A Neural Network Based Method for Shape Measurement in Steel Plate Forming Robot
Shape measurement is one of the critical problems in manufacturing robot systems. The point coordinates that we get change distinctly, because different objects to be processed own various shape forms. So it is difficult for traditional methods to get original accurate shape information. It always affects the processing results of manufacturing robot systems. According to the shipbuilding requirements, this paper proposes a dynamic and intelligent shape measurement method, which is based on the fuzzy reasoning (FR) and neural network (NN) method. FR is used to judge the relation of measured points. As the input of the NN, the fitted coordinate and the possibility of the rim point can be got. It has been demonstrated effective in Dalian Shipbuilding manufacturing robot system.
KeywordsSteel Plate Robot System Space Relation Fuzzy Reasoning Shape Measurement
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- 1.Jinyu, Z.: Design of the Controller for Sample Machine of SPFCH Intelligent Robot. Master Thesis of Tsinghua University, Tsinghua University Beijing (2004)Google Scholar
- 2.Simpson, P.K.: Foundation of Neural Networks, New York. IEEE Technology UPDATE SERIES Neural Networks Theory, Technology, and Applications, pp. 1–22 (1996)Google Scholar
- 3.Armitage, A.F.: Neural Networks in Measurement and Control. Measurement and Control 25, 205–215 (1995)Google Scholar
- 4.Rumelhart, D., Mecelelland, J.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Bredford Books, MIT Press, Cambridge, Massachusetts (1986)Google Scholar
- 6.Eren, H., Fung, C.C., Wang, K.W.: An Application of Artificial Neural Network for Prediction of Densities and Particle Size Distributions in Mineral Processing Industry. In: Proceedings of IEEE Instrumentation and Measurement Technical Conference, vol. 1, pp. 1118–1121 (1997)Google Scholar
- 9.McBratney, A.B., Odeh, I.O.A.: Application of Fuzzy Sets in Soil Science: Fuzzy Logic. Fuzzy Measurements and Fuzzy Decisions. Geoderma 77, 85–113 (1997)Google Scholar
- 11.Hagan, M.T., Demuth, H.B.: Mark Beale, Neural Network Design. CITIC Publishing House, Beijing (2002)Google Scholar
- 12.Horvath, G.: Neural Networks from the Perspective of Measurement Systems. In: Proceedings of the 20th IEEE Instrumentation and Measurement Technology Conference, vol. 2, pp. 1102–1107 (2003)Google Scholar
- 13.Sun, Z., Zaixing, Z.: Intelligent Control. Tsinghua University Press, Beijing (1996)Google Scholar