PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Optimization of manipulation logistics using data matrix codes

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the paper we deal with optimization of manipulation logistics using Data Matrix codes. Our goal is scanning and decoding Data Matrix codes in real-time. We have designed and verified an efficient computer aided method for location of the Data Matrix codes. This method is also suited to real-time processing and has been verified on a test set of images taken from real industrial world. We have proposed a modified, computationally efficient local thresholding technique that uses local mean and variation under the sliding window. The proposed Data Matrix code localization algorithm utilizes the connecting of the adjoining points into the continuous regions and determining of the boundaries of the outer region and it works in two basic steps: localization of the Finder Pattern and verification of the Timing Pattern. Part of the algorithm deals also with the decoding of the Data Matrix code using external libraries. Data Matrix codes can be used to mark logistic units, parts, warehousing positions, but also for automated robot navigation. Because of their low cost, accuracy, speed, reliability, flexibility and efficiency, as well as the ability to write large amounts of data on a small area, they still have a great advantage in logistics.
Twórcy
  • Technical University in Zvolen, Masarykova 24, 960 53 Zvolen, Slovakia
  • Technical University in Zvolen, Masarykova 24, 960 53 Zvolen, Slovakia
  • Tomas Bata University in Zlín, Department of Logistics, Faculty of Logistics and Crisis Management, Studentské nám. 1532, 686 01 Uherské Hradiště, Czech Republic
Bibliografia
  • 1. Huang Q., Chen W-S., Huang X-Y. and Zhu Y-Y. Data Matrix Code Location Based on Finder Pattern Detection and Bar Code Border Fitting. Mathematical Problems in Engineering, 2012, 13 p.
  • 2. Burns J.B., Hanson A.R. and Riseman E.M. Extracting straightlines. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986.
  • 3. Gioi G.R., Jakubowicz J., Morel J.M. and Randall G. LSD: a fastline segment detector with a false detection control. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010.
  • 4. Donghong H., Hui T. and Xinmeng C. Radon transformation applied in two dimensional barcode image recognition. Journal of Wuhan University, 2005.
  • 5. Chenguang Z., Na Y. and Rukun H. Study of two dimensional barcode identification technology based on HOUGH transform. Journal of Changchun Normal University, 2007.
  • 6. Hrčková M. and Koleda P. Identifikácia objektov v obraze na základe geometrických príznakov. Acta Facultatis Technicae, XIX(2), 2014, 13–19.
  • 7. Waters J.: QR Codes For Dummies. 1. London: For Dummies, 2012.
  • 8. Price K.: QR Codes Made EZ. A Complete Guide to Creating and Implementing QR Codes. 1. North Charleston: CreateSpace, 2014.
  • 9. Karrach L. and Pivarčiová E. Data Matrix code location marked with laser on surface of metal tools, Acta facultatis technicae, XXII(2), 2017, 29–38.
  • 10. Karrach L. and Pivarčiová E. The analyse of the various methods for location of Data Matrix codes in images. Elektro 2018: 12th International Conference, Mikulov, 2018.
  • 11. Lin J-A., Fuh CH-S. 2D Barcode Image Decoding. Mathematical Problems in Engineering, 2013.
  • 12. Gaur P. and Tiwari S. Recognition of 2D Barcode Images Using Edge Detection and Morphological Operation. International Journal of Computer Science and Mobile Computing, 2014.
  • 13. Li S., Shang J., Duan Z. and Huang J. Fast detection method of quick response code based on run-length coding. IET Image Processing, 12(4), 2018, 546 –551.
  • 14. Hansen D.K. and Nasrollahi K. Real-Time Barcode Detection and Classification Using Deep Learning. 9th International Joint Conference on Computational Intelligence, 2017.
  • 15. Szymczyk T., Montusiewicz J. and Gutek D. Navigation in large-format buildings based on RFID sensors and QR and AR markers. Advances in Science and Technology Research Journal, 10(31), 2016, 263–273.
  • 16. Turygin Y., Božek P., Nikitin Y., Sosnovich E. and Abramov A.: Enhancing the reliability of mobile robots control process via reverse validation. International Journal of Advanced Robotic Systems, 13(6), 2016, 1–8.
  • 17. Božek P. Robot path optimization for spot welding applications in automotive industry. Tehnicki Vjesnik – Technical Gazette, 20(5), 2013, 913–917.
  • 18. More M., Liska O. and Kovac J. Experimental verification of force feedback for rehabilitation robot. International journal of engineering research in Africa, 18, 2015, 123–129.
  • 19. Liptak T., Virgala I., Frankovsky P., Šarga P., Gmiterko A. and Baločková L. A geometric approach to modeling of four- and five-link planar snake-like robot. International journal of advanced robotic systems, 13, 2016.
  • 20. Umut I., Aki O., Uçar E. and Öztürk L. Detection of driver sleepiness and warning the driver in real-time using image processing and machine learning techniques. Advances in Science and Technology Research Journal. 11(2), 2017, 95–102.
  • 21. Neradilova H. and Fedorko G. The Use of Computer Simulation Methods to Reach Data for Economic Analysis of Automated Logistic Systems. Open Engineering, 6(1), 2016, 700–710.
  • 22. Molnar V., Fedorko G., Andrejiova M., Grinčová A. and Michalík P. Online monitoring of a pipe conveyor. Part I: Measurement and analysis of selected operational parameters. Measurement, 94, 2016, 364–371.
  • 23. Lonkwic P., Rozylo P. and Debski H. Numerical and experimental analysis of the progressive gear body with the use of finite-element method. Eksploatacja i niezawodnosc-maintenance and reliability, 17(4), 2015, 544–550.
  • 24. Garbacz T., Jachowicz T., Gajdos I. and Kijewski G. Research on the influence of blowing agent on selected properties of extruded cellular products. Advances in Science and Technology Research Journal, 9(28), 2015, 81–88.
  • 25. Fedorko G., Molnar V., Dovica M., Husáková N., Kráľ J. and Ferdynus M. The use of industrial metrotomography in the field of maintenance and reliability of rubber-textile conveyor belts in closed continuous transport systems. Eksploatacja i Niezawodnosc. Maintenance and Reliability, 18(4), 2016, 539–543.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-17423c47-2ad3-4add-bd52-57c2cd44d28a
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.