Abstract
Advanced robotic systems require an end effector capable of achieving considerable gripping dexterity in unstructured environments. A dexterous end effector has to be able of dynamic adaptation to novel and unforeseen situation. Thus, it is vital that gripper controller is able to learn from its perception and experience of the environment. An attractive approach to solve this problem is intelligent control, which is a collection of complementary ’soft computing’ techniques within a framework of machine learning. Several attempts have been made to combine methodologies to provide a better framework for intelligent control, of which the most successful has probably been that of neurofuzzy modelling. Here, a neurofuzzy controller is trained using the actor-critic method. Further, an expert system is attached to the neurofuzzy system in order to provide the reward signal and failure signal. Results show that the proposed framework permits a transparent-robust control of a robotic end effector.
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References
Brown, M., Harris, C.J.: Neurofuzzy Adaptive Modelling and Control. Prentice Hall International, New York (1994)
Harris, C.J., Hong, X., Gan, Q.: Adaptive Modelling. Estimation and Fusion from Data: A Neurofuzzy Approach. Springer, Heidelberg (2002)
Berenji, H., Khedkar, P.: Learning and tuning fuzzy logic controllers through reinforcements. IEEE Transactions on Neural Networks 3(5), 724–740 (1992)
De Ridder, D.: Shared Weights Neural Networks in Image Analysis. Master’s thesis, Delft University of Technology, Delft, The Netherlands (1996)
Singh, S., Norving, P., Cohn, D.: A tutorial survey of reinforcement learning. Sadhana 19(6), 851–889 (1994)
Watkins, C.J.C.H.: Automatic learning of efficient behaviour. In: Proceedings of First IEE International Conference on Artificial Neural Networks, London, UK, pp. 395–398 (1989)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2000)
Nehmzow, U.: Mobile Robotics: A Practical Introduction. Springer, London (2000)
Haykin, S.: Neural Networks A Comprehensive Foundation. Prentice-Hall, Upper Saddle River (1999)
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© 2005 Springer-Verlag Berlin Heidelberg
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Domínguez-López, J.A. (2005). Adaptive Neuro-Fuzzy-Expert Controller of a Robotic Gripper. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_105
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DOI: https://doi.org/10.1007/11579427_105
Publisher Name: Springer, Berlin, Heidelberg
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