Abstract
Visual–Motor Coordination is a problem considered analogous to the hand-eye coordination in biological systems. In this work we propose a novel approach to this problem using Quantum Clustering and an extended Kohonen's Self-Organizing Feature Map (K-SOFM). This facilities the use of the method in varying workspaces by considering the joint angles of the robot arm. Unlike previous work, where a fixed topology for the input space is considered, the proposed approach determines a topology as the workspace varies. Quantum Clustering is a method which constructs a scale-space probability function and uses the Schroedinger equation and its lowest eigenstate to obtain a potential whose minimum gives the cluster centers. It transforms the input space into a Hilbert space, where it searches for its minimum. The motivation of this work is to identify the implicit relationship existing between the end-effector positions and the joint angles through Quantum Clustering and Neural Network methods to fine-tune the system to correctly identify the mapping.
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References
Behera, L. and Kirubanandan, N.: A hybrid neural control scheme for visual-motor coordination. IEEE Control Systems Magazine, 19(1999), 34–41.
Duda, R. Hart, P. and Stork, D.: Pattern Classification. John Wiley & Sons, 2001.
Horn, D.: Clustering via hilbert space. Physica A, 302(2001), 70–79.
Kuperstein, M.: Adaptive visual-motor coordination in multijoint robots using parallel architectures. In Proceedings of the IEEE International Automation and Robotics, pp. 1595–1602, 1990.
Kuperstein, M.: Neural model of adaptive hand-eye coordination for single postures. Science, 239(1998), 1308–1311.
Martinetz, T. Ritter, H. and Schulten. K.: Three dimensional neural network for learning visuomotor coordination of a robot arm. IEEE Transactions on Neural Networks, 1(1990), 131–136.
Ripley, B.: Pattern Recognition and Neural Networks. Cambridge University Press, 1996.
Ritter, H. Martinetz, T. and Schulten, K.: Topology conserving maps for learning visuomotor coordination. Neural Networks, 2(1998), 159–168.
Roberts. S.: Non-parametric unsupervised cluster analysis. Pattern Recognition, 30(1997), 261–272.
Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, 1998.
Walter, J. and Schulten, K.: Implementation of self-organizing neural networks for visuomotor control of an industrial robot. IEEE Transactions on Neural Networks, 4(1993), 86–95.
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Kumar, N., Behera, L. Visual–Motor Coordination Using a Quantum Clustering Based Neural Control Scheme. Neural Processing Letters 20, 11–22 (2004). https://doi.org/10.1023/B:NEPL.0000039429.89321.07
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DOI: https://doi.org/10.1023/B:NEPL.0000039429.89321.07