Towards Patterns for a Reference Assurance Case for Autonomous Inspection Robots
An assurance case provides a structured argument, supported by evidence, aiming to justify some key property of a system. Reference assurance cases can serve as standardised templates or examples for developing assurance cases across various industries, facilitating alignment with regulatory standards and supporting certification. They hold the potential to more efficiently develop the assurance case and ensure best practice is maintained. A key technique in developing a reference assurance case is the use of assurance patterns. These patterns, inspired by design patterns, enable the reuse of safety argument structures. In this paper we apply this concept to the assurance of autonomous inspection robots that operate in dynamic and uncertain environments. Given the inherent complexity that arises from the autonomy of these systems, a range of distinct verification methods (e.g., formal verification, simulation, physical experiments) will be required to foster confidence. This work-in-progress paper proposes a corroborative assurance approach, enabling engineers to leverage various verification and validation methods when constructing an assurance case. The main contributions of this paper are initial proposals for reusable assurance patterns based on mission patterns, and a high-level methodology for achieving a reference assurance case utilising these. An initial application of our approach is presented through a case study of road verge inspection using an autonomous robot.
Authors
- Louise A. Dennis (ORCID: https://orcid.org/0000-0003-1426-1896)
- Dhaminda B. Abeywickrama (ORCID: https://orcid.org/0000-0002-4423-0284)
- Michael Fisher (ORCID: https://orcid.org/0000-0002-0875-3862)
- Frederick P. Wheeler
Institutions
- University of Manchester (GB)
Publication Details
- Published
- 2025-04-27
- DOI
- https://doi.org/10.1109/icsr66718.2025.00016
- Citations
- 1
- Primary Topic
- Safety Systems Engineering in Autonomy
- Type
- article
- Field-Weighted Citation Impact
- 1.30