Technologies for Other Robot Applications

  • Chenguang YangEmail author
  • Hongbin MaEmail author
  • Mengyin Fu


This chapter presents some robot applications and technologies which are not covered in the previous chapters. At first, we introduce the robot kicking and describe the inverse kinematics and software used in order to reach the best kick that a small humanoid can give to a ball. Next, a computational model of human motor reference trajectory adaptation has been developed. This adaptation model aims to satisfy a desired impedance model to minimize interaction force and performance error. It can also be used as a robotic motion planner when robot interacts with objects of unknown shapes.


Force Field Interaction Force Humanoid Robot Inverse Kinematic Joint Torque 
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  1. 1.
    Boue, T., Yang, C., Ma, H., Culverhouse, P.: Investigation of best robot kicking. In: 32nd Chinese Control Conference (CCC) (2013)Google Scholar
  2. 2.
    Sariyildiz, E., Cakiray, E., Temeltas, H.: A comparative study of three inverse kinematic methods of serial industrial robot manipulators in the screw theory framework. Int. J. Adv. Robot. Syst. 8(5), 9–24 (2011)Google Scholar
  3. 3.
    Lees, A., Nolan, L.: The biomechanics of soccer: a review. J. Sport. Sci. 16(3), 211–234 (1998)CrossRefGoogle Scholar
  4. 4.
    Joy, K.I.: Cubic uniform b-spline curve refinement. In: On-Line Geometric Modeling Notes (2000)Google Scholar
  5. 5.
  6. 6.
    Hu, J., Queiroz, M., Burg, T., Dawson, D.: Adaptive position/force control of robot manipulators without velocity measurements. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 887–892 (1995)Google Scholar
  7. 7.
    Hogan, N.: Impedance control: an approach to manipulation: part implementation. J. Dyn. Syst. Meas. Control 107(1), 8–16 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Yang, C., Burdet, E.: A model of reference trajectory adaptation for interaction with objects of arbitrary shape and impedance. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 4121–4126 (2011)Google Scholar
  9. 9.
    Tee, K.P., Franklin, D.W., Kawato, M., Milner, T.E., Burdet, E.: Concurrent adaptation of force and impedance in the redundant muscle system. Biol. Cybern. 102(1), 31–44 (2010)CrossRefzbMATHGoogle Scholar
  10. 10.
    Chib, V.S., Patton, J.L., Lynch, K.M., Mussa-Ivaldi, F.A.: Haptic identification of surfaces as fields of force. J. Neurophysiol. 95(2), 2006 (2006)Google Scholar
  11. 11.
    Hirsch, M.W., Smale, S.: Differential equations, dynamical systems and linear algebra. In: A subsidiary of Harcourt Brace Jovanovich, Academic Press Publishers, New York (1974)Google Scholar
  12. 12.
    Wang, D., Cheah, C.C.: An iterative learning control scheme for impedance control of robotic manipulators. Int. J. Robot. Res. 17(10), 1091–1104 (1998)CrossRefGoogle Scholar
  13. 13.
    Tao, G.: Adaptive Control Design and Analysis. Wiley, Hoboken (2003)Google Scholar
  14. 14.
    Burdet, E., Tee, K.P., Mareels, I., Milner, T.E., Chew, C.M., Franklin, D.W., Osu, R., Kawato, M.: Stability and motor adaptation in human arm movements. Biol. Cybern. 94(1), 20–32 (2006)CrossRefzbMATHGoogle Scholar

Copyright information

© Science Press and Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Key Lab of Autonomous Systems and Networked Control, Ministry of EducationSouth China University of TechnologyGuangzhouChina
  2. 2.Centre for Robotics and Neural SystemsPlymouth UniversityDevonUK
  3. 3.School of AutomationBeijing Institute of TechnologyBeijingChina
  4. 4.State Key Lab of Intelligent Control and Decision of Complex SystemBeijing Institute of TechnologyBeijingChina

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