Advertisement

User-Friendly Intuitive Teaching Tool for Easy and Efficient Robot Teaching in Human-Robot Collaboration

  • Hyunmin Do
  • Taeyong Choi
  • Dong Il Park
  • Hwi-su Kim
  • Chanhun Park
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

Production automation by human-robot collaboration has drawn significant attention due to increasing demands for automation in the manufacturing process of small electronic products, which were previously manufactured manually. Accordingly, the research for human-robot collaboration is being actively conducted and intuitive teaching is an essential technology to realize easy and efficient teaching of a collaborative robot. This paper proposes an intuitive teaching tool attached to a robot end effector that can accurately teach motions to a robot manipulator, without being affected by sensor noises. This device consists of three parts: a motion operation part for teaching six degrees of freedom motion of the robot, a motion setting part which consists of core functions necessary for teaching and a status display part for displaying the status of the teaching device and the robot. It is designed to perform teaching work by a combination of twelve switches which have one-to-one mapping relation to each degree of freedom of motion. A prototype has been implemented to verify the performance and has been applied to an experiment of six degrees of freedom motion teaching with UR5 robot.

Keywords

Intuitive teaching tool Robot teaching Human-robot collaboration 

Notes

Acknowledgements

This work was supported by the Ministry of Trade, Industry & Energy and KEIT under program number 10063413.

References

  1. 1.
    Do, H.M., Choi, T.-Y., Kyung, J.H.: Automation of cell production system for cellular phones using dual-arm robots. Int. J. Adv. Manuf. Technol. 83, 1349–1360 (2016)CrossRefGoogle Scholar
  2. 2.
    Thomas, C., Matthias, B., Kuhlenktter, B.: Human-robot collaboration - new applications in industrial robotics. In: International Conference on Competitive Manufacturing, pp. 293–299 (2016)Google Scholar
  3. 3.
    Coupete, E., Weistroffer, V., Hugues, O.: New challenges for human-robot collaboration in an industrial context. In: Fifth Workshop “Towards a Framework for Joint Action”, IEEE International Symposium on Robot and Human Interactive Communication (2016)Google Scholar
  4. 4.
    Bauer, A., Wollherr, D., Buss, M.: Human-robot collaboration: a survey. Int. J. Humanoid Rob. 5, 47–66 (2008)CrossRefGoogle Scholar
  5. 5.
    Johannsmeier, L., Haddadin, S.: A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes. IEEE Rob. Autom. Lett. 2, 41–48 (2017)CrossRefGoogle Scholar
  6. 6.
    Lenz, C., Knoll, A.: Mechanisms and capabilities for human robot collaboration. In: IEEE International Symposium on Robot and Human Interactive Communication, pp. 666–671 (2014)Google Scholar
  7. 7.
    Sheng, W., Thobbi, A., Gu, Y.: An integrated framework for human-robot collaborative manipulation. IEEE Trans. Cybern. 45, 2030–2041 (2015)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Schraft, R.D., Christian, M.: The need for an intuitive teaching method for small and medium enterprises. In: Joint Conference on Robotics, 37th International Symposium on Robotics and 4th German Conference on Robotics, pp. 95–105 (2006)Google Scholar
  10. 10.
    Stopp, A., Horstmann, S., Kristensen, S., Kohnert, F.: Towards interactive learning for manufacturing assistants. In: IEEE International Symposium on Robot and Human Interactive Communication, pp. 338–342 (2001)Google Scholar
  11. 11.
    Park, C., Kyung, J., Park, D.I., Gweon, D.-G.: Direct teaching algorithm for a manipulator in a constraint condition using the teaching force shaping method. Adv. Robot. 24, 1365–1384 (2010)CrossRefGoogle Scholar
  12. 12.
  13. 13.
  14. 14.
  15. 15.
    Do, H.M., Kim, H., Park, D.I., Choi, T.Y., Park, C.: Intuitive and safe teaching device for efficient human-robot collaboration. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2272 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hyunmin Do
    • 1
  • Taeyong Choi
    • 1
  • Dong Il Park
    • 1
  • Hwi-su Kim
    • 1
  • Chanhun Park
    • 1
  1. 1.Department of Robotics and MechatronicsKorea Institute of Machinery and Materials (KIMM)DaejeonKorea

Personalised recommendations