Skip to main content

Automatized Switchgear Wiring: An Outline of the WIRES Experiment Results

  • Chapter
  • First Online:
Advances in Robotics Research: From Lab to Market

Abstract

This chapter reports an overview of the experience and the results achieved during the development of the robotized system for switchgear wiring carried out in the WIRES experiment. This specific application is particularly challenging for a robotic system due to the complexity of the manipulation task. As a matter of fact, in this task deformable linear objects, such as electric wires, are involved. Moreover, the precision requested during the assembly task and the typical crowded space inside the switchgear imply, on one side, the development of specific hardware and software tools and, on the other side, high adaptability and flexibility of the robotic system. In the WIRES experiment, a software package to extract the wiring information and to generate the robot task sequence directly from the switchgear CAD files has been developed. Additionally, a computer vision system able to recognize the location of the wires and of the electromechanical components inside the switchgear has been developed. To deal with the wire deformability and occlusion problems during the wire insertion, machine learning and sensor fusion techniques have been adopted to enable the wire manipulation by means of tactile sensors and 2D cameras feedback. From the hardware point of view, a specific end effector has been developed to manipulate and connect the wire to the components. This end effector is equipped with an electric screwdriver and a customized tactile sensor used to evaluate the wire shape, the wire end pose and its interaction with the environment during the manipulation. The entire task pipeline, going from the switchgear information extraction, to the wire grasp and manipulation, its connection and the routing along the desired wire path is presented in this chapter. The preliminary experimental results show that the developed system can achieve a success rate of about 95% in the wire insertion and connection task.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Website of the wires experiment. http://www-lar.deis.unibo.it/people/gpalli/WIRES/

  2. Busi, M., Cirillo, A., De Gregorio, D., Indovini, M., De Maria, G., Melchiorri, C., Natale, C., Palli, G., Pirozzi, S.: The wires experiment: tools and strategies for robotized switchgear cabling. Procedia Manuf. 11, 355–363 (2017)

    Article  Google Scholar 

  3. Cirillo, A., De Maria, G., Natale, C., Pirozzi, S.: Design and evaluation of tactile sensors for the estimation of grasped wire shape. In: Proceedings of IEEE International Conference on Advanced Intelligent Mechatronics, Munich, Germany, pp. 490–496 (2017)

    Google Scholar 

  4. Saxena, A., Driemeyer, J., Ng, A.Y.: Robotic grasping of novel objects using vision. Int. J. Robot. Res. 27(2), 157–173 (2008)

    Article  Google Scholar 

  5. Popović, M., Kraft, D., Bodenhagen, L., Başeski, E., Pugeault, N., Kragic, D., Asfour, T., Krüger, N.: A strategy for grasping unknown objects based on co-planarity and colour information. Robot. Auton. Syst. 58(5), 551–565 (2010)

    Article  Google Scholar 

  6. Allen, P.K.: Integrating vision and touch for object recognition tasks. Int. J. Robot. Res. 7(6), 15–33 (1988)

    Article  Google Scholar 

  7. Bimbo, J., Seneviratne, L.D., Althoefer, K., Liu, H.: Combining touch and vision for the estimation of an object’s pose during manipulation. In: Proceedings of International Conference on Intelligent Robots and Systems, pp. 4021–4026 (2013)

    Google Scholar 

  8. Björkman, M., Bekiroglu, Y., Högman, V., Kragic, D.: Enhancing visual perception of shape through tactile glances. In: Proceedings of International Conference on Intelligent Robots and Systems, pp. 3180–3186 (2013)

    Google Scholar 

  9. Bhattacharjee, T., Shenoi, A.A., Park, D., Rehg, J.M., Kemp, C.C.: Combining tactile sensing and vision for rapid haptic mapping. In: Proceedings of International Conference on Intelligent Robots and Systems, pp. 1200–1207 (2015)

    Google Scholar 

  10. Jamali, N., Ciliberto, C., Rosasco, L., Natale, L.: Active perception: building objects’ models using tactile exploration. In: Proceedings of International Conference on Humanoid Robots (Humanoids), pp. 179–185 (2016)

    Google Scholar 

  11. Lepora, N.F., Aquilina, K., Cramphorn, L.: Exploratory tactile servoing with active touch. IEEE Robot. Autom. Lett. 2(2), 1156–1163 (2017)

    Article  Google Scholar 

  12. Falco, P., Lu, S., Cirillo, A., Natale, C., Pirozzi, S., Lee, D.: Cross-modal visuo-tactile object recognition using robotic active exploration. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 5273–5280 (2017)

    Google Scholar 

  13. Tombari, F., Franchi, A., Di, L.: Bold features to detect texture-less objects. In: 2013 IEEE International Conference on Computer Vision, pp. 1265–1272 (2013)

    Google Scholar 

  14. De Gregorio, D., Tonioni, A., Palli, G., Di Stefano, L.: Semi-automatic labeling for deep learning in robotics. In: Submitted to International Conference on Intelligent Robots and Systems (2018)

    Google Scholar 

  15. De Maria, G., Natale, C., Pirozzi, S.: Force/tactile sensor for robotic applications. Sens. Actuators A: Phys. 175, 60–72 (2012)

    Google Scholar 

  16. De Maria, G., Natale, C., Pirozzi, S.: Tactile data modeling and interpretation for stable grasping and manipulation. Robot. Auton. Syst. 61(9), 1008–1020 (2013)

    Google Scholar 

  17. De Gregorio, D., Zanella, R., Palli, G., Pirozzi, S., Melchiorri, C.: Integration of robotic vision and tactile sensing for wire-terminal insertion tasks. IEEE Trans. Autom. Sci. Eng. 16(2), 585–598 (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gianluca Palli .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Palli, G., Pirozzi, S., Indovini, M., De Gregorio, D., Zanella, R., Melchiorri, C. (2020). Automatized Switchgear Wiring: An Outline of the WIRES Experiment Results. In: Grau, A., Morel, Y., Puig-Pey, A., Cecchi, F. (eds) Advances in Robotics Research: From Lab to Market. Springer Tracts in Advanced Robotics, vol 132. Springer, Cham. https://doi.org/10.1007/978-3-030-22327-4_6

Download citation

Publish with us

Policies and ethics