Simulation-based Testing for Early Safety-Validation of Robot Systems

TL;DR

This paper presents a simulation-based testing approach that uses human models and optimization algorithms to identify safety hazards early in the design process of industrial robot systems, reducing costly late-stage modifications.

Contribution

It introduces a novel method combining human modeling and optimization to generate high-risk behaviors for early hazard detection in robot system design.

Findings

Successfully identified hazards in an industrial robot cell

Demonstrated effectiveness of the method in early safety validation

Reduced need for physical prototypes in hazard detection

Abstract

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.