Visual motor control of a 7 DOF robot manipulator using a fuzzy SOM network

Original Research Paper


A fuzzy self-organizing map (SOM) network is proposed in this paper for visual motor control of a 7 degrees of freedom (DOF) robot manipulator. The inverse kinematic map from the image plane to joint angle space of a redundant manipulator is highly nonlinear and ill-posed in the sense that a typical end-effector position is associated with several joint angle vectors. In the proposed approach, the robot workspace in image plane is discretized into a number of fuzzy regions whose center locations and fuzzy membership values are determined using a Fuzzy C-Mean (FCM) clustering algorithm. SOM network then learns the inverse kinematics by on-line by associating a local linear map for each cluster. A novel learning algorithm has been proposed to make the robot manipulator to reach a target position. Any arbitrary level of accuracy can be achieved with a number of fine movements of the manipulator tip. These fine movements depend on the error between the target position and the current manipulator position. In particular, the fuzzy model is found to be better as compared to Kohonen self-organizing map (KSOM) based learning scheme proposed for visual motor control. Like existing KSOM learning schemes, the proposed scheme leads to a unique inverse kinematic solution even for a redundant manipulator. The proposed algorithms have been successfully implemented in real-time on a 7 DOF PowerCube robot manipulator, and results are found to concur with the theoretical findings.


Redundant manipulator Inverse kinematics Visual motor control Fuzzy self-organizing map network 


