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Ensuring the compliance of the finished product with the project during the manufacturing of cutting heads/drums of the mining machines, largely determines the efficiency of rock mining, especially hard-to-cut rocks. The manufacturing process of these crucial elements of cutting machines is being robotized in order to ensure high accuracy and repeatability. This determines, among others the need to assess in real-time the degree of the approach of pick holders positioned by the industrial robot to the side surface of the working unit of the cutting machine in their target position. This problem is particularly important when in the manufacturing process are used the bodies of decommissioned cutting heads/drums, from which old pick holders have been removed. The shape and external dimensions of these hulls, unless they are subjected to regeneration, may differ quite significantly from the nominal ones. The publication, on the example of a road header cutting head, presents the procedure for automatically identifying and indexing markers displayed on its side surface, recorded on measuring photos by two digital cameras of a 3D vision system. Experimental research of the developed method was carried out using the KUKA VisionTech vision system installed on the test stand in the robotics laboratory of the Department of Mining Mechanization and Robotization at the Faculty of Mining, Safety Engineering and Industrial Automation of the Silesian University of Technology. Data processing was carried out in the Matlab environment using the libraries of the Image Processing Toolbox. The functions provided in this library were used in the developed algorithm, implemented in the software. This algorithm allows automatic identification of markers located in the images of the side surface of the cutting head. This is the basis for determining their location in space. The publication presents a method of segmenting images recorded by cameras into homogeneous areas. The method of separating interesting areas from the image by comparison to the pattern was presented. Also shown is the method of the automatic numbering of mutually matching pairs of markers on photos from two cameras included in the vision system depending on the spatial orientation of the marker grid in the measuring images.
Wydawca
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Rocznik
Tom
Strony
251--271
Opis fizyczny
Bibliogr. 25 poz., rys.
Twórcy
autor
- Silesian University of Technology, Poland
autor
- Silesian University of Technology, Poland
Bibliografia
- 1. Ashburner, J. and Friston, K.J. (1997) Spatial Transformation of Images. London.
- 2. Bräuer-Burchardt C. Voss K. and Schiller F. (2000) ‘Automatic Lens Distortion Calibration Using Single Views’. in Sommer, G. Krüger, N. Perwass, C. (ed) Mustererkennung 2000. Informatik aktuell. Berlin, Heidelberg: Springer, pp. 187-194.
- 3. Bradley, D. Roth, G. (2007) ‘Adaptive Thresholding Using the Integral Image’, Graphics Tools, 12(2), pp. 13-21.
- 4. Cheluszka, P. Nocoń, M. (2015) ‘Robotised digitalisation technology of roadheader working units for the purpose of manufacturing quality control’, Surface Mining, 56(6), pp. 11-23. (in Polish)
- 5. Chen, M. Artieres, T. Denoyer, L. (2019) ‘Unsupervised Object Segmentation by Redrawing’, Conference on Neural Information Processing Systems, NeurIPS.
- 6. Corke, P.I. (1996) Visual control of robots: High-Performance Visual Servoing. Australia: CSIRO Division of Manufacturing Technology.
- 7. Dolipski M. Cheluszka P. Remiorz E. Sobota P. Osadnik J. (2010) ‘Manufacturing processes of cutting machine working units using robotic technologies’ in Mikołajczyk, T. (ed) CAX’2010 Group work. Bydgoszcz: Wydawnictwo Uczelniane Uniwersytetu Technologiczno-Przyrodniczego, pp. 85-90. (in Polish)
- 8. Dudzik, S. Sochacka, O. (2018) ‘Application of local progression methods for detecting defects using active thermography’, Intercollegiate Metrologists Conference Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej, 59(8), pp. 43-46. (in Polish)
- 9. Ewald, M. (2009) ‘Content-Based Image Indexing and Retrieval in an Image Database for Technical Domains’, Transactions on Machine Learning and Data Mining, 2(1), pp. 3-22.
- 10. Fisker, R. Poulsen, H.F. Schou, J. Carstensen, J.M. Garbe, S. (1998) ‘Use of Image-Processing Tools for Texture Analysis of High-Energy X-ray Synchrotron Data’, Journal of Applied Crystallography, 31, pp. 647-653.
- 11. Goral, A. (2014) ‘Comparison of selected marker tracking methods for video locators’, Przegląd elektrotechniczny, 90(5), p97-101. (in Polish
- 12. ) Jagieła-Zając, A. Cheluszka, P. (2019) ‘Measurement of the pick holders position on the side surface of the cutting head of a mining machine with the use of stereoscopic vision’, IOP Conference Series: Materials Science and Engineering, 679(1) [online]. Available at: https://iopscience.iop.org/article/10.1088/1757-899X/679/1/012005/pdf (Accessed: 28 February 2020).
- 13. Kubiak, I. (2014) ‘Algorithm for equalizing the histogram of image pixel amplitude values in the image processing process obtained from electromagnetic emission signals correlated with video signals’, Przegląd Telekomunikacyjny, 7, pp. 682-686. (in Polish)
- 14. Kryjak, T. Komorkiewicz, M. (2013) ‘Real-time FPGA implementation of disparity map calculation for a 3D video stream’, PAK, 59(8), pp. 748-750. (in Polish)
- 15. Malina, W. Smiatacz, M. 2005 Digital image processing methods. Warszawa: Akademicka oficyna wydawnicza EXIT. (in Polish)
- 16. Raviya, K.S. Kothari, A.M. Vyas, D.V. (2014) ‘Depth and Disparity Extraction Structure for Multi View Images-Video Frame- A Review’, European Journal of Academic Essays, 1(10), pp. 29-35.
- 17. Roy, P. Dey, N. Dey, G. Dutta, S. Chakraborty, S. Rey, R. (2014) ‘Adaptive thresholding: A comparative study’, International Conference on Control, Instrumentation, Communication and Computational Technologies, p1182-1186.
- 18. Sricha, R. Khan, A. (2013) ‘Morphological Operations for Image Processing: Understanding and its Applications’, NCVSComs conference proceedings, 13, pp. 17-19.
- 19. Szymczyk, T. (2008) ‘Pattern matching method in image recognition - limitations, problems and modifications of the method’, Automatyka, 12(2), pp. 449-462. (in Polish)
- 20. Tadeusiewicz, R. Korohoda, P. (1997) Computer analysis and image processing. Kraków: Wydawnictwo Fundacji Postępu Telekomunikacji. (in Polish)
- 21. Wojciechwski, P. Bal, A. (2012) ‘Method of finding cells and counting foci of histone γ-H2AX in images for the purpose of double-stranded DNA crack detection’, PAK, 58(4), pp. 387-390. (in Polish)
- 22. Wrobel, Z. Koprowski, R. (2004) Image processing practice with tasks in the program Matlab. EXIT. (in Polish)
- 23. Yang, W. Zhang, G. Zhang, X. (2019) ‘Infrared LEDs-based pose estimation with underground camera model for Boom-type roadheader in coal mining’, IEEE, 7, pp. 33698-33712.
- 24. Zhang, Y. (2009) ‘Image processing using spatial transform’, International Conference on Image Analysis and Signal Processing, pp. 282-285. Ż
- 25. orski, W. Samsel, P. 2009 ‘Segmentation of color images with irregular patterns’ Biuletyn instytutu automatyki i robotyki, 26, pp. 45-65. (in Polish)
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-dfceb719-afd7-4628-b570-8ce4937d39bf