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Vision-based Generation of Precedence Graphs

  • Dorian RohnerEmail author
  • Myriel Fichtner
  • Dominik Henrich
Conference paper
  • 1.6k Downloads

Zusammenfassung

The current developments in robotics aim towards a usage in households and small and medium-sized enterprises. In this case it is necessary to coordinate human and robot by describing the task in a common model. Precedence graphs are a possible representation of such a model. The generation of these is tedious especially for non-experts.We contribute a vision-based approach which generates precedence graphs based on a world representation generated by an registered eye-in-hand camera. To achieve this, we describe a world representation based on boundary representation models and a corresponding object recognition method. The results are used to generate the precedence graph by calculating the AND/OR graph as an intermediate step. We evaluate our approach based on several scene models and on a real world application.

Schlüsselwörter

Task models Precedence graph Object recognition Boundary representation 

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Literatur

  1. 1.
    A. Billard, and S. Calinon: Handbook of Robotics Chapter 59: Robot Programming by Demonstration. Springer, 2007Google Scholar
  2. 2.
    A. Bourjault: Contribution a une approche methodologique de l’assemblage automatise. Elaboration automatique des sequences operatoires. Phd thesis, Université de Franche-Comté, Besançon, 1984Google Scholar
  3. 3.
    H. Bruyninckx, T. Lefebvre, L. Mihaylova, E. Staffetti, J. De Schutter, and J. Xiao: A roadmap for autonomous robotic assembly. IEEE International Symposium on Assembly and Task Planning , 2001Google Scholar
  4. 4.
    CGAL: The Computational Geometry Algorithms Library. https://www.cgal.org/, 2018. Accessed 02.11.2018
  5. 5.
    T. L. De Fazio, and D. E. Whitney: Simplified generation of all mechanical assembly sequences. IEEE Journal of Robotics and Automation, 1987Google Scholar
  6. 6.
    P. Jimnez: Survey on assembly sequencing: A combinatorial and geometrical perspective. Journal of Intelligent Manufacturing, 2013Google Scholar
  7. 7.
    L. Kavraki, J.-C. Latombe, and R. H. Wilson: On the complexity of assembly partitioning. Information Processing Letters, 1993Google Scholar
  8. 8.
    S. M. La Valle: Planning algorithms. Cambridge University Press, 2006Google Scholar
  9. 9.
    J. M. Lien: 3D point-based Minkowski sum. http://masc.cs.gmu.edu/wiki/Software#m+3dpt, 2007. Accessed 02.11.2018
  10. 10.
    J. M. Lien: A simple method for computing Minkowski sum boundary in 3D using collision detection. Springer Tracts in Advanced Robotics, 2010Google Scholar
  11. 11.
    D. G. Lowe: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004Google Scholar
  12. 12.
    L. S. Homem de Mello, and A. C. Sanderson: Representations of Mechanical Assembly Sequences, 1991Google Scholar
  13. 13.
    R. Mojtahedzadeh, A. Bouguerra, and A. J. Lilienthal. Automatic relational scene representation for safe robotic manipulation. IEEE International Conference on Intelligent Robots and Systems, 2013Google Scholar
  14. 14.
    X. Niu, H. Ding, and Y. Xiong: A hierarchical approach to generating precedence graphs for assembly planning. International Journal of Machine Tools and Manufacture, 2003Google Scholar
  15. 15.
    K. Nottensteiner, T. Bodenmller, M. Kaecker, M. A. Roa, A. Stemmer, T. Stouraitis, D. Seidel, and U. Thomas: A Complete Automated Chain for Flexible Assembly using Recognition, Planning and Sensor-Based Execution. International Symposium on Robotics, 2016Google Scholar
  16. 16.
    D. Riedelbauch and D. Henrich: Coordinating Flexible Human-Robot Teams by Local World State Observation. IEEE International Symposium on Robot and Human Interactive Communication, 2017Google Scholar
  17. 17.
    M. Sand and D. Henrich: Incremental reconstruction of planar B-Rep models from multiple point clouds. The Visual Computer, 2016Google Scholar
  18. 18.
    M. Sand and D. Henrich: Matching and Pose Estimation of Noisy, Partial and Planar B-Rep Models. Computer Graphics International, 2017Google Scholar
  19. 19.
    U. Thomas, T. M. Barrenscheenm and F. M. Wahl: Efficient Assembly Sequence Planning Using Stereographical Projections of k. IEEE Symposium on Assembly and Task Planning, 2003Google Scholar
  20. 20.
    U. Thomas, T. Stouraitis, and M. A Roa: Flexible assembly through integrated assembly sequence planning and grasp planning. IEEE International Conference on Automation Science and Engineering, 2015Google Scholar
  21. 21.
    J. Xiao, and X. Ji: A divide-and-merge approach to automatic generation of contact states and planning of contact motion. Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation, 2000Google Scholar
  22. 22.
    Y. Z. Zhang, J. Ni, Z. Q. Lin, and X. M. Lai: Automated sequencing and subassembly detection in automobile body assembly planning. Journal of Materials Processing Technology, 2002Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Dorian Rohner
    • 1
    Email author
  • Myriel Fichtner
    • 1
  • Dominik Henrich
    • 1
  1. 1.Lehrstuhl für Robotik und Eingebettete SystemeUniversität BayreuthBayreuthDeutschland

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