| Title: | Exploiting image quality measure for automatic trajectory generation in robot-aided visual quality inspection |
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| Authors: | ID Jafari-Tabrizi, Atae (Author)
ID Gruber, Dieter P. (Author)
ID Gams, Andrej, Institut Jožef Stefan (Author) |
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| Files: |  URL - Source URL, visit https://link.springer.com/article/10.1007/s00170-024-13609-5#article-info
 PDF - Presentation file, download (3,00 MB)
MD5: 4F1D767A2FAFE82140015A562D0FCBC6
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  IJS - Jožef Stefan Institute
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| Abstract: | Currently, the standard method of programming industrial robots is to perform it manually, which is cumbersome and time-consuming. Thus, it can be a burden for the flexibility of inspection systems when a new component with a different design needs to be inspected. Therefore, developing a way to automate the task of generating a robotic trajectory offers a substantial improvement in the field of automated manufacturing and quality inspection. This paper proposes and evaluates a methodology for automatizing the process of scanning a 3D surface for the purpose of quality inspection using only visual feedback. The paper is divided into three sub-tasks in the same general setting: (1) autonomously finding the optimal distance of the camera on the robot’s end-effector from the surface, (2) autonomously generating a trajectory to scan an unknown surface, and (3) autonomous localization and scan of a surface with a known shape, but with an unknown position. The novelty of this work lies in the application that only uses visual feedback, through the image focus measure, for determination and optimization of the motion. This reduces the complexity and the cost of such a setup. The methods developed have been tested in simulation and in real-world experiments and it was possible to obtain a precision in the optimal pose of the robot under 1 mm in translational, and 0.1° in angular directions. It took less than 50 iterations to generate a trajectory for scanning an unknown free-form surface. Finally, with less than 30 iterations during the experiments it was possible to localize the position of the surface. Overall, the results of the proposed methodologies show that they can bring substantial improvement to the task of automatic motion generation for visual quality inspection. |
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| Keywords: | robot learning, eobotic quality inspection, visual quality inspection |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 29.11.2023 |
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| Article acceptance date: | 05.04.2024 |
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| Publication date: | 26.04.2024 |
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| Publisher: | Springer Nature |
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| Year of publishing: | 2024 |
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| Number of pages: | str. [1-17] |
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| Numbering: | Vol. , iss. , [article no.] |
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| Source: | Švica |
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| PID: | 20.500.12556/DiRROS-18884  |
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| UDC: | 007.52 |
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| ISSN on article: | 1433-3015 |
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| DOI: | 10.1007/s00170-024-13609-5 |
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| COBISS.SI-ID: | 194551555 |
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| Copyright: | © The Author(s) 2024 |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 7. 5. 2024;
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| Publication date in DiRROS: | 09.05.2024 |
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| Views: | 531 |
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| Downloads: | 623 |
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