Bayesian Learning for the Robust Verification of Autonomous Robots

作     者：Zhao, Xingyu Gerasimou, Simos Calinescu, Radu Imrie, Calum Robu, Valentin Flynn, David 

作者机构：Warwick Manufacturing Group University of Warwick Coventry United Kingdom Department of Computer Science University of York York United Kingdom Department of Computer Science and Assuring Autonomy International Programme University of York York United Kingdom Intelligent and Autonomous Systems Group Centrum Wiskunde & Informatica Amsterdam Netherlands Electrical Engineering Department Eindhoven University of Technology Eindhoven Netherlands James Watt School of Engineering University of Glasgow Glasgow United Kingdom 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Robots 

摘      要：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. © 2023, CC BY.