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    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Lorenzo, Gonzalo Pomares, Jorge Lledó, Asuncion and Jara, Carlos A. 2015. Control of Redundant Joint Structures Using Image Information During the Tracking of Non-Smooth Trajectories. Journal of Intelligent & Robotic Systems, Vol. 78, Issue. 1, p. 33.


    Pomares, Jorge Perea, Ivan and Torres, Fernando 2014. Dynamic Visual Servoing With Chaos Control for Redundant Robots. IEEE/ASME Transactions on Mechatronics, Vol. 19, Issue. 2, p. 423.


    Moubarak, Paul and Ben-Tzvi, Pinhas 2013. A globally converging algorithm for adaptive manipulation and trajectory following for mobile robots with serial redundant arms. Robotica, Vol. 31, Issue. 08, p. 1299.


    Pomares, J. Perea, I. Jara, C. A. and Torres, F. 2013. 2013 IEEE International Conference on Mechatronics (ICM). p. 75.

    Comparetti, Mirko Daniele De Momi, Elena Vaccarella, Alberto Riechmann, Matthias and Ferrigno, Giancarlo 2011. 2011 IEEE International Conference on Robotics and Automation. p. 661.

    Sigaud, Olivier Salaün, Camille and Padois, Vincent 2011. On-line regression algorithms for learning mechanical models of robots: A survey. Robotics and Autonomous Systems, Vol. 59, Issue. 12, p. 1115.


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

  • Swagat Kumar (a1), Premkumar P. (a1), Ashish Dutta (a1) and Laxmidhar Behera (a1) (a2)
  • DOI: http://dx.doi.org/10.1017/S026357470999049X
  • Published online: 21 September 2009
Abstract
SUMMARY

This paper deals with the design and implementation of a visual kinematic control scheme for a redundant manipulator. The inverse kinematic map for a redundant manipulator is a one-to-many relation problem; i.e. for each Cartesian position, multiple joint angle vectors are associated. When this inverse kinematic relation is learnt using existing learning schemes, a single inverse kinematic solution is achieved, although the manipulator is redundant. Thus a new redundancy preserving network based on the self-organizing map (SOM) has been proposed to learn the one-to-many relation using sub-clustering in joint angle space. The SOM network resolves redundancy using three criteria, namely lazy arm movement, minimum angle norm and minimum condition number of image Jacobian matrix. The proposed scheme is able to guide the manipulator end-effector towards the desired target within 1-mm positioning accuracy without exceeding physical joint angle limits. A new concept of neighbourhood has been introduced to enable the manipulator to follow any continuous trajectory. The proposed scheme has been implemented on a seven-degree-of-freedom (7DOF) PowerCube robot manipulator successfully with visual position feedback only. The positioning accuracy of the redundant manipulator using the proposed scheme outperforms existing SOM-based algorithms.

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*Corresponding author. E-mail: lbehera@iitk.ac.in
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Robotica
  • ISSN: 0263-5747
  • EISSN: 1469-8668
  • URL: /core/journals/robotica
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