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Cooperative Slip Detection Using a Dual-Arm Baxter Robot

  • Shane TrimbleEmail author
  • Wasif Naeem
  • Seán McLoone
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 924)

Abstract

When dealing with robotic manipulation tasks, slip detection and control is vital to overcome payload uncertainties and to compensate for external disturbances. Many modern smart manipulators are shipped with integrated joint torque sensing capabilities providing a potential means of detecting slip and hence generating a feedback signal for slip control, without the need for additional external sensors. This paper investigates preliminary results on slip detection with an aim to extend the existing work on single manipulator to a dual arm cooperative manipulator system. The slip signal is obtained through filtering of onboard joint torque measurements when different materials are held in a friction grip between the two cooperating robotic end-effectors of a Baxter robot.

Frequency domain analysis of data from preliminary experiments shows a substantial frequency component in the vicinity of 12 Hz when slip is taking place. Consequently, a 9–15 Hz band-pass Chebyshev filtered torque signal is proposed as a real-time slip detection signal and its performance evaluated in experiments using a selection of materials with differing mass, coefficient of friction and thickness.

Analysis of the resulting filter output and threshold signal for each material revealed that the proposed approach was effective at detecting slip for some materials (laminated card, MDF, PVC and Perspex) but not for others (sheet steel, cardboard and polypropylene). This is attributed to the variation in consistency in slip behaviour of different material/weight combinations.

