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Environment sensing for the creation of work cell models

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Abstract

Since small and medium enterprises (SME) mostly produce small lot sizes, industrial robots cannot be applied profitably. This is due to the fact that the efforts for commissioning, such as expert knowledge, set-up time and know-how—compared to manual manufacturing—lead to an unbalanced cost benefit ratio. Hence, commissioning methods need to be developed, providing special commission processes within SMEs. A promising approach is the combination of the individual advantages of the online and offline commissioning in order to support untrained operators. Those hybrid commissioning methods assure the robot motion by simulation, whereby a virtual model of the work cell is required which includes exact solid models for all objects (e.g. robots, machines, fences) in the workspace of the robot. Within SMEs, those solid models are often available for a few objects only. Therefore, the required work cell model cannot be constructed using conventional modeling methods. This paper presents an approach for combining measured 3D depth data with accurate CAD models.

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References

  1. Schuh G, Meier J, Wellensiek M (2005) Chance robotik. ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 07–08:416–419

    Google Scholar 

  2. Brecher C, Schröter B, Kürzel A, Herchel M, Matthias B (2006) Portable robot systems for machine tending tasks. In: Proceedings of the 37th international symposium on robotics, München, pp 15–17. Mai 2006

  3. Klempnow H, Steinhagen G, Bilek E, Kuhlenkötter B (2011) Innovative Fortbildung in Roboteranwendungen. wt Werkstattechnik online Jahrgang 101 (2011) H. 11/12

  4. Wenk M (2009) Virtuelle inbetriebnahme von produktionsanlagen. Automation Valley Profile 10.11.2009/2

  5. Göbel M (2012) Verfahren zur intuitiven programmierung von industrierobotern durch demonstration. Apprimus Verlag. ISBN 978-3-86359-067-3

  6. Brecher C, Roßmann J, Schlette C, Herfs W, Ruf H, Göbel M (2010) Intuitive roboter-programmierung in der automatisierten montage. wt Werkstattechnik online Jahrgang 100 (2010) H. 9

  7. Suppa M (2008) Autonomous robot work cell exploration using multisensory eye-in-hand systems. Fortschritt-Berichte, Meß-, Steuerungs- und Regelungstechnik, VDI Verlag

  8. Besl P, McKay N (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 239–256

  9. Rusinkiewicz S, Levoy M (2001) Efficient variants of the ICP algorithm. In: Proceedings the third international conference on 3-D digital imaging and modeling. IEEE Computer Society, pp 145–152. ISBN 0-7695-0984-3

  10. Simon D, Hebert M, Kanade T (1994) Real-time 3-D pose estimation using a high-speed range sensor. SIMO94. In: Proceedings of the 1994 IEEE international conference on robotics and automation. IEEE, pp 2235–2241

  11. Schröter B (2008) Inertiale positionserfassung zur programmierung robotergestützter handhabungsaufgaben. Apprimus Verlag. ISBN 978-3-940565-18-1

  12. Klosowski J, Held M, Mitchell J et al (1998) Efficient collision detection using bounding volume hierarchies of k-DOPs. IEEE Trans Visual Comput Graph 4(1):21–36

    Article  Google Scholar 

  13. Khoshelham K (2010) Accuracy analysis of the kinect depth data. GeoInform Sci 38(5/W12):1–6

    Google Scholar 

  14. Pierce J, Agrawala M, Klemmer S et al (2011) KinectFusion. In: Proceedings of the 24th annual ACM symposium on user interface software and technology—UIST ‘11. ACM Press, New York, p 559

  15. Newcombe RA, Davison AJ, Izadi S et al (2011) KinectFusion: real-time dense surface mapping and tracking. In: Proceedings of the 10th IEEE international symposium on mixed and augmented reality. IEEE, pp 127–136

  16. Shaowen X, Zhenyu Y, Weiyong W (2010) Algorithm of denoising based on point cloud segmentation. SHAO10. In: Proceedings of the 5th international conference on computer science and education (ICCSE). IEEE, pp 1707–1711

Download references

Acknowledgments

The authors would like to thank the German Research Foundation DFG for the support of the depicted research within the Cluster of Excellence “Integrative Production Technology for High-Wage Countries”.

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Correspondence to Thomas Breitbach.

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Brecher, C., Breitbach, T., Ecker, C. et al. Environment sensing for the creation of work cell models. Prod. Eng. Res. Devel. 7, 329–338 (2013). https://doi.org/10.1007/s11740-013-0448-4

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