Semantic modelling for collaborative robot applications

Model-based code generation

Authors

  • Tobias Vogel
  • Florian Kerber

DOI:

https://doi.org/10.17560/atp.v59i11.1912

Keywords:

semantic modelling, UML, human-robot collaboration, model-based development

Abstract

Digitization as well as horizontal and vertical integration are revolutionizing the development of industrial production systems. With increasing customer demands, especially for individualized products, automated production systems have to become more flexible and intuitive so that increasingly small batch sizes can be manufactured efficiently. Collaborative robots are a key technology to meet the requirements postulated in initiatives like “Industry 4.0”. To set up human-robot collaboration applications an integrated development process will be needed to address both functional and safety-related aspects. Semantic modelling and formal methods used in theoretical computer science and software development provide methodologies that can be used for automated production systems as well. In this paper, a systematic development process for HRC applications is presented that exploits the increased functionalities of collaborative robot systems. Semantic modelling using UML diagrams forms the basis of the proposed approach in order to apply model-based development tools through to machine code generation.

References

Albu-Schäffer, A., Ott, C., & Hirzinger, G. (2007). A unified passivity-based control framework for position, torque and impedance control of flexible joint robots. The international journal of robotics research, 26(1), 23-39.

Alt, O. (2015). Potenzial der OMG-Standards. In Computer Automation 2015(11), S. 35 – 38. Haar: WEKA FACHMEDIEN GmbH

Baras, J. (2016). The Next Wonder – MBSE/MBE: From Ideas to “Making Products and Services”. Paper presented at Emerging Technologies & Factory Automation (ETFA), 2016 IEEE 21st Conference.

Bareiß, P., Schütz, D., Priego, R., Marcos, M., & Vogel-Heuser, B. (2016, September). A model-based failure recovery approach for automated production systems combining SysML and industrial standards. In Emerging Technologies and Factory Automation (ETFA), 2016 IEEE 21st International Conference on (pp. 1-7). IEEE.

Botthof, A., & Hartmann, E. A. (Eds.). (2014). Zukunft der Arbeit in Industrie 4.0. Springer-Verlag.

Gausemeier, J., Trächtler, A., & Schäfer, W. (2014). Semantische technologien im entwurf mechatronischer systeme: Effektiver austausch von lösungswissen in branchenwertschöpfungsketten. Carl Hanser Verlag GmbH Co KG.

Huber, W. (2016). Industrie 4.0 in der Automobilproduktion: ein Praxisbuch. Springer-Verlag.

Händel, G., Kerber, F. (2016). A UML-based Approach to Manage Product

Variability in Automated Production Lines. Presented at 21th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), 2016 IEEE 21st Conference.

Objekt Management Group (2016). What is UML?. Abgerufen von http://www.uml.org/

Kagermann, H., Helbig, J., Hellinger, A., Stumpf, V., Wahlster, W. (2013). Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0: Deutschlands Zukunft als Produktionsstandort sichern; Abschlussbericht des Arbeitskreises Industrie 4.0. Forschungsunion. Abrufbar unter: http://www.plattform-i40.de/sites/default/files/Bericht_Industrie%204.0_0.pdf

Published

2017-11-17