An Adaptive Neuro-fuzzy Inference System for Robot Handling Fabrics with Curved Edges towards Sewing
- 145 Downloads
This paper presents the design of a neuro-fuzzy visual servoing controller for robot guiding fabrics with curved edges towards sewing. Fabrics comprising real cloths consist of curved edges of arbitrary curvatures that can not be standardized. To overcome this difficulty, the idea is to train the robot sewing system and to apply this methodology in a real-time operation environment. The proposed approach for robot sewing is based on visual servoing and a learning technique that combines neural networks and fuzzy logic. A novel genetic-oriented clustering method is used to construct the initial FIS models and then, adaptive neuro-fuzzy inference systems allow tuning them so that it is possible to obtain better estimates. Extensive experiments were carried out in order to build data sets using fabrics with curved edges of various curvatures. The proposed model is validated using fabrics that had not been included in the training process and the results demonstrate that the proposed approach is efficient and effective for robot guiding fabrics with arbitrary curved edges towards sewing.
KeywordsAdaptive neuro-fuzzy inference systems (ANFIS) Fuzzy logic Genetic-based clustering Robot handling Flexible materials
Unable to display preview. Download preview PDF.
- 3.Gershon, D., Porat, I.: Vision servo control of a robotic sewing system. Proc. IEEE Int. Conf. Robotics Automat. 3, 1830–1835 (1988)Google Scholar
- 5.Sanchez, E., Shibata, T., Zadeh, L.A.: Genetic Algorithms and Fuzzy Logic Systems. World Scientific, River Edge (1997)Google Scholar
- 9.Mannle, M.: FTSM—Fast Takagi-Sugeno Fuzzy Modeling. Institute for Computer Design and Fault Tolerance, University of Karlsruhe, Karlsruhe (2001)Google Scholar
- 12.Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computation Approach to Learning and Machine Intelligence. Prentice Hall, Upper Saddle River (1997)Google Scholar
- 13.Rizzi, A., Mascioli F.M.F., Martinelli G.: Automatic training of ANFIS networks. In: Proceeding of IEEE International Fuzzy Systems Conference, Korea (1999)Google Scholar
- 21.Zacharia, P.T., Aspragathos, N.A.: Genetically oriented clustering using variable length chromosomes. In: I*PROMS NoE Virtual International Conference on Intelligent Production Machines and Systems, 1–14 July 2008Google Scholar