The use of technical vision in flexible production systems to determine coordinates of randomly located objects
https://doi.org/10.25206/1813-8225-2023-186-60-66
Abstract
The article is devoted to the development of an object recognition algorithm using technical vision using Python and the OpenCV computer vision library. The article presents a program that allows you to set the coordinates of an object arbitrarily located in the field of view of the camera, as well as determine its orientation. This data will allow you to perform an effective capture of the object by the grip of the manipulator. In modern mechanical engineering, tasks of this kind are quite relevant, they make it possible to increase the autonomy of flexible production systems and make production safer.
About the Authors
K. V. Averkov
Omsk State Transport University
Russian Federation
Russian Federation
AVERKOV Konstantin Vasilyevich, Candidate of Technical Sciences, Associate Professor, Associate Professor of Technologies of Transport Engineering and Rolling Stock Repair Department
Omsk
D. S. Makashin
Omsk State Transport University; Omsk State Technical University
Russian Federation
Russian Federation
MAKASHIN Dmitriy Sergeyevich, Candidate of Technical Sciences, Associate Professor of Metalcutting Machines and Tools Department, Mechanical Engineering Institute, Associate Professor of Technologies of Transport Engineering and Rolling Stock Repair Department
Omsk
References
1. Bakhramov N. M., Khramov V. V. Problemy ispol’zovaniya tekhnicheskogo zreniya robotov-assistentov v kontekste tsifrovoy transformatsii [Directions for improving and integrating the robot vision system for integrated data mining] // Intellektual’nyye resursy — regional’nomu razvitiyu. Intellectual Resources for Regional Development. 2022. No. 2. P. 163–167. (In Russ.).
2. Androsov A. Yu., Gorshkov A. A., Lutskov Yu. I. Razmeshcheniye sistemy tekhnicheskogo zreniya na manipulyatore mobil’nogo robota [A placement of the vision system on the mobile robot manipulator] // Izvestiya TulGU. Tekhnicheskiye nauki. Izvestiya TulGU. Technical Sciences. 2014. No. 11-1. P. 418–426. (In Russ.).
3. Gurov V. S., Kolod’ko G. N., Kostyashkin L. N. [et al.]. Obrabotka izobrazheniy v aviatsionnykh sistemakh tekhnicheskogo zreniya [Image processing in aviation vision systems] / ed. by L. N. Kostyashkina, M. B. Nikiforova. Moscow, 2016. 240 p. (In Russ.).
4. Kotlyar D. I., Lomanov A. N., Korneychuk V. S. Primeneniye tekhnicheskogo zreniya dlya skanirovaniya kromki lopatki v protsesse remonta [The application of computer vision for scanning the blade edge during repair procedures // Vestnik Cherepovetskogo gosudarstvennogo universiteta. Cherepovets State University Bulletin. 2022. No. 1 (106). P. 42–54. DOI: 10.23859/1994-0637-2022-1-106-4 (In Russ.).
5. Yudin D. A., Magergut V. Z. Programmnyy kompleks sistemy tekhnicheskogo zreniya dlya otsenki sostoyaniya protsessa obzhiga [Computer vision system software for assessment of firing process] // Programmnyye produkty i sistemy. Software Products and Systems. 2013. No. 2. P. 257–262. (In Russ.).
6. Bublik D. A., Kononenko R. V. K voprosu kontrolya geometricheskikh parametrov ob”yektov dvoynoy krivizny bol’shikh razmerov pri pomoshchi sistemy tekhnicheskogo zreniya [To control of geometrical parameters of large size double curvature objects using computer vision system] // Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta. Proceedings of Irkutsk State Technical University. 2019. No. 4. P. 670–677. DOI: 10.21285/1814-3520-2019-4-670-677. (In Russ.).
7. Blokhin K. O., Matlakhov V. P., Khandozhko V. A. Naukoyemkaya tekhnologiya kontrolya kachestva keramicheskoy plitki s ispol′zovaniyem tekhnicheskogo zreniya [Science intensive technology for ceramic tile quality control using computer vision] // Naukoyemkiye tekhnologii v mashinostroyenii. Science-based Technology in Mechanical Engineering. 2018. No. 2. P. 27–34. (In Russ.).
8. Pruks V. E. Primer sistemy tekhnicheskogo zreniya na osnove neyrosetevogo klassifikatora [Example of a vision system based on a neural network] // Vestnik Baltiyskogo federal'nogo universiteta im. I. Kanta. Bulletin of I. Kant Baltic Federal University. 2012. No. 10. P. 99–103. (In Russ.).
9. Pelevin E. E., Balyasnyy S. V. Optimal'nyye algoritmy vydeleniya konturov izobrazheniya v sisteme tekhnicheskogo zreniya [Optimal algorithm of edge detection within the system of computer vision] // Juvenis scientia. Juvenis Scientia. 2016. No. 6. P. 6–8. DOI: 10.15643/jscientia.2016.6.195. (In Russ.).
10. Sokol E. E. Kontseptsiya sistemy tekhnicheskogo zreniya dlya kontrolya kachestva i sortirovki gotovykh detaley [Vision system concept for quality control and sorting of finished parts] // Molodezhnyy vestnik IrGTU. Young Researchers Journal of ISTU. 2021. No. 3. P. 20–25. (In Russ.).
Review
For citations:
Averkov K.V., Makashin D.S. The use of technical vision in flexible production systems to determine coordinates of randomly located objects. Omsk Scientific Bulletin. 2023;(2):60-66. (In Russ.) https://doi.org/10.25206/1813-8225-2023-186-60-66
Views:
7
JATS XML
Email this article 