Shape Recognition and Measurement of Position and Orientation of Different Parts Produced within a Manufacturing Process

Abstract:

In a manufacturing process it may be necessary to distinguish the parts of various types produced by different machine-tools and placed on a conveyor. Using an artificial vision system represents a possible solution. Images of the parts can be periodically taken using a video camera. It is considered that all parts are positioned with one of the faces which defines its type on the upper side. Thus, parts’ recognition can be solved by recognizing the shapes from the images. In addition, information about position and orientation of parts can be determined using the captured images. All these information are listed in a text file which can be used by a decision algorithm. This algorithm can choose which parts are useful to assemble different objects. An industrial robot can be commanded using a program written in RobotStudio environment’s programming language (RAPID) to pick the needed parts and place them in a storage area.This paper describes a recognition and measurement of position and orientation method of the different parts produced. In addition, details about implementing this method in MATLAB environment using Image Processing Toolbox and geometrical relations are provided gradually.

• Periodical: Applied Mechanics and Materials (Volumes 809-810)
• Pages: 1237-1242
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.809-810.1237
• Online since: November 2015
• Authors: Viorel Cohal*, Alexandru Cohal
• Keywords: MATLAB, Orientation, Position, Recognition, Shape

References

[1] V. Cohal, Recognition of geometric shapes in images, Advanced Materials Research. 1036 (2014) 755 – 759.

DOI: 10.4028/www.scientific.net/amr.1036.755

[2] V. Cohal, Some aspects concerning finding geometric forms automatically and ordering them using RobotStudio simulation, Advanced Materials Research. 1036 (2014) 760 – 763.

DOI: 10.4028/www.scientific.net/amr.1036.760

[3] M. Hagedoorn, Pattern matching using similarity measures, PhD thesis, Universiteit Utrecht, (2000).

[4] A. Ashbrook, N.A. Thacker, Algorithms For 2-Dimensional Object Recognition, Tina Memo No. 1996-003.

[5] M.F. Zakaria, H. S. Choon, S.A. Suandi, Object Shape Recognition in Image for Machine Vision Application, Journal of Computer Theory and Engineering. 4, 1 (2012) 76 – 80.

DOI: 10.7763/ijcte.2012.v4.428

[6] S. Rege, R. Memane, M. Phatak, P. Agarwal, 2D Geometric shape and color recognition using digital image processing, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2, Issue 6 (2013).

[7] ** http: /www. mathworks. com/help/images/index. html, accessed: 10. 02. (2015).