References

  1. 1.
    Gerling, G.J., Rivest, I.I., Lesniak, D.R., Scanlon, J.R., Wan, L.: Validating a population model of tactile mechanotransduction of slowly adapting type I afferents at levels of skin mechanics, single-unit response and psychophysics. IEEE Trans. Haptics 7, 216–228 (2014)CrossRefGoogle Scholar
  2. 2.
    Deng, H., Zhong, G., Li, X., Nie, W.: Slippage and deformation preventive control of bionic prosthetic hands. IEEE/ASME Trans. Mechatron. 22, 888–897 (2017)CrossRefGoogle Scholar
  3. 3.
    Xian, Z., Lertkultanon, P., Pham, Q.C.: Closed-chain manipulation of large objects by multi-arm robotic systems. IEEE Rob. Autom. Lett. 2, 1832–1839 (2017)CrossRefGoogle Scholar
  4. 4.
    Won, H.-I., Chung, J.: Numerical analysis for the stick-slip vibration of a transversely moving beam in contact with a frictional wall. J. Sound Vibr. 419, 42–62 (2018)CrossRefGoogle Scholar
  5. 5.
    Khurshid, R.P., Fitter, N.T., Fedalei, E.A., Kuchenbecker, K.J.: Effects of grip-force, contact, and acceleration feedback on a teleoperated pick-and-place task. IEEE Trans. Haptics 10, 40–53 (2017)CrossRefGoogle Scholar
  6. 6.
    Erhart, S., Hirche, S.: Model and analysis of the interaction dynamics in cooperative manipulation tasks. IEEE Trans. Rob. 32, 672–683 (2016)CrossRefGoogle Scholar
  7. 7.
    Saen, M., Ito, K., Osada, K.: Action-intention-based grasp control with fine finger-force adjustment using combined optical-mechanical tactile sensor. IEEE Sens. J. 14, 4026–4033 (2014)CrossRefGoogle Scholar
  8. 8.
    Aqilah, A., Jaffar, A., Bahari, S., Low, C.Y., Koch, T.: Resistivity characteristics of single miniature tactile sensing element based on pressure sensitive conductive rubber sheet. In: 2012 IEEE 8th International Colloquium on Signal Processing and its Applications, pp. 223–227, March 2012Google Scholar
  9. 9.
    Teshigawara, S., Ishikawa, M., Shimojo, M.: Development of high speed and high sensitivity slip sensor. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 47–52, September 2008Google Scholar
  10. 10.
    Teshigawara, S., Tadakuma, K., Ming, A., Ishikawa, M., Shimojo, M.: High sensitivity initial slip sensor for dexterous grasp. In: 2010 IEEE International Conference on Robotics and Automation, pp. 4867–4872, May 2010Google Scholar
  11. 11.
    Engeberg, E.D., Meek, S.G.: Adaptive sliding mode control for prosthetic hands to simultaneously prevent slip and minimize deformation of grasped objects. IEEE/ASME Trans. Mechatron. 18, 376–385 (2013)CrossRefGoogle Scholar
  12. 12.
    Schoepfer, M., Schuermann, C., Pardowitz, M., Ritter, H.: Using a Piezo-resistive tactile sensor for detection of incipient slippage. In: ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics), pp. 1–7, June 2010Google Scholar
  13. 13.
    Dzitac, P., Mazid, A.M., Ibrahim, M.Y., Appuhamillage, G.K., Choudhury, T.A.: Optimal sensing requirement for slippage prevention in robotic grasping. In: 2015 IEEE International Conference on Industrial Technology (ICIT), pp. 373–378, March 2015Google Scholar
  14. 14.
    Li, Z., Tao, P.Y., Ge, S.S., Adams, M., Wijesoma, W.S.: Robust adaptive control of cooperating mobile manipulators with relative motion. IEEE Trans. Syst. Man. Cybern. Part B (Cybern.) 39, 103–116 (2009)CrossRefGoogle Scholar
  15. 15.
    Lotfavar, A., Hasanzadeh, S., Janabi-Sharifi, F.: Cooperative continuum robots: concept, modeling, and workspace analysis. IEEE Rob. Autom. Lett. 3, 426–433 (2018)CrossRefGoogle Scholar
  16. 16.
    Arshad, A., Badshah, S., Soori, P.K.: Design and fabrication of smart robots. In: 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), pp. 1–4, December 2016Google Scholar
  17. 17.
    Makrini, I.E., Rodriguez-Guerrero, C., Lefeber, D., Vanderborght, B.: The variable boundary layer sliding mode control: a safe and performant control for compliant joint manipulators. IEEE Rob. Autom. Lett. 2, 187–192 (2017)Google Scholar
  18. 18.
    Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., Yin, B.: Smart factory of industry 4.0: key technologies, application case, and challenges. IEEE Access 6, 6505–6519 (2018)CrossRefGoogle Scholar
  19. 19.
    ReThink Robotics: Baxter hardware specifications, May 2014. Accessed 14 Jan 2018Google Scholar
  20. 20.
    Cremer, S., Mastromoro, L., Popa, D.O.: On the performance of the baxter research robot. In: 2016 IEEE International Symposium on Assembly and Manufacturing (ISAM), pp. 106–111, August 2016Google Scholar
  21. 21.
    Rethink robotics opens up baxter robot for researchers (2018). Accessed 5 Feb 2018Google Scholar
  22. 22.
    Stachowsky, M., Hummel, T., Moussa, M., Abdullah, H.A.: A slip detection and correction strategy for precision robot grasping. IEEE/ASME Trans. Mechatron. 21, 2214–2226 (2016)CrossRefGoogle Scholar
  23. 23.
    Damian, D.D., Newton, T.H., Pfeifer, R., Okamura, A.M.: Artificial tactile sensing of position and slip speed by exploiting geometrical features. IEEE/ASME Trans. Mechatron. 20, 263–274 (2015)CrossRefGoogle Scholar
  24. 24.
    Tuononen, A.J.: Onset of frictional sliding of rubber–glass contact under dry and lubricated conditions. Sci. Rep. 6(1) (2016)Google Scholar
  25. 25.
    Xu, F., Yoshimura, K.-I., Mizuta, H.: Experimental study on friction properties of rubber material: influence of surface roughness on sliding friction. Procedia Eng. 68, 19–23 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Electronics, Electrical Engineering and Computer ScienceQueen’s University BelfastBelfastUK

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