Advertisement

Design and Implementation of a Cognitive Simulation Model for Robotic Assembly Cells

  • Marco Faber
  • Sinem Kuz
  • Marcel Ph. Mayer
  • Christopher M. Schlick
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8019)

Abstract

Against the background of a changing global economy, new production technologies have to be developed to stay competitive in high-wage countries. Therefore, an integrated cognitive simulation model (CSM) has been developed to support the human operator and the assembly process. By making the behavior of the system more intuitive the cognitive compatibility between the operator and the production system is enhanced significantly. The presented CSM faces three different challenges: (1) visualizing the behavior of the system to give the human operator an understanding of the technical systems, (2) cognitive control of a real robotic assembly cell and (3) performing mass simulations in order to evaluate parameters, new assembly or planning strategies or the assembly of new products. Additionally, a graph-based planner supports the cognitive planning instance for realizing complex tasks.

Keywords

cognitive simulation joined cognitive systems human- machine interaction production systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wiendahl, H.P., ElMaraghy, H., Nyhuis, P., Zäh, M., Wiendahl, H.H., Duffie, N., Brieke, M.: Changeable Manufacturing - Classification, Design and Operation. CIRP Annals - Manufacturing Technology 56(2), 783–809 (2007)CrossRefGoogle Scholar
  2. 2.
    Brecher, C.: Integrative Production Technology for High-Wage Countries. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Onken, R., Schulte, A.: System-Ergonomic Design of Cognitive Automation. SCI, vol. 235. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Bainbridge, L.: Ironies of Automation. In: Rassmussen, J., Duncan, K., Leplat, J. (eds.) New Technology and Human Error, pp. 271–283. Wiley, Chichester (1987)Google Scholar
  5. 5.
    Hollnagel, E., Woods, D.D.: Joint Cognitive Systems: Foundations of Cognitive Systems Engineering. Taylor & Francis Group, Boca Raton (2005)CrossRefGoogle Scholar
  6. 6.
    Brecher, C., Müller, S., Faber, M., Herfs, W.: Design and Implementation of a omprehensible Cognitive Assembly System. In: Conference Proceedings of the 4th International Conference on Applied Human Factors and Ergonomics (AHFE). USA Publishing (2012)Google Scholar
  7. 7.
    Mayer, M.P.: Entwicklung eines kognitionsergonomischen Konzeptes und eines Simulationssystems für die robotergestützte Montage. PhD thesis, RWTH Aachen University (2012) (in German)Google Scholar
  8. 8.
    Mayer, M.P., Schlick, C.M.: Improving operator’s conformity with expectations in a cognitively automated assembly cell using human heuristics. In: Conference Proceedings of the 4th International Conference on Applied Human Factors and Ergonomics (AHFE), pp. 1263–1272. USA Publishing (2012)Google Scholar
  9. 9.
    Kuz, S., Heinicke, A., Schwichtenhövel, D., Mayer, M., Schlick, C.: The Effect of Anthropomorphic Movements of Assembly Robots on Human Prediction. In: Karwowski, W., Trzcielinski, S. (eds.) Advances in Ergonomics in Manufacturing, Boca Raton, FL, USA, pp. 263–271 (2012)Google Scholar
  10. 10.
    Russel, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Upper Saddle River (2003)Google Scholar
  11. 11.
    Kempf, T.: Ein kognitives Steuerungsframework für robotergestützte Handhabungsaufgaben. PhD thesis, Aprimus, Aachen (2010) (in German)Google Scholar
  12. 12.
    Mayer, M.P., Odenthal, B., Faber, M., Schlick, C.M.: Cognitively Automated Assembly Processes: A Simulation Based Evaluation of Performance. In: Work: A Journal of Prevention, Assessment and Rehabilitation - IEA 2012: 18th World Congress on Ergonomics - Designing a Sustainable Future, vol. 1, pp. 3449–3454 (2012)Google Scholar
  13. 13.
    Barachini, F.: Match-Time Predictability in Real-Time Production Systems. In: Gottlob, G., Nejdl, W. (eds.) Expert Systems in Engineering. LNCS, vol. 462, pp. 190–203. Springer, Heidelberg (1990)Google Scholar
  14. 14.
    Odenthal, B., Mayer, M., Kabuß, W., Schlick, C.: Design and Evaluation of an Augmented Vision System for Human-Robot Cooperation in Cognitively Automated Assembly Cells. In: Proceedings of the 9th International Multi-Conference on Systems, Signals and Devices (SSD). Institute of Electrical and Electronics Engineers (IEEE), Chemnitz (2012)Google Scholar
  15. 15.
    Ewert, D., Mayer, M.P., Schilberg, D., Jeschke, S.: Adaptive assembly planning for a nondeterministic domain. In: Conference Proceedings of the 4th International Conference on Applied Human Factors ad Ergonomics (AHFE), pp. 2720–2729 (2012)Google Scholar
  16. 16.
    Thomas, U., Wahl, F.M.: A System for Automatic Planning, Evaluation and Execution of Assembly Sequences for Industrial Robots. In: Proceedings of International Conference on Intelligent Robots and Systems, vol. 3, pp. 1458–1464 (2001)Google Scholar
  17. 17.
    Liu, G., Ramakrishnan, K.G.: A*Prune: an algorithm for finding K shortest paths subject to multiple constraints. In: Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 743–749 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marco Faber
    • 1
  • Sinem Kuz
    • 1
  • Marcel Ph. Mayer
    • 1
  • Christopher M. Schlick
    • 1
  1. 1.Institute of Industrial Engineering and ErgonomicsRWTH Aachen UniversityAachenGermany

Personalised recommendations