Development of Direct-printed Tactile Sensors for Gripper Control through Contact and Slip Detection

  • Ju-Kyoung Lee
  • Hyun-Hee Kim
  • Jae-Won Choi
  • Kyung-Chang Lee
  • Suk Lee
Regular Paper Robot and Applications


This work demonstrates the use of printed tactile sensors for detection of contact location and slip in a robot gripper. Research and development of robots for behaviors similar to those of humans are being conducted by many institutions. For these robot systems, flexible tactile sensors imitating human tactile senses have been developed and applied to robots. The sensors used in this work were fabricated through a direct-print process using a multi-walled carbon nanotube (MWCNT)/polymer composite. These sensors are a resistance type and were characterized by detecting changes in resistance of MWCNT networks within the composite in response to external forces. With tactile sensors attached to gripper fingers, signals generated when the gripper grasped objects were analyzed and the resulting information was used for robot gripper control.


Direct-print multi-walled carbon nanotube robot gripper signal processing slip detection tactile sensor 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    A. Joubair, L. F. Zhao, P. Bigras, and I. A. Bonev, “Use of a force-torque sensor for self-calibration of a 6-DOF medical robot,” Sensors, vol. 16, no. 6, p. 798, 2016.CrossRefGoogle Scholar
  2. [2]
    D. Wu, T. Chen, and A. Li, “A high precision approach to calibrate a structured light vision sensor in a robot-based three-dimensional measurement system,” Sensors, vol. 16, no. 9, p. 1388, 2016. [click]CrossRefGoogle Scholar
  3. [3]
    R. S. Dahiya, and M. Valle, Tactile Sensing for Robotic Applications, INTECH Open Access Publisher, 2008.Google Scholar
  4. [4]
    F. Alonso-Martín, M. Malfaz, J. Sequeira, J. F. Gorostiza, and M. A. Salichs, “A multimodal emotion detection system during human–robot interaction,” Sensors, vol. 13, no. 11, pp. 15549–15581, 2013. [click]CrossRefGoogle Scholar
  5. [5]
    Y. Jung, D.-G. Lee, J. Park, H. Ko, and H. Lim, “Piezoresistive tactile sensor discriminating multidirectional forces,” Sensors, vol. 15, no. 10, pp. 25463–25473, 2015. [click]CrossRefGoogle Scholar
  6. [6]
    J. W. Morley, A. W. Goodwin, and I. Darian-Smith, “Tactile discrimination of gratings,” Experimental Brain Research, vol. 49, no. 2, pp. 291–299, 1983.CrossRefGoogle Scholar
  7. [7]
    N. Sato, S. Shigematsu, H. Morimura, M. Yano, K. Kudou, T. Kamei, and K. Machida, “Novel surface structure and its fabrication process for MEMS fingerprint sensor,” IEEE Transactions on Electron Devices, vol. 52, no. 5, pp. 1026–1032, 2005.CrossRefGoogle Scholar
  8. [8]
    T. Someya, Y. Kato, T. Sekitani, S. Iba, Y. Noguchi, Y. Murase, H. Kawaguchi, and T. Sakurai, “Conformable, flexible, large-area networks of pressure and thermal sensors with organic transistor active matrixes,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 35, pp. 12321–12325, 2005. [click]CrossRefGoogle Scholar
  9. [9]
    G. S. Kim, and H. J. Shin, “Development of intelligent robot’s hand with three-axis finger force sensors for intelligent robot,” Journal of Institute of Control, Robotics and Systems, vol. 15, no. 3, pp. 300–305, 2009.CrossRefGoogle Scholar
  10. [10]
    H. Liu, F. Sun, D. Guo, B. Fang, and Z. Peng, “Structured output-associated dictionary learning for haptic understanding,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 7, pp. 1567–1574, 2017.Google Scholar
  11. [11]
    H. Liu, J. Qin, F. Sun, and D. Guo, “Extreme kernel sparse learning for tactile object recognition,” IEEE Transactions on Cybernetics, vol. 47, no. 12, pp. 4509–4520, Dec. 2017. [click]CrossRefGoogle Scholar
