Extension of Established Modern Physics Simulation for the Training of Robotic Electrical Cabinet Assembly

Abstract

High-performance physics simulation environments are an excellent option to simulate robot-based assembly processes for training faster-than-real-time and providing the necessary data. However, since established physics engines are rigid multi-body simulators at their core, they cannot simulate the deformation process during the assembly of control cabinet terminals. Thus, appropriate extensions of the simulation are necessary. This paper introduces and evaluates extensions incorporating physical joining models to simulate the snap-fit assembly in electrical cabinets. The presented results showed that the extensions can provide input data of high accuracy for the joining models. In this way, they empower the generation of valid training data for snap-fit assembly from standard rigid body simulations.