Abstract
Many tasks in robotics are difficult to tackle with explicit models, based on “first principles”. Consequently, neural networks with their inherent learning ability can offer feasible alternatives to more traditional approaches. Focusing on neural network approaches that have been derived from the Self-Organizing Map, we review several examples, how such networks can contribute to the solution of typical tasks in robotics, such as map building, object recognition and, in particular, the coordination of multi-joint movements. We argue, that a frequent common structure is a “continuous associative memory” for the flexible representation of continuous relations between degrees of freedom, and show how such representation can be obtained with a parametrized Self-organizing Map.
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Klein C.A., Huang C-H.: Review of Pseudoinverse Control for Use with Kinematically Redundant Manipulators. IEEE Trans. Sys. Man and Cybern. SMC-13, (1983) 245–250.
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C, Cambridge University Press (1990).
Hunt K.J., Sbarbaro D., Zbikowski R., Gawthrop P.J.: Neural Networks for Control Systems: A Survey. Automatica 28 (1992) 1083ff.
Ritter, H., Martinetz, T., Schulten, K.: Neural Computation and Self-organizing Maps, Addison-Wesley, Reading, MA. (1992)
Kurz A.: Building Maps for Path Planning and Navigation Using Learning Classification of External Sensor Data. In: Artificial Neural Networks 2 (I. Aleksander and J. Taylor eds.), (1992) 587–590, Elsevier Science Publishers.
Vleugels J.M., Kok J.N., Overmars M.H.: A self-organizing neural network for robot motion planning. In: ICANN 93 Art. Neural Networks Conf. Proc. (S. Gielen and B. Kappen eds.), (1993) 281–284, Springer Berlin Heidelberg.
Ritter, H. Parametrized Self-Organizing Maps. ICANN 93 Proceedings (S.Gielen and B.Kappen eds.), (1993) 268–273, Springer Berlin Heidelberg.
Kröse B.J.A., Eecen M.: Self-learning maps for path planning in sensor space. In: ICANN 94 Art. Neural Networks Conf. Proc. (M. Marinaro and P.G. Morasso eds.), (1994) 1303–1306, Springer Berlin Heidelberg
Wood J.: Invariant Pattern Recognition: A Review. Pattern Recognition 29, (1996) 1–17
Walter, J., Ritter, H., Investment Learning with Hierarchical PSOM. In: Advances in Neural Information Processing Systems 8 (D. Touretzky, M. Mozer and M. Hasselmo eds.), (1996) 570–576, MIT Press.
Walter J.: Rapid Learning in Robotics. Cuvillier Verlag Göttingen 1996.
Walter, J., Ritter, H., Rapid Learning with Parametrized Self-organizing Maps. Neurocomputing 12, (1996) 131–153
Heidemann G., Ritter H.: A Neural 3-D Object Recognition Architecture Using Optimized Gabor Filters. Proc. 13th Int. Conf. Patt. Recog., Vol. IV, (1996) 70–74, IEEE Computer Society Press.
Littmann E., Drees A., Ritter H.: Neural Recognition of Human Pointing Gestures in Real Images. Neural Processing Letters (1996) 61–71, Kluwer Academic Publishers.
Heidemann G., Ritter H.: A neural recognition architecture for composed objects. DAGM Symposium Mustererkennung (B. Jähne, P. Gei\ler, H. Hau\ecker, F. Hering eds.), (1996) 475–482, Springer Verlag Berlin Heidelberg.
Heidemann, G., Kummert F., Ritter H., Sagerer G.: A Hybrid Object Recognition Architecture, ICANN 96, Springer Lecture Notes in Computer Science 1112, (1996) 305–310. Springer Berlin Heidelberg.
Heidemann, G., Nattkemper T., Menkhaus G., Ritter H.: Blicksteuerung durch präattentive Fokussierungspunkte. Proceedings in Artificial Intelligence (B. Mertsching ed.) (1996) 109–116 Springer Verlag (in German).
Kohonen, T.: Self-Organizing Maps, Springer Series in Information Sciences, Springer Berlin Heidelberg 1997.
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Ritter, H. (1997). Self-organizing maps for robot control. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020232
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DOI: https://doi.org/10.1007/BFb0020232
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