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Service-Driven Enrichment for KbR in the OMiLAB Environment

  • Michael WalchEmail author
  • Dimitris Karagiannis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10371)

Abstract

In this paper, details are presented on how physical objects interact with conceptual models in Factory of the Future (FoF) scenarios. For this reason, a hierarchical three layer structure - for physical objects, models and concepts - is described as part of the Knowledge-based Robotics (KbR) approach. Focusing on the integration of physical objects and models, the need for service-driven enrichment emerges. Thereby, the extension of physical objects with cyber twins is realized for enabling service capabilities like monitoring and control. For their development, an architecture is introduced based on the integration of logical and physical components. This is validated in the OMiLAB environment using the OMiRob case. The conceptual approach proves the capability of applying service-driven enrichment to physical objects using metamodeling techniques.

Keywords

Knowledge-based robotics Service-driven enrichment (micro)service architecture Embedded computation 

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References

  1. 1.
    John Chambers and Rik Kirkland. Cisco’s John Chambers on the digital era. http://www.mckinsey.com/industries/high-tech/our-insights/ciscos-john-chambers-on-the-digital-era (visited on 01.03.2017), 2015
  2. 2.
    John Chambers and Zoelle Egner. Advice from John Chambers to CIOs - and the world. https://blog.box.com/blog/advice-john-chambers-cios-and-world/ (visited on 01.03.2017), 2015
  3. 3.
    Shahyan Khan. Leadership in the digital age: A study on the effects of digitalisation on top management leadership, 2016Google Scholar
  4. 4.
    Christian Schlegel, Andreas Steck, Davide Brugali, and Alois Knoll. Design Abstraction and Processes in Robotics: From Code-Driven to Model-Driven Engineering, pages 324–335. Springer, Berlin Heidelberg, Berlin, Heidelberg, 2010Google Scholar
  5. 5.
    Stucke, Maurice E.: Is competition always good? Journal of Antitrust Enforcement 1(1), 162 (2013)CrossRefGoogle Scholar
  6. 6.
    Myers, Michael D., Klein, Heinz K.: A set of principles for conducting critical research in information systems. MIS Q. 35(1), 17–36 (2011)Google Scholar
  7. 7.
    Kentaro Watanabe, Masaaki Mochimaru, and Yoshiki Shimomura. Service engineeringresearch in Japan: Towards a sustainable society. In Services and the Green Economy, pages 221–244. Springer, 2016Google Scholar
  8. 8.
    The OMiLAB Community. OMiLAB Europe. http://www.omilab.org/psm/about (visited on 01.03.2017), 2017
  9. 9.
    Fill, Hans-Georg, Karagiannis, Dimitris: On the conceptualisation of modelling methods using the ADOxx meta modelling platform. Enterprise Modelling and Information Systems Architectures - An International Journal 8(1), 4–25 (2013)CrossRefGoogle Scholar
  10. 10.
    Hubert Österle, Jörg Becker, Ulrich Frank, Thomas Hess, Dimitris Karagiannis, Helmut Krcmar, Peter Loos, Peter Mertens, Andreas Oberweis, and Elmar J Sinz. Memorandum on design-oriented information systems research. European Journal of Information Systems, 20(1):7–10, 2011Google Scholar
  11. 11.
    Alberto Rodrigues da Silva: Model-driven engineering: A survey supported by the unified conceptual model. Computer Languages, Systems & Structures 43, 139–155 (2015)CrossRefGoogle Scholar
  12. 12.
    Schlegel, Christian, Steck, Andreas, Lotz, Alex: Robotic software systems: From code-driven to model-driven software development. Control and Programming. InTech, In Robotic Systems-Applications (2012)Google Scholar
  13. 13.
    Bernd-Holger Schlingloff. Cyber-Physical Systems Engineering, pp. 256–289. Springer International Publishing, Cham, 2016Google Scholar
  14. 14.
    Dimitris Karagiannis and Harald Kühn. Metamodelling Platforms, pp. 182–182. Heidelberg, Berlin, Heidelberg, 2002Google Scholar
  15. 15.
    Dimitris Karagiannis. Agile modeling method engineering. In Proceedings of the 19th Panhellenic Conference on Informatics, PCI ’15, pp. 5–10, New York, NY, USA, 2015. ACMGoogle Scholar
