Bayesian learning for the robust verification of autonomous robots

Zhao, X., Gerasimou, S., Calinescu, R., Imrie, C., Robu, V. and Flynn, D. (2024) Bayesian learning for the robust verification of autonomous robots. Communications Engineering, 3, 18. (doi: 10.1038/s44172-024-00162-y)

Abstract

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.

• Item Type: Articles
• Additional Information: This project has received funding from the ORCA-Hub PRF project ‘COVE’, the Assuring Autonomy International Programme, the UKRI project EP/V026747/1 ‘Trustworthy Autonomous Systems Node in Resilience’, and the European Union’s Horizon 2020 project SESAME (grant agreement No 101017258).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Flynn, Professor David

Creator Roles

Flynn, D.: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review and editing

• Authors: Zhao, X., Gerasimou, S., Calinescu, R., Imrie, C., Robu, V., and Flynn, D.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: Communications Engineering
• Publisher: Nature Research
• ISSN: 2731-3395
• ISSN (Online): 2731-3395
• Copyright Holders: Copyright © 2024 The Author(s)
• First Published: First published in Communications Engineering 3:18
• Publisher Policy: Reproduced under a Creative Commons license