Abstract
In this paper, we propose an unsupervised neural network for prediction and planning of complex robot trajectories. A general approach is developed which allows Kohonen’s Self-Organizing Map (SOM) to approximate nonlinear input-output dynamical mappings for trajectory reproduction purposes. Tests are performed on a real PUMA 560 robot aiming to assess the computational characteristics of the method as well as its robustness to noise and parametric changes. The results show that the current approach outperforms previous attempts to predictive modeling of robot trajectories through unsupervised neural networks.
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References
Althöfer, K., Bugmann, G.: Planning and learning goal-directed sequences of robot arm movements. In: Fogelman-Soulié, F., Gallinari, P. (eds.) Proc. Int. Conf. on Artificial Neural Networks (ICANN), vol. I, pp. 449–454 (1995)
Araújo, A.F.R., Barreto, G.A.: A self-organizing context-based approach to tracking of multiple robot trajectories. Applied Intelligence 17(1), 99–116 (2002)
Barreto, G.A., Araújo, A.F.R.: Time in self-organizing maps: An overview of models. International Journal of ComputerResearch 10(2), 139–179 (2001)
Barreto, G.A., Araújo, A.F.R.: Nonlinear modelling of dynamic systems with the self-organizing map. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 975–980. Springer, Heidelberg (2002)
Barreto, G.A., Dücker, C., Ritter, H.: A distributed robotic control system based on a temporal self-organizing network. IEEE Transactions on Systems, Man, and Cybernetics-Part C 32(4), 347–357 (2002)
Bugmann, G., Koay, K.L., Barlow, N., Phillips, M., Rodney, D.: Stable encoding of robot trajectories using normalised radial basis functions: Application to an autonomous wheelchair. In: Proc. 29th Int. Symposium on Robotics (ISR), Birmingham, UK, pp. 232–235 (1998)
Denham, M.J., McCabe, S.L.: Robot control using temporal sequence learning. In: Proc. World Congress on Neural Networks (WCNN), Washington DC, vol. II, pp. 346–349 (1995)
Fu, K., Gonzalez, R., Lee, C.: Robotics: Control, Sensing, Vision, and Intelligence. McGraw-Hill, New York (1987)
Hunt, K.J., Sbarbaro, D., Zbikowski, R., Gawthrop, P.J.: Neural networks for control systems – A survey. Automatica 28(6), 1083–1112 (1992)
Kohonen, T.: Self-Organizing Maps, 2nd extended edn. Springer, Berlin (1997)
Raibert, M.H., Horn, B.K.P.: Manipulator control using the configuration space method. The Industrial Robot 5, 69–73 (1978)
Ritter, H., Martinetz, T., Schulten, K.: Neural Computation and Self- Organizing Maps: An Introduction. Addison-Wesley, Reading (1992)
Walter, J., Ritter, H.: Rapid learning with parametrized self-organizing maps. Neurocomputing 12, 131–153 (1996)
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Barreto, G.A., Araújo, A.F.R. (2004). Predictive Modeling and Planning of Robot Trajectories Using the Self-Organizing Map. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_118
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DOI: https://doi.org/10.1007/978-3-540-24677-0_118
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