Advertisement

Impedance Identification Using Tactile Sensing and Its Adaptation for an Underactuated Gripper Manipulation

  • Zhong-Yi Chu
  • Shao-Bo Yan
  • Jian Hu
  • Shan Lu
Regular Paper Robot and Applications
  • 44 Downloads

Abstract

Underactuated gripper has a broad application in the field of space robot and industrial robot because of its better shape-adaptation. However, because of the underactuated characteristics, it is a great challenge to accurately obtain the displacement of the contact point between the finger and grasped object, which makes it difficult to control the gripper grasp stably, especially the environmental parameters are unknown. This paper develops the identification of the unknown environmental parameters using a tactile array sensor based on the recursive leastsquares (RLS) method. The unknown environments are described as linear systems with unknown dynamics, and the environmental parameters are identified using the measured contact force and the derived displacement of the contact point which is obtained through the underactuated gripper dynamics. Meanwhile, an impedance adaptive control is presented to match the variability of the environment parameters, and the desired impedance model is imposed to the underactuated gripper to achieve stable grasp. A cost function that measures the contact force, velocity and displacement error is defined, and the critical impedance parameters are found to minimize it. At last, a co-simulation of ADAMS and MATLAB for an underactuated gripper grasp is implemented to show the feasibility of environmental parameters identification and its adaptive method.

Keywords

Environmental parameters identification impedance adaptation stable grasp underactuated gripper 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    A. Kragten, F. C. T. van der Helm, and J. L. Herder, “A planar geometric design approach for a large grasp range in underactuated hands,” Mechanism and Machine Theory, vol. 46, no. 8, pp. 1121–1136, March 2011.CrossRefzbMATHGoogle Scholar
  2. [2]
    L. Birglen and C. M. Gosselin, “On the force capability of underactuated fingers,” Proc. of IEEE Int. Conf. on Robotics and Automation, 2003.Google Scholar
  3. [3]
    S. Krut, V. Begoc, E. Dombre, and F. Pierrot, “Extension of the form-closure property to underactuated hands,” IEEE Transactions on Robotics, vol. 26, no. 5, pp. 853–866, September 2010.CrossRefGoogle Scholar
  4. [4]
    L. Odhner and A. Dollar, “Dexterous manipulation with underactuated elastic hands,” Proc. IEEE Int. Conf. Robot. Autom., Karlsruhe, Germany, pp. 5254–5260, May 2011.Google Scholar
  5. [5]
    M. N. Luo, F. C. Sun, and H. P. Liu, “Dynamic T-S fuzzy systems identification based on sparse regularization,” Asian Journal of Control, vol. 17, no. 1, pp. 274–283, January 2015.MathSciNetCrossRefzbMATHGoogle Scholar
  6. [6]
    R. Ma, H. P. Liu, F. C. Sun, Q. F. Yang, and M. Gao, “Linear dynamic system method for tactile object classification,” Science in China. Series F: Information Sciences, vol. 57, no. 12, pp. 1–11, December 2014.Google Scholar
  7. [7]
    C.-P. Fritzen, “Identification of mass, damping, and stiffness matrices of mechanical system,” Journal of Vibration and Acoustics, vol. 108, no. 1, pp. 9–16, January 1986.CrossRefGoogle Scholar
  8. [8]
    S. S. Ge, Y. N. Li, and C. Wang, “Impedance adaptation for optimal robot-environment interaction,” International Journal of Control, vol. 87, no. 2, pp. 249–263, September 2013. [click]MathSciNetCrossRefzbMATHGoogle Scholar
  9. [9]
    N. Hogan, “Impedance control: an approach to manipulation-part I: theory; part II: implementation; part III: applications,” Transaction ASME Journal of Dynamic Systems Measurement and Control, vol. 107, no. 1, pp. 17–24, 1985.CrossRefzbMATHGoogle Scholar
  10. [10]
    J. Yoon, A. Manurung, and G.-S. Kim, “Impedance control of a small treadmill with sonar sensors for automatic speed adaptation,” International Journal of Control, Automation, and Systems, vol. 12, no. 6, pp. 1323–1335, February 2014.CrossRefGoogle Scholar
  11. [11]
    M. Salehi and G. Vossoughi, “Impedance control of flexible base mobile manipulator using singular perturbation method and sliding mode control law,” International Journal of Control, Automation, and Systems, vol. 6, no. 5, pp. 677–688, October 2008.Google Scholar
  12. [12]
    L. Biagiotti, H. Liu, G. Hirzinger, and C. Melchiorri, “Cartesian impedance control for dexterous manipulation,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Las Vegas, Nevada, October 2003.Google Scholar
  13. [13]
    J. E. Colgate and N. Hogan, “Robust control of dynamically interacting systems,” International Journal of Control, vol. 48, no. 1, pp. 65–88, 1988.MathSciNetCrossRefzbMATHGoogle Scholar
  14. [14]
    R. Z. Stanisic and A. V. Fernandez, “Adjusting the parameters of the mechanical impedance for velocity, impact and force control,” Robotica, vol. 30, no. 4, pp. 583–597, 2012. [click]CrossRefGoogle Scholar
  15. [15]
    Y. Jiang and Z. P. Jiang, “Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics,” Automatica, vol. 48, no. 10, pp. 2699–2704, October 2012. [click]MathSciNetCrossRefzbMATHGoogle Scholar
  16. [16]
    L. Birglen, T. Laliberte, and C. Gosselin, Underactuated Robotic Hands, Springer-Verlag, 2008.zbMATHGoogle Scholar
  17. [17]
    Z. Y. Chu, J. Hu, and Y. A. Lei, An Adaptive Gripper of Space Robot for Space On-orbit Services, CN 201310326633.7, 2013, China patent.Google Scholar
  18. [18]
    M. Zhou and Z. Y. Chu, “Impedance joint torque control of an active-passive composited driving self-adaptive end effector for space manipulator,” Proc. of the 11th World Congress on Intelligent Control and Automation, Shenyang, China, June 29-July 4 2014.Google Scholar
  19. [19]
    Y. J. Liu and F. Ding, “Convergence properties of the least squares estimation algorithm for multivariable systems,” Applied Mathematical Modelling, vol. 37, no. 1-2, pp. 476–483, January 2013. [click]MathSciNetCrossRefzbMATHGoogle 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

  1. 1.School of Instrument Science and Opto-electronicsBeihang UniversityBeijingChina
  2. 2.Shanghai Institute of Spaceflight Control TechnologyShanghai Key Laboratory of Aerospace Intelligent Control TechnologyShanghaiChina

Personalised recommendations