  16. 16.
    Yingbing Hua, Stefan Zander, Mirko Bordignon, and Björn Hein. From automationml to ros: A model-driven approach for software engineering of industrial robotics using ontological reasoning. In Emerging Technologies and Factory Automation (ETFA), 2016 IEEE 21st International Conference on, pages 1–8. IEEE, 2016Google Scholar
  17. 17.
    Stefan Zander, Georg Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger, and Nadia Ahmed. A model-driven engineering approach for ros using ontological semantics. arXiv preprint arXiv:1601.03998, 2016
  18. 18.
    Ashutosh Saxena, Ashesh Jain, Ozan Sener, Aditya Jami, Dipendra Kumar Misra, and Hema Swetha Koppula. Robobrain: Large-scale knowledge engine for robots. CoRR, abs/1412.0691, 2014Google Scholar
  19. 19.
    Tenorth, Moritz, Beetz, Michael: Knowrob: A knowledge processing infrastructure for cognition-enabled robots. The International Journal of Robotics Research 32(5), 566–590 (2013)CrossRefGoogle Scholar
  20. 20.
    Shotaro Kobayashi, Susumu Tamagawa, Takeshi Morita, and Takahira Yamaguchi. Intelligent humanoid robot with japanese wikipedia ontology and robot action ontology. In Human-Robot Interaction (HRI), 2011 6th ACM/IEEE International Conference on, pp. 417–424. IEEE, 2011Google Scholar
  21. 21.
    Hiroshi Asano, Takeshi Morita, and Takahira Yamaguchi. Development and evaluation of an operational service robot using wikipedia-based and domain ontologies. In Web Intelligence (WI), 2016 IEEE/WIC/ACM International Conference on, pp. 511–514. IEEE, 2016Google Scholar
  22. 22.
    Takeshi Morita, Yu Sugawara, Ryota Nishimura, and Takahira Yamaguchi. Integrating symbols and signals based on stream reasoning and ros. In Pacific Rim Knowledge Acquisition Workshop, pp. 251–260. Springer, 2016Google Scholar
  23. 23.
    Stefan Zander and Ramez Awad. Expressing and reasoning on features of robot-centric workplaces using ontological semantics. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, pp. 2889–2896. IEEE, 2015Google Scholar
  24. 24.
    David Götzinger, Elena-Teodora Miron, and Franz Staffel. OMiLAB: An Open Collaborative Environment for Modeling Method Engineering, pages 55–76. Springer International Publishing, Cham, 2016Google Scholar
  25. 25.
    The OMiLAB Community. OMiLAB core infrastructure. https://gitlab.dke.univie.ac.at/omilab-core-infrastructure (visited on 01.03.2017), 2017
  26. 26.
    The OMiLAB Community. OMiROB proof of concept. http://austria.omilab.org/psm/content/omicarpoc1/info (visited on 01.03.2017), 2017
  27. 27.
    Venable, John, Pries-Heje, Jan, Baskerville, Richard: A Comprehensive Framework for Evaluation in Design Science Research. In: Peffers, Ken, Rothenberger, Marcus, Kuechler, Bill (eds.) DESRIST 2012. LNCS, vol. 7286, pp. 423–438. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-29863-9_31 CrossRefGoogle Scholar
  28. 28.
    The OMiLAB Community. The ADOxx metamodelling platform. http://www.adoxx.org (visited on 01.03.2017), 2017
  29. 29.
    The OMiLAB Community. OMiROB proof of concept infastructure. https://gitlab.dke.univie.ac.at/OMiROB/RobotCarPoC (visited on 01.03.2017), 2017
  30. 30.
    The OMiLAB Community. Conceptual Models for Robot Control and Robot Monitoring. https://www.youtube.com/watch?v=n4AqAxfHxJk (visited on 01.03.2017), 2016
  31. 31.
    The OMiLAB Community. OMiRob experiments. http://austria.omilab.org/psm/omirob (visited on 01.03.2017), 2016
  32. 32.
    Yu Sugawara, Takeshi Morita, S Saito, and Takahira Yamaguchi. An intelligent application development platform for service robots. In MuSRobS@ IROS, pp. 16–20, 2015Google Scholar
  33. 33.
    Dimitris Karagiannis, Robert Andrei Buchmann, Patrik Burzynski, Ulrich Reimer, and Michael Walch. Fundamental Conceptual Modeling Languages in OMiLAB, pp. 3–30. Springer International Publishing, Cham, 2016Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Research Group Knowledge Engineering,Faculty of Computer ScienceUniversity of ViennaViennaAustria

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