Advertisement

Development of Adaptive Force-Following Impedance Control for Interactive Robot

  • Huang JianbinEmail author
  • Li Zhi
  • Liu Hong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)

Abstract

This paper presented a safety approach for the interactive manipulator. At first, the basic compliance control of the manipulator is realized by using the Cartesian impedance control, which inter-related the external force and the end position. In this way, the manipulator could work as an external force sensor. A novel force-limited trajectory was then generated in a high dynamics interactive manner, keeping the interaction force within acceptable tolerance. The proposed approach also proved that the manipulator was able to contact the environment compliantly, and reduce the instantaneous impact when collision occurs. Furthermore, adaptive dynamics joint controller was extended to all the joints for complementing the biggish friction. Experiments were performed on a 5-DOF flexible joint manipulator. The experiment results of taping the obstacle, illustrate that the interactive robot could keep the desired path precisely in free space, and follow the demand force in good condition.

Keywords

Interactive robot Cartesian impedance control Collision detection 

References

  1. 1.
    Mühlig, M., Gienger, M., Steil, J.J.: Interactive imitation learning of object movement skills. Auton. Robot. 32(2), 97–114 (2012)CrossRefGoogle Scholar
  2. 2.
    Haddadin, S., Albu-Schäffer, A., Hirzinger, G.: Safe physical human-robot interaction: measurements analysis and new insights. In: Kaneko, M., Nakamura, Y. (eds.) Robotics Research. Springer Tracts in Advanced Robotics, vol. 66. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-14743-2_33CrossRefGoogle Scholar
  3. 3.
    Huang, J.B., et al.: DSP/FPGA-based controller architecture for flexible joint robot with enhanced impedance performance. J. Intell. Robot. Syst. 53(3), 247 (2008)CrossRefGoogle Scholar
  4. 4.
    Doggett, W.R., et al.: Development of a Tendon-Actuated Lightweight In-Space MANipulator (TALISMAN) (2014)Google Scholar
  5. 5.
    Albu-Schäffer, A., Bicchi, A.: Actuators for Soft Robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-32552-1_21CrossRefGoogle Scholar
  6. 6.
    Olson, M.W.: Passive trunk loading influences muscle activation during dynamic activity. Muscle Nerve 44(5), 749 (2011)CrossRefGoogle Scholar
  7. 7.
    Hongwei, Z., Ahmad, S., Liu, G.: Torque estimation for robotic joint with harmonic drive transmission based on position measurements. IEEE Trans. Robot. 31(2), 322–330 (2017)Google Scholar
  8. 8.
    Kulić, D., Croft, E.: Pre-collision safety strategies for human-robot interaction. Auton. Robot. 22(2), 149–164 (2007)CrossRefGoogle Scholar
  9. 9.
    Hogan, N.: Impedance control: an approach to manipulation: theory (part 1); implementation (part 2); applications (part 3). ASME J. Dyn. Syst. Measur. Contr. 107, 1–24 (1985)CrossRefGoogle Scholar
  10. 10.
    Kazerooni, H., Sheridan, T.B., Houpt, P.K.: Robust compliant motion for manipulators: the fundamental concepts of compliant motion (part I); design method (part II). IEEE J. Robot. Autom. 2(2), 83–105 (1986)CrossRefGoogle Scholar
  11. 11.
    Brock, O., Khatib, O.: Elastic strips: a framework for motion generation in human environments. Int. J. Robot. Res. 21(12), 1031–1052 (2002)CrossRefGoogle Scholar
  12. 12.
    Wu, X.D., et al.: Parameter identification for a LuGre model based on steady-state tire conditions. Int. J. Automot. Technol. 12(5), 671 (2011)CrossRefGoogle Scholar
  13. 13.
    Hamon, P., et al.: Dynamic identification of robot with a load-dependent joint friction model, pp. 129–135 (2015)Google Scholar
  14. 14.
    Huang, J.B., et al.: Adaptive cartesian impedance control system for flexible joint robot by using DSP/FPGA architecture. Int. J. Robot. Autom. 23(4), 251–258 (2008)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Qian Xuesen Laboratory of Space Technology, China Academy of Space TechnologyBeijingPeople’s Republic of China
  2. 2.State Key Laboratory of Robotics and SystemHarbin Institute of TechnologyHarbinPeople’s Republic of China

Personalised recommendations