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Visual Servoing of a Redundant Manipulator with Jacobian Matrix Estimation using Self-organizing Map

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Patchaikani, Prem Kumar and Behera, Laxmidhar (2010) Visual Servoing of a Redundant Manipulator with Jacobian Matrix Estimation using Self-organizing Map. Robotics and Autonomous Systems, 58 (8). pp. 978-990. [Journal article]

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URL: http://www.sciencedirect.com/science/article/pii/S0921889010000825

DOI: 10.1016/j.robot.2010.04.001

Abstract

Vision based redundant manipulator control with a neural network based learning strategy is discussed in this paper. The manipulator is visually controlled with stereo vision in an eye-to-hand configuration. A novel Kohonen's self-organizing map (KSOM) based visual servoing scheme has been proposed for a redundant manipulator with 7 degrees of freedom (DOF). The inverse kinematic relationship of the manipulator is learned using a Kohonen's self-organizing map. This learned map is shown to be an approximate estimate of the inverse Jacobian, which can then be used in conjunction with the proportional controller to achieve closed loop servoing in real-time. It is shown through Lyapunov stability analysis that the proposed learning based servoing scheme ensures global stability. A generalized weightupdate law is proposed for KSOM based inverse kinematic control, to resolve the redundancy during the learning phase. Unlike the existing visual servoing schemes, the proposed KSOM based scheme eliminates the computation of the pseudo-inverse of the Jacobian matrix in real-time. This makes the proposed algorithm computationally more efficient. The proposed scheme has been implemented on a 7 DOF PowerCubeTM robot manipulator with visual feedback from two cameras.

Item Type:Journal article
Keywords:Redundant manipulator, Visual servoing, Inverse Jacobian, Self-organizing map, Kinematic control, Redundancy resolution
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:21044
Deposited By:Dr Laxmidhar Behera
Deposited On:14 Feb 2012 16:14
Last Modified:14 Feb 2012 16:14

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