Visual motor control of a 7DOF redundant manipulator using redundancy preserving learning network

Swagat Kumar, Premkumar Patcaikani, Ashish Dutta, Laxmidhar Behera

    Research output: Contribution to journalArticlepeer-review

    24 Citations (Scopus)


    This paper deals with the design and implementation of avisual kinematic control scheme for a redundant manipulator.The inverse kinematic map for a redundant manipulatoris a one-to-many relation problem; i.e. for each Cartesianposition, multiple joint angle vectors are associated. Whenthis inverse kinematic relation is learnt using existinglearning schemes, a single inverse kinematic solution isachieved, although the manipulator is redundant. Thus anew redundancy preserving network based on the selforganizingmap (SOM) has been proposed to learn theone-to-many relation using sub-clustering in joint anglespace. The SOM network resolves redundancy using threecriteria, namely lazy arm movement, minimum angle normand minimum condition number of image Jacobian matrix.The proposed scheme is able to guide the manipulator endeffectortowards the desired target within 1-mm positioningaccuracy without exceeding physical joint angle limits. Anew concept of neighbourhood has been introduced toenable the manipulator to follow any continuous trajectory.The proposed scheme has been implemented on a sevendegree-of-freedom (7DOF) PowerCube robot manipulatorsuccessfully with visual position feedback only. Thepositioning accuracy of the redundant manipulator usingthe proposed scheme outperforms existing SOM-basedalgorithms
    Original languageEnglish
    Pages (from-to)795-810
    Publication statusPublished - 2009

