The Method of the Computer Vision System Coordinate Transformation for an Industrial Robot for a Laser Welding Operation

In this article, the authors consider the problem of coordinate transformation in computer vision systems (CVS) of robotic system (RS) for laser welding. Laser welding is a highly efficient technological operation in many respects superior to common types of welding due to the high concentration of energy at the welding point. However, laser welding has a number of requirements, including a high requirement for the accuracy of positioning the laser head relative to the welding joint. Adaptive control systems based on CVS allow to provide the required accuracy. The main task of CVS is to determine the three-dimensional coordinates of the welding joint using a video sensor, convert the received coordinates into a coordinate system in which the RS is controlled, and the converted coordinates are transferred to the control system. Note, the accuracy and determination of coordinates are important factors. To accomplish this task, it is necessary to consider the coordinate transformation as a set of actions performed taking into account the specifics of using CVS as part of an RS for laser welding. For this purpose, the article analyzes typical schemes for placing CVS on industrial robots and proposes the most suitable configuration for laser welding. A methodology was also developed for measuring the three-dimensional coordinates of the welding joint using the triangulation method. The authors carried out a comparative analysis of the main existing methods for calibrating CVS video sensors and proposed an original method for calibrating videosensors taking into account the specifics of the functioning of the RS for laser welding. As a result, the article presents the rationale for the need to consider coordinate conversion to CVS as part of an RS for laser welding, as well as a set of methods that allows to perform conversions from a virtual coordinate system of a video sensor to a coordinate system of a robot, which allows direct control based on CVS data. In conclusion, the authors give a method for calibrating a video sensor, which allows achieving the requirements specified in the article for the accuracy of determining the coordinates of the welding joint.

robotic vision system, coordinate transformation, camera calibration, laser welding, robotic system, triangulation, Tsai calibration technique, videosensors

1. Poduraev Y. V., Ilukhin Y. V., Yakovlev S. F., Vozhinskiy A. V. The main aspects of the creation of domestic robotic laser welding systems with an adaptive control system, Mekhatronika, Avtomatizatsiya, Upravlenie, 2011, vol. 11. pp. 18—22.

2. Zarubin S. G., Nicholaev P. M., Ilukhin Y. V., Poduraev Y. V., Polivanov A. Y. Robotic technological complex for precision plasma cutting, plasma hardening of the surface and applying protective coatings, Engineering Technology, 2013, vol . 9, pp. 48—53 (in Russian).

3. Poduraev Y. V., Ilukhin Y. V., Yakovlev S. F., Vozhinskiy A. V. Prospects for the development of dome stic robotic laser welding systems, Engineering Technology, 2012, vol. 1. pp. 32—35.

4. Poezhaeva E. V., Sergeev A. A., Misurov M. N. Development of the concept of adaptive seam tracking in real time for robotic welding, Young Scientist, 2015, vol. 16, pp. 214—218 (in Russian).

5. Tsai R. Y. A Versatile Camera Calibration Technique for High-Accuracy 3d Machine Vision Metrology Using OffThe-Shelf TV Cameras and Lenses / R. Y. Tsai, IEEE Journal on Robotics and Automation, 1987, vol. 3(4), pp. 323—344.

6. Zhang Z. A flexible new technique for camera calibration, IEEE Trans. on PAMI, 2000, vol. 22(11), pp. 1330—1334.

7. Maybank S. J., Faugeras O. D. A theory of self-calibration of a moving camera, International Journal of Computer Vision, 1992, vol. 8(2), pp. 123—151.

8. Ilukhin Y. V. Implementation of the mechatronic approach in the construction of computer control systems for laser and plasma cutting systems, Mekhatronika, Avtomatizatsiya, Upravlenie, 2005, vol. 10, pp. 25—50 (in Russian).

9. Zarudnev A. S., Ilukhin Y. V. Improving the performance of laser systems based on the prediction of contour error, Mekhatronika, Avtomatizatsiya, Upravlenie, 2010. vol. 9, pp. 52—56 (in Russian).

10. Faig W. Calibration of Close-Range Photogrammetry Systems: Mathematical Formulation, Photogrammetric Eng. and Remote Sensing, 1975, vol. 41, no. 12, pp. 1479—1486.

11. Sturm S., Maybank S. J. On Plane-Based Camera Calibration: A General Algorithm Singularities Applications, Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1999, pp. 432—437.

12. Klevalin V. A., Polivanov A. Y. Digital recognition methods in vision systems of industrial robots, Mekhatronika, Avtomatizatsiya, Upravlenie, 2008, vol. 5, pp. 56 (in Russian).

13. Klevalin V. A., Polivanov A. Y. Vision systems in industrial robotics, Mekhatronika, Avtomatizatsiya, Upravlenie, 2010, vol. 9, pp. 26—36 (in Russian).

14. Polivanov A. Y., Shatunov K. V. Adaptive robot control KUKA KR-16 using a vision system, Electrical Complexes and Control Systems, 2012, vol. 3, pp. 60—64 (in Russian).

15. Klevalin V. A., Polivanov A. Y., Shatunov K. V. Improving the accuracy of vision systems of industrial robots by calibrating the image receiver, Vestnik MGTU STANKIN, 2013, vol. 1(24), pp. 34—38 (in Russian).