Abstract
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.
This work was jointly supported by the National Nature Science Foundation for Youth Fund (Grant No: 60405011), the China Postdoctoral Foundation for China Postdoctoral Science Fund (Grant No: 20040350078) and the National 863 Project of China (Grant No: 2001AA421200-1).
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Xu, H., Jia, P., Zhang, X. (2005). A Neural Network Based Method for Shape Measurement in Steel Plate Forming Robot. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_46
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DOI: https://doi.org/10.1007/11427469_46
Publisher Name: Springer, Berlin, Heidelberg
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