Implementation of self-organizing neural networks for visuo-motorcontrol of an industrial robot
Walter JA, Schulten K (1993)
IEEE Transactions on Neural Networks 4(1): 86-96.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Walter, Jörg A.;
Schulten, Klaus
Einrichtung
Abstract / Bemerkung
The implementation of two neural network algorithms for visuo-motor control of an industrial robot (Puma 562) is reported. The first algorithm uses a vector quantization technique, the "neural-gas" network, together with an error correction scheme based on a Widrow-Hoff-type learning rule. The second algorithm employs an extended self-organizing feature map algorithm. Based on visual information provided by two cameras, the robot learns to position its end effector without an external teacher. Within only 3000 training steps, the robot-camera system is capable of reducing the positioning error of the robot's end effector to approximately 0.1% of the linear dimension of the work space. By employing adaptive feedback the robot succeeds in compensating not only slow calibration drifts, but also sudden changes in its geometry. Hardware aspects of the robot-camera system are discussed
Erscheinungsjahr
1993
Zeitschriftentitel
IEEE Transactions on Neural Networks
Band
4
Ausgabe
1
Seite(n)
86-96
ISSN
1045-9227
Page URI
https://pub.uni-bielefeld.de/record/1782934
Zitieren
Walter JA, Schulten K. Implementation of self-organizing neural networks for visuo-motorcontrol of an industrial robot. IEEE Transactions on Neural Networks. 1993;4(1):86-96.
Walter, J. A., & Schulten, K. (1993). Implementation of self-organizing neural networks for visuo-motorcontrol of an industrial robot. IEEE Transactions on Neural Networks, 4(1), 86-96. https://doi.org/10.1109/72.182698
Walter, Jörg A., and Schulten, Klaus. 1993. “Implementation of self-organizing neural networks for visuo-motorcontrol of an industrial robot”. IEEE Transactions on Neural Networks 4 (1): 86-96.
Walter, J. A., and Schulten, K. (1993). Implementation of self-organizing neural networks for visuo-motorcontrol of an industrial robot. IEEE Transactions on Neural Networks 4, 86-96.
Walter, J.A., & Schulten, K., 1993. Implementation of self-organizing neural networks for visuo-motorcontrol of an industrial robot. IEEE Transactions on Neural Networks, 4(1), p 86-96.
J.A. Walter and K. Schulten, “Implementation of self-organizing neural networks for visuo-motorcontrol of an industrial robot”, IEEE Transactions on Neural Networks, vol. 4, 1993, pp. 86-96.
Walter, J.A., Schulten, K.: Implementation of self-organizing neural networks for visuo-motorcontrol of an industrial robot. IEEE Transactions on Neural Networks. 4, 86-96 (1993).
Walter, Jörg A., and Schulten, Klaus. “Implementation of self-organizing neural networks for visuo-motorcontrol of an industrial robot”. IEEE Transactions on Neural Networks 4.1 (1993): 86-96.
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2019-09-06T08:48:50Z
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9 Zitationen in Europe PMC
Daten bereitgestellt von Europe PubMed Central.
Learning inverse kinematics: reduced sampling through decomposition into virtual robots.
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Speeding up the learning of robot kinematics through function decomposition.
Ruiz de Angulo V, Torras C., IEEE Trans Neural Netw 16(6), 2005
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Araujo AR, Barreto GA., IEEE Trans Neural Netw 13(1), 2002
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de Angulo VR, Torras C., IEEE Trans Neural Netw 8(4), 1997
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de Angulo VR, Torras C., IEEE Trans Neural Netw 8(4), 1997
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Demers D, Kreutz-Delgado K., IEEE Trans Neural Netw 7(1), 1996
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