin Robu, David Flynn
分類: cs.RO, cs.AI
原文アブストラクト
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.
