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An Adaptive Neuro-fuzzy Inference System for Robot Handling Fabrics with Curved Edges towards Sewing

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Abstract

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.

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References

  1. Torgerson, E., Paul, F.-W.: Vision-guided robotic fabric manipulation for apparel manufacturing. IEEE Control Syst. Mag. 8(1), 14–20 (1988). doi:10.1109/37.463

    Article  Google Scholar 

  2. Kudo, M., Nasu, Y., Mitobe, K., Borovac, B.: Multi-arm robot control system for manipulation of flexible materials in sewing operation. Mechatronics 10(8), 371–402 (2000). doi:10.1016/S0957-4158(99)00047-1

    Article  Google Scholar 

  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 

  4. Zacharia, P., Mariolis, I., Aspragathos, N., Dermatas, E.: Robot handling fabrics with curved edges based on visual servoing and polygonal approximation. Proc. Inst. Mech. Eng., B J. Eng. Manuf. 222(10), 1263–1274 (2008). doi:10.1243/09544054JEM999 (special issue)

    Article  Google Scholar 

  5. Sanchez, E., Shibata, T., Zadeh, L.A.: Genetic Algorithms and Fuzzy Logic Systems. World Scientific, River Edge (1997)

    Google Scholar 

  6. Lee, C.C.: Fuzzy logic in control systems: fuzzy logic controller. IEEE Trans. Syst. Man Cybern. 20(2), 404–434 (1990). doi:10.1109/21.52551

    Article  MATH  Google Scholar 

  7. Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets Syst. 28(1), 15–33 (1988). doi:10.1016/0165-0114(88)90113-3

    Article  MATH  MathSciNet  Google Scholar 

  8. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985)

    MATH  Google Scholar 

  9. Mannle, M.: FTSM—Fast Takagi-Sugeno Fuzzy Modeling. Institute for Computer Design and Fault Tolerance, University of Karlsruhe, Karlsruhe (2001)

  10. Delgado, M.R., Von Zuben, F., Gomide, F.: Hierarchical genetic fuzzy systems. Inf. Sci. 136(1), 29–52 (2001). doi:10.1016/S0020-0255(01)00140-2

    Article  MATH  Google Scholar 

  11. Jang, J.-S.R.: ANFIS: Adaptive network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993). doi:10.1109/21.256541

    Article  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)

  14. Marichal, G.N., Acosta, L., Moreno, L., Méndez, J.A., Rodrigo, J.J., Sigut, M.: Obstacle avoidance for a mobile robot: a neuro-fuzzy approach. Fuzzy Sets Syst. 124(2), 171–179 (2001). doi:10.1016/S0165-0114(00)00095-6

    Article  MATH  Google Scholar 

  15. Rusu, P., Petriu, E.M., Whalen, T.E., Cornell, A., Spoelder H.J.W.: Behaviour-based neuro fuzzy controller for mobile robot navigation. IEEE Trans. Instrum. Meas. 52(4), 1335–1340 (2003). doi:10.1109/TIM.2003.816846

    Article  Google Scholar 

  16. Hui, N.B., Mahendar, V., Pratihar, D.K.: Time-optimal, collision-free navigation of a car-like mobile robot using neuro-fuzzy approaches. Fuzzy Sets Syst. 157(16), 2171–2204 (2006). doi:10.1016/j.fss.2006.04.004

    Article  MATH  MathSciNet  Google Scholar 

  17. Lo, S.-P.: An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. J. Mater. Process. Technol. 142(3), 665–675 (2003). doi:10.1016/S0924-0136(03)00687-3

    Article  Google Scholar 

  18. Nariman-Zadeh, N., Darvizeh, A., Dadfarmai, M.H.: Design of ANFIS networks using hybrid genetic and SVD methods for the modelling of explosive cutting process. J. Mater. Process. Technol. 155–156, 1415–1421 (2004). doi:10.1016/j.jmatprotec.2004.04.228

    Article  Google Scholar 

  19. Çağatay Bayindir, K., Urğas Cuma, M., Tümay, M.: Hierarchical neuro-fuzzy current control for a shunt active power filter. Neural Comput. Appl. 15(3–4), 223–238 (2006). doi:10.1007/s00521-005-0024-8

    Google Scholar 

  20. Torres, S.P., Peralta, W.H., Castro, C.A.: Power system loading margin estimation using a neuro-fuzzy approach. IEEE Trans. Power Syst. 22(4), 1955–1964 (2007). doi:10.1109/TPWRS.2007.907380

    Article  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 2008

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  1. Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece

    Paraskevi Th. Zacharia

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  1. Paraskevi Th. Zacharia
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Correspondence to Paraskevi Th. Zacharia.

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Zacharia, P.T. An Adaptive Neuro-fuzzy Inference System for Robot Handling Fabrics with Curved Edges towards Sewing. J Intell Robot Syst 58, 193–209 (2010). https://doi.org/10.1007/s10846-009-9362-6

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  • DOI: https://doi.org/10.1007/s10846-009-9362-6

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