Part Deviation Correction Method Based on Geometric Feature Recognition
<p>To realize the automatic loading process of parts, one of the core tasks is to identify the geometric contour of the part’s surface and the angular direction. Since the angular direction of each part is not the same when it arrives at the loading position, for example, there are two same ty...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2023-09-01
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| Series: | International Journal of Crowd Science |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/IJCS.2023.9100005 |
| _version_ | 1797649238674374656 |
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| author | Guoqing Zhang Hongbo Sun |
| author_facet | Guoqing Zhang Hongbo Sun |
| author_sort | Guoqing Zhang |
| collection | DOAJ |
| description | <p>To realize the automatic loading process of parts, one of the core tasks is to identify the geometric contour of the part’s surface and the angular direction. Since the angular direction of each part is not the same when it arrives at the loading position, for example, there are two same types of parts with the same pattern, when they arrive at the loading position, the pattern on one part may be on the right side of the part surface, and the pattern on the other part may be on the left side of the part surface, the gripper of the mechanical arm needs to rotate above the parts in order to grab the parts during each loading process. If the rotation angle is wrong, there will be an impact between the gripper and the parts. Therefore, in order to solve the problem of different angles, this paper proposes a method of parts deviation correction based on geometric features. In this work, firstly, the acquired image is preprocessed, the image background is separated, and the geometric features of the parts are obtained. Then edge detection is used to obtain the set of edge pixels to obtain the contour in the image. Finally, the image moment and measurement model are used to output angular direction. Through 500 repeated detection experiments, the results show that this method can perform better angular direction correction. The maximum angular direction difference is 0.073°, which is 0.856° and 1.793° higher than the Least square method and Hough transform circle detection accuracy, respectively. The average detection time is 1.89 s and is 0.336 s and 1.39 s less than the Least square method and Hough transform circle detection, which meets the requirements of industrial applications.</p> |
| first_indexed | 2024-03-11T15:42:22Z |
| format | Article |
| id | doaj.art-9f65c98bfff14be1a8014edd4a7fd5d7 |
| institution | Directory Open Access Journal |
| issn | 2398-7294 |
| language | English |
| last_indexed | 2024-03-11T15:42:22Z |
| publishDate | 2023-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | International Journal of Crowd Science |
| spelling | doaj.art-9f65c98bfff14be1a8014edd4a7fd5d72023-10-26T10:18:46ZengTsinghua University PressInternational Journal of Crowd Science2398-72942023-09-017311311910.26599/IJCS.2023.9100005Part Deviation Correction Method Based on Geometric Feature RecognitionGuoqing Zhang0Hongbo Sun1School of Computer and Control Engineering, Yantai University, Yantai 264005, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai 264005, China<p>To realize the automatic loading process of parts, one of the core tasks is to identify the geometric contour of the part’s surface and the angular direction. Since the angular direction of each part is not the same when it arrives at the loading position, for example, there are two same types of parts with the same pattern, when they arrive at the loading position, the pattern on one part may be on the right side of the part surface, and the pattern on the other part may be on the left side of the part surface, the gripper of the mechanical arm needs to rotate above the parts in order to grab the parts during each loading process. If the rotation angle is wrong, there will be an impact between the gripper and the parts. Therefore, in order to solve the problem of different angles, this paper proposes a method of parts deviation correction based on geometric features. In this work, firstly, the acquired image is preprocessed, the image background is separated, and the geometric features of the parts are obtained. Then edge detection is used to obtain the set of edge pixels to obtain the contour in the image. Finally, the image moment and measurement model are used to output angular direction. Through 500 repeated detection experiments, the results show that this method can perform better angular direction correction. The maximum angular direction difference is 0.073°, which is 0.856° and 1.793° higher than the Least square method and Hough transform circle detection accuracy, respectively. The average detection time is 1.89 s and is 0.336 s and 1.39 s less than the Least square method and Hough transform circle detection, which meets the requirements of industrial applications.</p>https://www.sciopen.com/article/10.26599/IJCS.2023.9100005surface geometric contourimage processingedge detectionimage momentmeasurement model |
| spellingShingle | Guoqing Zhang Hongbo Sun Part Deviation Correction Method Based on Geometric Feature Recognition International Journal of Crowd Science surface geometric contour image processing edge detection image moment measurement model |
| title | Part Deviation Correction Method Based on Geometric Feature Recognition |
| title_full | Part Deviation Correction Method Based on Geometric Feature Recognition |
| title_fullStr | Part Deviation Correction Method Based on Geometric Feature Recognition |
| title_full_unstemmed | Part Deviation Correction Method Based on Geometric Feature Recognition |
| title_short | Part Deviation Correction Method Based on Geometric Feature Recognition |
| title_sort | part deviation correction method based on geometric feature recognition |
| topic | surface geometric contour image processing edge detection image moment measurement model |
| url | https://www.sciopen.com/article/10.26599/IJCS.2023.9100005 |
| work_keys_str_mv | AT guoqingzhang partdeviationcorrectionmethodbasedongeometricfeaturerecognition AT hongbosun partdeviationcorrectionmethodbasedongeometricfeaturerecognition |