Hazard Analysis of Collaborative Automation Systems: A Two-layer Approach based on Supervisory Control and Simulation
Paper in proceeding, 2023

Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reasoning, past experiences, and simple tools such as checklists and spreadsheets. Increasing system complexity makes such approaches decreasingly suitable. Furthermore, testing-based hazard analysis is often not suitable due to high costs or dangers of physical faults. A remedy for this are model-based hazard analysis methods, which either rely on formal models or on simulation models, each with their own benefits and drawbacks. This paper proposes a two-layer approach that combines the benefits of exhaustive analysis using formal methods with detailed analysis using simulation. Unsafe behaviours that lead to unsafe states are first synthesised from a formal model of the system using Supervisory Control Theory. The result is then input to the simulation where detailed analyses using domain-specific risk metrics are performed. Though the presented approach is generally applicable, this paper demonstrates the benefits of the approach on an industrial human-robot collaboration system.

Author

Tom P. Huck

Karlsruhe Institute of Technology (KIT)

Yuvaraj Selvaraj

Chalmers, Electrical Engineering, Systems and control

Constantin Cronrath

Chalmers, Electrical Engineering, Systems and control

Christoph Ledermann

Karlsruhe Institute of Technology (KIT)

Martin Fabian

Chalmers, Electrical Engineering, Systems and control

Bengt Lennartson

Chalmers, Electrical Engineering, Systems and control

Torsten Kroger

Karlsruhe Institute of Technology (KIT)

Proceedings - IEEE International Conference on Robotics and Automation

10504729 (ISSN)

Vol. 2023-May 10560-10566
9798350323658 (ISBN)

2023 IEEE International Conference on Robotics and Automation, ICRA 2023
London, United Kingdom,

Automatically Assessing Correctness of Autonomous Vehicles (Auto-CAV)

VINNOVA (2017-05519), 2018-03-01 -- 2021-12-31.

Subject Categories

Computer Engineering

DOI

10.1109/ICRA48891.2023.10161338

More information

Latest update

9/5/2023 1