Der digitale Zwilling in der autonomen Robotik

Formale Modelle von der Semantik bis zur Auflösung

Autor/innen

  • Bernd Kast Siemens AG
  • Sebastian Albrecht
  • Vincent Dietrich Siemens AG
  • Florian Wirnshofer Siemens AG
  • Wendelin Feiten Siemens AG
  • Georg von Wichert Siemens AG

DOI:

https://doi.org/10.17560/atp.v61i5.2422

Schlagworte:

Digitaler Zwilling, Autonomie, Hierarchie

Abstract

Autonome Systeme sind der Schlüssel zu signifikant höherer Flexibilität in der Automatisierung. Sie benötigen einen Digitalen Zwilling, der detaillierte Modelle und Daten vereint. Robuste Planung und Ausführung erfordern extrem detaillierte Simulationen, die sich aufgrund des hohen Rechenaufwands nur für Teilaufgaben in Echtzeit lösen lassen. Wirklich autonome Systeme können komplexe Aufgaben jedoch selbstständig zerlegen und dann auch lösen. In diesem Artikel werden die wesentlichen Bestandteile autonomer Produktionssysteme beschrieben: formale Modelle für Dinge und Aktionen auf unterschiedlichen Abstraktionsebenen, die zugehörige hierarchische Planung und die Modell-prädiktive Ausführung.

Literaturhinweise

Bish, E. K., Muriel, A., und Biller, S. (2005). Managing flexible capacity in a make-to-order environment. Management Science, 51(2), (pp.167-180).

Hitomi, K. (2017). Manufacturing systems engineering: A unified approach to manufacturing technology, production management and industrial economics. Routledge.

Schmitz, S., Schluetter, M., & Epple, U. (2009, September). Automation of Automation—Definition, components and challenges. In 2009 IEEE Conference on Emerging Technologies & Factory Automation (pp. 1-7). IEEE.

Vogel-Heuser, B., Diedrich, C., Fay, A., Jeschke, S., Kowalewski, S., Wollschlaeger, M., und Göhner, P. (2014). Challenges for software engineering in automation. Journal of Software Engineering and Applications, 7(05), (pp. 440).

Rosenschein, S. J. (1985). Formal theories of knowledge in AI and robotics. New generation computing, 3(4), (pp. 345-357).

Guarino, N. (1995). Formal ontology, conceptual analysis and knowledge representation. International journal of human-computer studies, 43(5-6), (pp. 625-640).

Glaessgen, E., Stargel, D. (2012, April). The digital twin paradigm for future NASA and US Air Force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA (pp. 1818).

Rosen, R., Von Wichert, G., Lo, G., und Bettenhausen, K. D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine, 48(3), (pp. 567-572).

Uhlemann, T. H. J., Lehmann, C., und Steinhilper, R. (2017). The digital twin: Realizing the cyber-physical production system for industry 4.0. Procedia Cirp, 61, (pp. 335-340).

Russell, S. J., Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,.

McDermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., ... und Wilkins, D. (1998). PDDL-the planning domain definition language.

Helmert, M. (2006). The fast downward planning system. Journal of Artificial Intelligence Research, 26, (pp. 191-246).

Kuffner Jr, J. J., LaValle, S. M. (2000, April). RRT-connect: An efficient approach to single-query path planning. In ICRA (Vol. 2).

Karaman, S., Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. The international journal of robotics research, 30(7), 846-894.

Fagin, R., Halpern, J. Y., Moses, Y., und Vardi, M. (2004). Reasoning about knowledge. MIT press.

Pearl, J. (2014). Probabilistic reasoning in intelligent systems: networks of plausible inference. Elsevier.

Nocedal, J., und Wright, S. (2006). Numerical optimization. Springer Science & Business Media.

Bertsekas, D. P. (1999). Nonlinear Programming 2nd edn (Belmont, MA: Athena Scientific).

Kouvelis, P., und Yu, G. (2013). Robust discrete optimization and its applications (Vol. 14). Springer Science & Business Media.

Georgeff, M. P., Lansky, A. L. (1986). Procedural knowledge. Proc. of the Institute of Electrical and Electronics Engineers, Special Issue on Knowledge Representation 74(10), (pp. 1383–1398).

Apt, K. R., Blair, H. A., und Walker, A. (1988). Towards a theory of declarative knowledge. In Foundations of deductive databases and logic programming (pp. 89-148). Morgan Kaufmann.

Baral, C. (2003). Knowledge representation, reasoning and declarative problem solving. Cambridge university press.

Bryan, A., Ko, J., Hu, S. J., und Koren, Y. (2007). Co-evolution of product families and assembly systems. CIRP annals, 56(1), (pp. 41-44).

Hu, S. J., Ko, J., Weyand, L., ElMaraghy, H. A., Lien, T. K., Koren, Y., ... und Shpitalni, M. (2011). Assembly system design and operations for product variety. CIRP annals, 60(2), (pp. 715-733)s.

Hasegawa, T., Suehiro, T., und Takase, K. (1992). A model-based manipulation system with skill-based execution. IEEE Transactions on Robotics and Automation, 8(5), (pp. 535-544).

Billard, A., Calinon, S., Dillmann, R., und Schaal, S. (2008). Robot programming by demonstration. Springer handbook of robotics, (pp. 1371-1394).

Miller, A. T., Knoop, S., Christensen, H. I., und Allen, P. K. (2003). Automatic grasp planning using shape primitives.

Bekey, G. A. (2005). Autonomous robots: from biological inspiration to implementation and control. MIT press.

Camacho, E. F., und Bordons, C. (2004). Model Predictive Control, © Springer-Verlag, 2004.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), (pp. 436).

Thrun, S. (2002). Robotic mapping: A survey. Exploring artificial intelligence in the new millennium, 1(1-35), 1.

Siciliano, B., und Khatib, O. (Eds.). (2016). Springer handbook of robotics. Springer.

Wirnshofer, F., Schmitt, P. S., Feiten, W., Wichert, G. V., und Burgard, W. (2018). Robust, compliant assembly via optimal belief space planning. arXiv preprint arXiv:1811.03904.

Schmitt, P. S., Neubauer, W., Feiten, W., Wurm, K. M., Wichert, G. V., & Burgard, W. (2017, May). Optimal, sampling-based manipulation planning. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3426-3432). IEEE.

Brooks, R. (1986). A robust layered control system for a mobile robot. IEEE journal on robotics and automation, 2(1), (pp. 14-23).

Fox, M., und Long, D. (2003). PDDL2. 1: An extension to PDDL for expressing temporal planning domains. Journal of artificial intelligence research, 20, (pp. 61-124).

Lozano-Pérez, T., und Kaelbling, L. P. (2014, September). A constraint-based method for solving sequential manipulation planning problems. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3684-3691). IEEE.

Lee, J., und Leyffer, S. (Eds.). (2011). Mixed integer nonlinear programming (Vol. 154). Springer Science & Business Media.

Arendt, P. D., Apley, D. W., und Chen, W. (2012). Quantification of model uncertainty: Calibration, model discrepancy, and identifiability. Journal of Mechanical Design, 134(10), 100908.

Ganter, B., und Wille, R. (2013). Formale Begriffsanalyse: mathematische grundlagen. Springer-Verlag.

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Veröffentlicht

2019-05-07

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Rubrik

Hauptbeitrag / Peer-Review