  12. [12]
    M. Vatani, Y. Lu, K.-S. Lee, H.-C. Kim, and J.-W. Choi, “Direct-write stretchable sensors using single-walled carbon nanotube/polymer matrix,” Journal of Electronic Packaging, vol. 135, no. 1, pp. 011009, 2013.CrossRefGoogle Scholar
  13. [13]
    M. Vatani, E. D. Engeberg, and J.-W. Choi, “Force and slip detection with direct-write compliant tactile sensors using multi-walled carbon nanotube/polymer composites,” Sensors and Actuators A: Physical, vol. 195, pp. 90–97, 2013. 13. [click]CrossRefGoogle Scholar
  14. [14]
    M. Vatani, E. D. Engeberg, and J. W. Choi, “Detection of the position, direction and speed of sliding contact with a multi-layer compliant tactile sensor fabricated using directprint technology,” Smart Materials and Structures, vol. 23, no. 9, pp. 1–11, 2014.CrossRefGoogle Scholar
  15. [15]
    M. Vatani, Y. Lu, E. D. Engeberg, and J. W. Choi, “Combined 3D printing technologies and material for fabrication of tactile sensors,” International Journal of Precision Engineering and Manufacturing, vol. 16, no. 7, pp. 1375–1383, 2015. [click]CrossRefGoogle Scholar
  16. [16]
    M. Vatani, E. D. Engeberg, and J.-W. Choi, “Conformal direct-print of piezoresistive polymer/nanocomposites for compliant multi-layer tactile sensors,” Additive Manufacturing, vol. 7, pp. 73–82, 2015. [click]CrossRefGoogle Scholar
  17. [17]
    I. Akita, and M. Ishida, “A 0.06 mm2 14nV/Hz chopper instrumentation amplifier with automatic differential-pair matching,” Proc. of IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC), pp. 178–179, 2013. [click]Google Scholar
  18. [18]
    E. D. Engeberg, and S. G. Meek, “Adaptive sliding mode control for prosthetic hands to simultaneously prevent slip and minimize deformation of grasped objects,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 1, pp. 376–385, 2013. [click]CrossRefGoogle Scholar
  19. [19]
    L. D. Harmon, “Automated tactile sensing,” The International Journal of Robotics Research, vol. 1, no. 2, pp. 3–32, 1982.CrossRefGoogle Scholar
  20. [20]
    R. S. Dahiya, and M. Valle, Robotic Tactile Sensing: Technologies and System, Springer Science & Business Media, 2012.Google Scholar
  21. [21]
    H. J. Lee, J.-K. Ryu, J. Kim, Y. J. Shin, K.-S. Kim, and S. Kim, “Design of modular gripper for explosive ordinance disposal robot manipulator based on modified dual-mode twisting actuation,” International Journal of Control, Automation and Systems, vol. 14, no. 5, pp. 1322–1330, 2016. [click]CrossRefGoogle Scholar
  22. [22]
    T. C. Phung, M. J. Kim, H. Moon, J. C. Koo, and H. R. Choi, “Exploration of local surface geometry with minimum number of contact points and surface normal information,” International Journal of Control, Automation and Systems, vol. 10, no. 2, pp. 383–395, 2012. [click]CrossRefGoogle Scholar
  23. [23]
    S. Lee and P. Y. Oh, “Sensor information analysis for a humanoid robot,” International Journal of Control, Automation, and Systems, vol. 13, no. 1, pp. 175, 2015. [click]CrossRefGoogle Scholar
  24. [24]
    A. Cavallo, G. De Maria, C. Natale, and S. Pirozzi, “Slipping detection and avoidance based on Kalman filter,” Mechatronics, vol. 24, no. 5, pp. 489–499, 2014.CrossRefGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ju-Kyoung Lee
    • 1
  • Hyun-Hee Kim
    • 2
  • Jae-Won Choi
    • 3
  • Kyung-Chang Lee
    • 2
  • Suk Lee
    • 1
  1. 1.School of Mechanical EngineeringPusan National UniversityBusanKorea
  2. 2.Department of Control and Instrumentation EngineeringPukyung National UniversityBusanKorea
  3. 3.Department of Mechanical EngineeringThe University of AkronAkronUSA

Personalised recommendations