<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper proposes a fast recognition and positioning algorithm based on deep learning for the problems of slow recognition of complex workpieces, low accuracy, and inaccurate positioning of industrial robots. The image grayscale and parameter calibration are performed by building an industrial high-precision vision system on the target image information. The target image is then localized and segmented by boundary pixel detection, and the trained improved SSD algorithm is used to identify the target, obtain the coordinates of the target location and the category to which it belongs, and realize the industrial robot sorting. The results show that the target recognition algorithm based on the improved SSD algorithm has an error of less than 0.5 mm, the fastest recognition speed of 0.045 sec/each, and the recognition accuracy can be maintained above 98% in the experimental environment, and the distance error between the real point and the calculated point is 8.09 mm on average, indicating that the algorithm has good accuracy and stability. Building a prototype system based on the improved SSD algorithm for industrial robots with complex processes is expected to provide an automated robot identification and positioning solution for production lines.</p>
</abstract>

This paper proposes a fast recognition and positioning algorithm based on deep learning for the problems of slow recognition of complex workpieces, low accuracy, and inaccurate positioning of industrial robots. The image grayscale and parameter calibration are performed by building an industrial high-precision vision system on the target image information. The target image is then localized and segmented by boundary pixel detection, and the trained improved SSD algorithm is used to identify the target, obtain the coordinates of the target location and the category to which it belongs, and realize the industrial robot sorting. The results show that the target recognition algorithm based on the improved SSD algorithm has an error of less than 0.5 mm, the fastest recognition speed of 0.045 sec/each, and the recognition accuracy can be maintained above 98% in the experimental environment, and the distance error between the real point and the calculated point is 8.09 mm on average, indicating that the algorithm has good accuracy and stability. Building a prototype system based on the improved SSD algorithm for industrial robots with complex processes is expected to provide an automated robot identification and positioning solution for production lines.

Research on visual recognition and positioning of industrial robots based on big data technology

Shanxi Vocational University of Engineering Science and Technology

Taiyuan University of Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper proposes a fast recognition and positioning algorithm based on deep learning for the problems of slow recognition of complex workpieces, low...

Research on visual recognition and positioning of industrial robots based on big data technology

Online veröffentlicht: 16. Aug. 2023

Seitenbereich: -

Eingereicht: 16. Sept. 2022

Akzeptiert: 24. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00193

Schlüsselwörter
Industrial robot, Deep learning, Vision system, Parameter calibration, Improved SSD algorithm

© 2023 Jin Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on visual recognition and positioning of industrial robots based on big data technology

Online veröffentlicht: 16. Aug. 2023

Seitenbereich: -

Eingereicht: 16. Sept. 2022

Akzeptiert: 24. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00193

SchlüsselwörterIndustrial robot, Deep learning, Vision system, Parameter calibration, Improved SSD algorithm

© 2023 Jin Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Industrial robot, Deep learning, Vision system, Parameter calibration, Improved SSD algorithm