  1. 1.
    Hutchinson S, Hager GD, Corke PI (1996) A tutorial on visual servo control. IEEE Trans Robot Autom 12(5): 651–670CrossRefGoogle Scholar
  2. 2.
    Kragic D et al. Survey on visual servoing for manipulation.
  3. 3.
    Kuperstein M (1987) Adaptive visual-motor coordination in multijoint robots using parallel architecture. Proc IEEE Int Conf Robot Autom 4: 1595–1602Google Scholar
  4. 4.
    Martinetz TM, Ritter HJ, Schulten KJ (1990) Three-dimensional neural net for learning visual motor coordination of a robot arm. IEEE Trans Neural Netw 1(1): 131–136CrossRefGoogle Scholar
  5. 5.
    Walter JA, Schulten KJ (1993) Implementation of self-organizing neural networks for visual-motor control of an industrial robot. IEEE Trans Neural Netw 4(1): 86–95CrossRefGoogle Scholar
  6. 6.
    Behera L, Kirubanandan N (1999) A hybrid neural control scheme for visual-motor coordination. IEEE Control Syst Mag 19(4): 34–41CrossRefGoogle Scholar
  7. 7.
    Tsai RY (1987) A versatile camera calibration technique for high-accuracy 3d machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J Robot Autom RA-3(4): 323–344CrossRefGoogle Scholar
  8. 8.
    Nakamura Y, Hanafusa H (1984) Task priority based redundancy control of robot manipulators. In: Proceedings of the 2nd internationl symposium on robotic research, Kyoto, JapanGoogle Scholar
  9. 9.
    Seraji H (1989) Configuration control of redundant manipulators: theory and implementation. IEEE Trans Robot Autom 5(4): 472–490CrossRefGoogle Scholar
  10. 10.
    Seraji H, Long MK, Lee TS (1993) Motion control of 7-dof arms: the configuration control approach. IEEE Trans Robot Autom 9(2): 125–139CrossRefGoogle Scholar
  11. 11.
    Kohonen T (1990) The self-organizing map. In: Proceedings of the IEEE, vol 78, September, pp 1464–1480Google Scholar
  12. 12.
    Kumar N, Behera L (2004) Visual motor coordination using a quantum clustering based neural control scheme. Neural Process Lett 20: 11–22CrossRefGoogle Scholar
  13. 13.
    Martinetz T, Ritter H, Schulten K (1990) Learning of visuomotor-coordination of a robot arm with redundant degrees of freedom. In: Proceedings of the international conference on parallel processing in neural systems and computers (ICNC). Dusseldorf, Elsevier, Amsterdam, pp 431–434Google Scholar
  14. 14.
    Hesselroth T, Sarkar K, Smagt PP, Schulten K (1994) Neural network control of a pneumatic robot arm. IEEE Trans Syst Man Cybernet 24(1): 28–38CrossRefGoogle Scholar
  15. 15.
    Kumar S, Shukla A, Dutta A, Behera L (2007) A model-free redundancy resolution technique for visual motor coordination of a 6 dof robot manipulator. In: IEEE 22nd international symposium on intelligent control, 1–3 Oct 2007, pp 544–549Google Scholar
  16. 16.
    Walter J, Ritter H (1996) Rapid learning with parametrized self-organizing maps. Neurocomputing 12: 131–153MATHCrossRefGoogle Scholar
  17. 17.
    Walter J, Nolker C, Ritter H (2000) The PSOM algorithm and applications. In: Proceedings of the international ICSC symposium on neural computation, pp 758–764Google Scholar
  18. 18.
    Oh PY, Allen PK (2001) Visual servoing by partitioning degrees of freedom. IEEE Trans Robot Autom 17(1): 1–17CrossRefGoogle Scholar
  19. 19.
    Roberts RG, Maciejewski AA (1993) Repeatable generalized inverse control strategies for kinematically redundant manipulators. IEEE Trans Autom Control 38(5): 689–699MATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    English JD, Maciejewski AA (2000) On the implementation of velocity control for kinematically redundant manipulators. IEEE Trans SMC 30(3): 233–237Google Scholar
  21. 21.
    Giuseppe RD, Taurisano F, Distante C, Anglani A (1999) Visual servoing of a robotic manipulator based on fuzzy logic controller. In: Proceedings of the international conference on robotics and automation. IEEE, Detroit, Michigan, pp 1487–1494Google Scholar
  22. 22.
    Kim CS, Seo WH, Han SH, Khatib O (2001) Fuzzy logic control of a robot manipulator based on visual servoing. In: Proceedings of the international symposium on industrial electronics. IEEE, Pusan, South Korea, pp 1597–1602Google Scholar
  23. 23.
    Kragic D, Christensen HI (2001) Cue integration in visual servoing. IEEE Trans Robot Autom 17(1): 18–27CrossRefGoogle Scholar
  24. 24.
    Kumaresan S, Li H, min Li X (1995) Robot hand-eye coordination based on fuzzy logic. In: Fuzzy logic and intelligent systems. Springer, Netherlands, pp 245–269Google Scholar
  25. 25.
    Prochazka A (1996) The fuzzy logic of visuomotor control. Can J Physiol Pharmacol 74(4): 456–462CrossRefGoogle Scholar
  26. 26.
    Prochazka A, Gillard D (1997) Sensory control of locomotion. In: Proceedings of the American control conference. Albuquerque, New Mexico, pp 2846–2850Google Scholar
  27. 27.
    Dunn JC (1973) A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J Cybernet 3: 32–57MATHCrossRefMathSciNetGoogle Scholar
  28. 28.
    Bezdek JC (1981) Pattern recognition with fuzzy objective function algoritms. Plenum Press, New YorkGoogle Scholar
  29. 29.
    D’Souza A, Vijayakumar S, Schaal S (2001) Learning inverse kinematics. In: International conference on intelligent robots and systems. IEEE, Maui, Hawai, pp 298–303Google Scholar
  30. 30.
    Open source computer vision library,
  31. 31.
  32. 32.
    Craig JJ (1989) Introduction to robotics. Pearson Education Inc., New JerseyMATHGoogle Scholar
  33. 33.
    Kiviluoto K (1996) Topology preservation in self-organizing maps. In: Proceedings of IEEE international conference on neural networks, vol 1, pp 294–299Google Scholar
  34. 34.
    Tevatia G, Schaal S (2000) Inverse kinematics for humanoid robots. In: IEEE international conference on robotics and automation, vol 1, 24–28 April 2000, pp 294–299Google Scholar
  35. 35.
    Kumar S, Behera L (2008) Visual motor control of a 7dof robot manipulator using function decomposition and sub-clustering in configuration space. Neural Process Lett 28(1): 17–33CrossRefGoogle Scholar
  36. 36.

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Indian Institute of TechnologyGuwahatiIndia
  2. 2.University of UlsterUlsterUK
  3. 3.Indian Institute of TechnologyKanpurIndia

Personalised recommendations