    Bibliographical note

    Reference text: 1. V. R. Angulo and C. Torras, “Speeding up the learning of robot
    kinematics through function decomposition,” IEEE Trans.
    Neural Networks 16(6), 1504–1512 (Nov. 2005).
    2. G. A. Barreto, A. F. R. Araujo and H. J. Ritter, “Selforganizing
    feature maps for modeling and control of robotic
    manipulators,” J. Intell. Rob. Syst. 36, 407–450 (2003).
    3. L. Behera and N. Kirubanandan, “A hybrid neural control
    scheme for visual-motor coordination,” IEEE Control Syst.
    Mag. 19(4), 34–41 (1999).
    4. F. Chaumette, “Image moments: A general and useful set of
    features for visual servoing,” IEEE Trans. Rob. 20(4), 713–723
    (Aug. 2004).
    5. F. Chaumette and E. Marchand, “A redundancy-based iterative
    approach for avoiding joint limits: Application to visual
    servoing,” IEEE Trans. Rob. Automat. 17(5), 719–730 (Oct.
    6. J. T. Feddema, C. S. George Lee and O. W. Mitchell,
    “Weighted selection of image features for resolved rate visual
    feedback control,” IEEE Trans. Rob. Automat. 7(1), 31–47
    (Feb. 1991).
    7. M. Han, N. Okada and E. Kondo, “Coordination of an
    uncalibrated 3-d visuo-motor system based on multiple selforganizing
    maps,” JSME Int. J. Ser. C 49(1), 230–239
    8. S. Hutchinson, G. D. Hager and P. I. Corke, “A tutorial on
    visual servo control,” IEEE Trans. Rob. Automat. 12(5), 651–
    670 (Oct. 1996).
    9. P. Jiang, L. C. A. Bamforth, Z. Feng, J. E. F. Baruch and Y. Q.
    Chen, “Indirect iterative learning control for a discrete visual
    servo without a camera-robot model,” IEEE Trans. Syst. Man
    Cybernet. Part B: Cybernet. 37(4), 863–876 (Aug. 2007).
    10. T. Kohonen, Self Organization and Associative Memory
    (Springer-Verlag, Berlin, Germany, 1984).
    11. D. Kragic and H. I. Christensen, Survey on Visual Servoing
    for Manipulation Technical Report (Stockholm, Sweden:
    ComputationalVision and Active Perception Laboratory, KTH,
    12. N. Kumar and L. Behera, “Visual motor coordination using
    a quantum clustering based neural control scheme,” Neural
    Process. Lett. 20, 11–22 (2004).
    13. S. Kumar and L. Behera, “Implementation of a Neural
    Network Based Visual Motor Control Algorithm for a 7 dof
    Redundant Manipulator,” International Joint Conference on
    Neural Networks (IJCNN), Hong Kong, China (June 2008)
    pp. 1344–1351.
    14. S. Kumar, N. Patel and L. Behera, “Visual motor control of
    a 7 dof robot manipulator using function decomposition and
    sub-clustering in configuration space,” Neural Process. Lett.
    28(1), 17–33 (Aug. 2008).
    15. L. Li,W. A. Gruver, Q. Zhang and Z. Yang, “Kinematic control
    of redundant robots and the motion optimizability measure,”
    IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 31(1),
    155–160 (Feb. 2001).
    16. Y. Li and S. H. Leong, “Kinematics control of redundant
    manipulators using a CMAC neural network combined with
    a genetic algorithm,” Robotica 22, 611–621 (2004).
    17. T. Martinetz, H. Ritter and K. Schulten, “Learning of
    visuomotor-coordination of a robot armwith redundant degrees
    of freedom,” In Proceedings of the International Conference on
    Parallel Processing in Neural Systems and Computers (ICNC),
    (Elsevier, Dusseldorf and Amsterdam 1990) pp. 431–434.
    18. T. M. Martinetz, H. J. Ritter and K. J. Schulten, “Threedimensional
    neural net for learning visual motor coordination
    of a robot arm,” IEEE Trans. Neural Networks 1(1), 131–136
    (Mar. 1990).
    19. R. I.V. Mayorgaa and P. Sanongboone, “Inverse kinematics and
    geometrically bounded singularities prevention of redundant
    manipulators: An artificial neural network approach,” Rob.
    Auton. Syst. 53, 164–176 (2005).
    20. R. Sharma and S. Hutchinson, “Optimizing Hand/Eye
    Configuration for Visual-Servo Systems,” Proceedings of the
    International Conference on Robotics and Automation (ICRA),
    Nagoya, Japan (May 1995) pp. 172–177.
    21. M.W. Spong andM.Vidyasagar, Robot Dynamics and Control,
    New York, USA (John Wiley, 1989).
    22. G. Tevatia and S. Schaal, “Inverse Kinematics of Humanoid
    Robots.” Proceedings of the IEEE International Conference
    on Robotics and Automation, San Francisco, CA (Apr. 2000)
    pp. 294–299.
    23. R. Y. Tsai, “A versatile camera calibration technique for highaccuracy
    3d machine vision metrology using off-the-shelf tv
    cameras and lenses,” IEEE J. Rob. Automat. RA-3(4), 323–344
    (Aug. 1987).
    24. J. A. Walter and K. J. Schulten, “Implementation of selforganizing
    neural networks for visual-motor control of an
    industrial robot,” IEEE Trans. Neural Networks 4(1), 86–95
    (Jan. 1993).
    25. R. Wilson, “Tsai Camera Calibration Software,” available at rgw/TsaiCode.html.
    26. Y. Xia and J. Wang, “A dual neural network for kinematic
    control of redundant robot manipulators,” IEEE Trans. Syst.
    Man Cybernet. Part B: Cybernet. 31(1), 147–154 (Feb. 2001).
    27. H. Zha, T. Onitsuka and T. Nagata, “A self-organization learning
    algorithm for visuo-motor coordination in unstructured
    environment,” Artif. Life Rob. 1(3), 131–136 (Sep. 1997).
    28. X.-Z. Zheng and K. Ito, “Self-organized learning and its
    implementation of robot movements,” IEEE International
    Conference on SMC, “Computational Cybernetics and
    Simulation,” Orlando, FL (1997) pp. 281–286.


    • Visual motor control
    • Self-organizing map
    • Sub-clustering
    • Redundancy resolution
    • Inverse kinematics.


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