A Hardware-Agnostic OPC UA Skill Model for Robot Manipulators and Tools

Stefan Profanter, Ari Breitkreuz, Markus Rickert und Alois Knoll

Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA),

September 2019 · Zaragoza, Spain


The current trend to lot-size-one production requires reduced integration effort and easy reuse of available devices inside the production line. These devices have to offer a uniform interface to fulfill these requirements. This paper presents a hardware-agnostic skill model using the semantic modeling capabilities of OPC UA. The model provides a standardized interface to hardware or software functionality while offering an intuitive way of grouping multiple skills to a higher hierarchical abstraction. Our skill model is based on OPC UA Programs and modeled as an open source NodeSet. We hereby focus on the reusability of the skills for many different domains. The model is evaluated by controlling three different industrial robots and their tools through the same skill interface. The evaluation shows that our generic OPC UA skill model can be used as a standardized control interface for device and software components in industrial manufacturing. With our solution new components can easily be exchanged without changing the interface. This is not only true for industrial robots, but for any device which provides a controllable functionality.

Stichworte: robotics, data backbone