Simulation-based Testing for Early Safety-Validation of Robot Systems
This work provides a method for robot system designers to identify safety hazards earlier in the design process, potentially reducing the cost of implementing safety changes.
This paper tackles the problem of early safety validation for industrial human-robot collaborative systems. It proposes using a human model and an optimization algorithm to generate high-risk human behavior in simulation, which successfully exposes potential hazards in an industrial robot cell application.
Industrial human-robot collaborative systems must be validated thoroughly with regard to safety. The sooner potential hazards for workers can be exposed, the less costly is the implementation of necessary changes. Due to the complexity of robot systems, safety flaws often stay hidden, especially at early design stages, when a physical implementation is not yet available for testing. Simulation-based testing is a possible way to identify hazards in an early stage. However, creating simulation conditions in which hazards become observable can be difficult. Brute-force or Monte-Carlo-approaches are often not viable for hazard identification, due to large search spaces. This work addresses this problem by using a human model and an optimization algorithm to generate high-risk human behavior in simulation, thereby exposing potential hazards. A proof of concept is shown in an application example where the method is used to find hazards in an industrial robot cell.