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TOWARDS A RESILIENCE ASSURANCE MODEL FOR ROBOTIC AUTONOMOUS SYSTEMS

Published online by Cambridge University Press:  27 July 2021

Felician Campean ,
Sohag Kabir ,
Cuong Dao ,
Qichun Zhang  and
Claudia Eckert
Felician Campean*
Affiliation:
University of Bradford;
Sohag Kabir
Affiliation:
University of Bradford;
Cuong Dao
Affiliation:
University of Bradford;
Qichun Zhang
Affiliation:
University of Bradford;
Claudia Eckert
Affiliation:
The Open University
*
Campean, Felician, University of Bradford School of Engineering United Kingdom, F.Campean@bradford.ac.uk

Abstract

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Applications of autonomous systems are becoming increasingly common across the field of engineered systems from cars, drones, manufacturing systems and medical devices, addressing prevailing societal changes, and, increasingly, consumer demand. Autonomous systems are expected to self-manage and self-certify against risks affecting the mission, safety and asset integrity. While significant progress has been achieved in relation to the modelling of safety and safety assurance of autonomous systems, no similar approach is available for resilience that integrates coherently across the cyber and physical parts. This paper presents a comprehensive discussion of resilience in the context of robotic autonomous systems, covering both resilience by design and resilience by reaction, and proposes a conceptual model of a system of learning for resilience assurance in a continuous product development framework. The resilience assurance model is proposed as a composable digital artefact, underpinned by a rigorous model-based resilience analysis at the system design stage, and dynamically monitored and continuously updated at run time in the system operation stage, with machine learning based knowledge extraction and validation.

Keywords

ResilienceAutonomous systemsSystems Engineering (SE)Artificial intelligenceIndustry 4.0
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 3189 - 3198
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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