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Towards Tool-Support for Robot-Assisted Product Creation in Fab Labs

  • Jan Van den Bergh
  • Bram van Deurzen
  • Tom Veuskens
  • Raf Ramakers
  • Kris Luyten
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11262)

Abstract

Collaborative robot-assisted production has great potential for high variety low volume production lines. These type of production lines are common in both personal fabrication settings as well as in several types of flexible production lines. Moreover, many assembly tasks are in fact hard to complete by a single user or a single robot, and benefit greatly from a fluent collaboration between both. However, programming such systems is cumbersome, given the wide variation of tasks and the complexity of instructing a robot how it should move and operate in collaboration with a human user.

In this paper we explore the case of collaborative assembly for personal fabrication. Based on a CAD model of the envisioned product, our software analyzes how this can be composed from a set of standardized pieces and suggests a series of collaborative assembly steps to complete the product. The proposed tool removes the need for the end-user to perform additional programming of the robot. We use a low-cost robot setup that is accessible and usable for typical personal fabrication activities in Fab Labs and Makerspaces. Participants in a first experimental study testified that our approach leads to a fluent collaborative assembly process. Based on this preliminary evaluation, we present next steps and potential implications.

Keywords

Human-robot collaboration Toolkit End-user development 

Notes

Acknowledgements

This research was partially supported by Flanders Make, the strategic research centre for the manufacturing industry. We thank the reviewers for their constructive comments.

References

  1. 1.
    Baizid, K., et al.: IRoSim: Industrial Robotics Simulation Design Planning and Optimization platform based on CAD and knowledgeware technologies. Robot. Comput. Integr. Manuf. 42 (2016).  https://doi.org/10.1016/j.rcim.2016.06.003
  2. 2.
    Van den Bergh, J., Luyten, K.: Dice-R: defining human-robot interaction with composite events. In: Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems, EICS 2017, pp. 117–122. ACM, New York (2017).  https://doi.org/10.1145/3102113.3102147
  3. 3.
    Duelen, G., Bernhardt, R., Schreck, G.: Use of CAD-data for the off-line programming of industrial robots. Robotics 3(3), 389–397 (1987).  https://doi.org/10.1016/0167-8493(87)90055-6CrossRefGoogle Scholar
  4. 4.
    Funk, M., Kosch, T., Schmidt, A.: Interactive worker assistance: comparing the effects of in-situ projection, head-mounted displays, tablet, and paper instructions. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, pp. 934–939. ACM, New York (2016).  https://doi.org/10.1145/2971648.2971706
  5. 5.
    Haug, A.: Work instruction quality in industrial management. Int. J. Ind. Ergon. 50, 170–177 (2015).  https://doi.org/10.1016/j.ergon.2015.09.015CrossRefGoogle Scholar
  6. 6.
    Mateo, C., Brunete, A., Gambao, E., Hernando, M.: Hammer: an android based application for end-user industrial robot programming. In: 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp. 1–6. IEEE (2014).  https://doi.org/10.1109/MESA.2014.6935597
  7. 7.
    Neto, P., Mendes, N.: Direct off-line robot programming via a common CAD package. Robot. Auton. Syst. 61(8), 896–910 (2013).  https://doi.org/10.1016/j.robot.2013.02.005CrossRefGoogle Scholar
  8. 8.
    Neto, P., Mendes, N., Araújo, R., Norberto Pires, J., Paulo Moreira, A.: High-level robot programming based on CAD: dealing with unpredictable environments. Ind. Robot Int. J. 39(3), 294–303 (2012).  https://doi.org/10.1108/01439911211217125CrossRefGoogle Scholar
  9. 9.
    Orendt, E.M., Fichtner, M., Henrich, D.: Robot programming by non-experts: intuitiveness and robustness of one-shot robot programming. In: 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 192–199. IEEE (2016).  https://doi.org/10.1109/ROMAN.2016.7745110
  10. 10.
    Paxton, C., Hundt, A., Jonathan, F., Guerin, K., Hager, G.D.: CoSTAR: instructing collaborative robots with behavior trees and vision. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 564–571. IEEE (2017).  https://doi.org/10.1109/ICRA.2017.7989070
  11. 11.
    Pedersen, M.R., Krüger, V.: Gesture-based extraction of robot skill parameters for intuitive robot programming. J. Intell. Robotic Syst. 80(1), 149–163 (2015).  https://doi.org/10.1007/s10846-015-0219-xCrossRefGoogle Scholar
  12. 12.
    Quigley, M., et al.: Ros: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p. 5 (2009)Google Scholar
  13. 13.
    Resnick, M., et al.: Scratch: programming for all. Commun. ACM 52(11), 60–67 (2009).  https://doi.org/10.1145/1592761.1592779CrossRefGoogle Scholar
  14. 14.
    Sefidgar, Y.S., Agarwal, P., Cakmak, M.: Situated tangible robot programming. In: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2017, pp. 473–482. ACM, New York (2017).  https://doi.org/10.1145/2909824.3020240

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.Hasselt University - tUL - Flanders Make, Expertise Centre for Digital MediaDiepenbeekBelgium
  2. 2.Hasselt UniversityHasseltBelgium

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