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- Cronrath, Constantin, 1990, et al.
(author)
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Relevant Safety Falsification by Automata Constrained Reinforcement Learning
- 2022
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In: IEEE International Conference on Automation Science and Engineering. - 2161-8070 .- 2161-8089. ; 2022-August, s. 2273-2280
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Conference paper (peer-reviewed)abstract
- Complex safety-critical cyber-physical systems, such as autonomous cars or collaborative robots, are becoming increasingly common. Simulation-based falsification is a testing method for uncovering safety hazards of such systems already in the design phase. Conventionally, the falsification method takes the form of a static optimization. Recently, dynamic optimization methods such as reinforcement learning have gained interest for their ability to uncover harder-to-find safety hazards. However, these methods may converge to risk-maximising, but irrelevant behaviors. This paper proposes a principled formulation and solution of the falsification problem by automata constrained reinforcement learning, in which rewards for relevant behavior are tuned via Lagrangian relaxation. The challenges and proposed methods are demonstrated in a use-case example from the domain of industrial human-robot collaboration, where falsification is used to identify hazardous human worker behaviors that result in human-robot collisions. Compared to random sampling and conventional approximate Q-learning, we show that the proposed method generates equally hazardous, but at the same time more relevant testing conditions that expose safety flaws.
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| 2. |
- Huck, Tom P., et al.
(author)
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Hazard Analysis of Collaborative Automation Systems: A Two-layer Approach based on Supervisory Control and Simulation
- 2023
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In: Proceedings - IEEE International Conference on Robotics and Automation. - 1050-4729. ; 2023-May, s. 10560-10566
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Conference paper (peer-reviewed)abstract
- 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.
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