Optical servoing for industrial surface machining

The surface machining of cracks is a key issue to ensure the quality of steel rods and billets. The aim is to grind these defects out of the material. This paper presents a real-time optical servo-system, consisting of three image processing systems and an industrial robot, which fully automate this process. A high resolution color progressive scan camera, placed at a suitable position above the roller conveyor, observes the material and detects color markings indicating the presence of a crack. This camera system controls the roller conveyor transporting the material until a marked crack is detected. Diffuse light sources provide homogeneous lighting to ensure reliable detection of the markings. A demosaicing algorithm, RGB to HSL color modeling and thresholding with statistical morphology are used to identify the marked areas. On detecting a crack the material is automatically positioned within the working area of an industrial robot. A collineation is used to generate metric two-dimensional coordinates corresponding to the bounding rectangle of the detected error. At this point two plane-of-light scanners are used to acquire a cross section of the material to the left and the right of the robot's working area. From this, a three-dimensional model for the rod or billet surface is calculated and the two-dimensional coordinates of the color marking are projected onto this surface to generate a patch. The coordinates of this patch are sent to the 6R industrial robot, which then grinds out the defect. A new concept has been implemented which enables the calibration of the three image processing systems and the industrial robot so as to have one common coordinate system. Operational results have shown the full functionality of the system concept in the harsh environment of a steel production facility.

[1]  Paul O'Leary,et al.  Comparison of CMOS and CCD cameras for laser profiling , 2004, IS&T/SPIE Electronic Imaging.

[2]  Jie Wang,et al.  A Comparative Study of Skin-Color Models , 2005, ICIAR.

[3]  J. Graham.  Least-Square Fitting , 2009 .

[4]  Sebastiano Battiato,et al.  Natural scenes classification for color enhancement , 2005, IEEE Transactions on Consumer Electronics.

[5]  Wesley E. Snyder,et al.  Demosaicking methods for Bayer color arrays , 2002, J. Electronic Imaging.

[6]  J. Birgitta Martinkauppi,et al.  Behavior of skin color under varying illumination seen by different cameras at different color spaces , 2001, IS&T/SPIE Electronic Imaging.

[7]  Reinhard Koch,et al.  A Color Similarity Measure for Robust Shadow Removal in Real Time , 2003, VMV.

[8]  Rastislav Lukac,et al.  Normalized color-ratio modeling for CFA interpolation , 2004, IEEE Transactions on Consumer Electronics.

[9]  Francis Quek,et al.  Comparison of five color models in skin pixel classification , 1999, Proceedings International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems. In Conjunction with ICCV'99 (Cat. No.PR00378).

[10]  R.W. Schafer,et al.  Demosaicking: color filter array interpolation , 2005, IEEE Signal Processing Magazine.

[11]  Maya R. Gupta,et al.  Vector color filter array demosaicing , 2001, IS&T/SPIE Electronic Imaging.

[12]  Paul O'Leary.  Fitting geometric models in image processing using Grassmann manifolds , 2002, IS&T/SPIE Electronic Imaging.

[13]  Takeo Kanade,et al.  Surface Reflection: Physical and Geometrical Perspectives , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  P. Zsombor-Murray,et al.  Elementary Mathematics from an Advanced Standpoint , 1940, Nature.