The purpose of this study was to solve the problem of pose measurement of various parts for a precision assembly system.
A novel alignment method which can achieve high-precision pose measurement of microparts based on monocular microvision system was developed. To obtain the precise pose of parts, an area-based contour point set extraction algorithm and a point set registration algorithm were developed. First, the part positioning problem was transformed into a probability-based two-dimensional point set rigid registration problem. Then, a Gaussian mixture model was fitted to the template point set, and the contour point set is represented by hierarchical data. The maximum likelihood estimate and expectation-maximization algorithm were used to estimate the transformation parameters of the two point sets.
The method has been validated for accelerometer assembly on a customized assembly platform through experiments. The results reveal that the proposed method can complete letter-pedestal assembly and the swing piece-basal part assembly with a minimum gap of 10 µm. In addition, the experiments reveal that the proposed method has better robustness to noise and disturbance.
Owing to its good accuracy and robustness for the pose measurement of complex parts, this method can be easily deployed to assembly system.
This work was financially supported by the National Key Technology Research and Development Program of China, under Grant SQ2019YFB130212.
Guo, P., Zhang, Z., Shi, L. and Liu, Y. (2021), "A contour-guided pose alignment method based on Gaussian mixture model for precision assembly", Assembly Automation, Vol. 41 No. 3, pp. 401-411. https://doi.org/10.1108/AA-08-2020-0103
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