Reducing Risk in Industrial Bin Picking With PPRS Configuration and Dependency Management

Abstract

Industrial Bin Picking Applications (IBPAs) shall improve production and economic efficiency, but still, they are hard to design efficiently, with sufficient system accuracy, cycle time, and technical availability. However, it is not clear how to represent the knowledge on quantitative means for IBPA subsystem analysis and specified quality parameters. This paper aims to close this gap by introducing the Risk-Aware Bin Picking Configuration (RABPC) model on sub-system configurations and their dependencies to quality characteristics of the IBPA. In the RABPC model, a configuration matrix represents the involved sub-systems, and a Product-Process-Resource-Skill (PPRS) Network describes quality dependencies of library components to reduce the risk of deficient applications. We evaluate the RABPC model with a Bin Picking use case from the automotive industry.