The Library
Accuracy evaluation of hand-eye calibration techniques for vision-guided robots
Tools
Enebuse, Ikenna, Kader Ibrahim, Babul K. S. M., Foo, Mathias, Matharu, Ranveer S and Ahmed, Hafiz (2022) Accuracy evaluation of hand-eye calibration techniques for vision-guided robots. PLoS One, 17 (10). e0273261. doi:10.1371/journal.pone.0273261 ISSN 1932-6203.
|
PDF
WRAP-accuracy-evaluation-hand-eye-calibration-techniques-vision-guided robots-Foo-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2227Kb) | Preview |
Official URL: https://doi.org/10.1371/journal.pone.0273261
Abstract
Hand-eye calibration is an important step in controlling a vision-guided robot in applications like part assembly, bin picking and inspection operations etc. Many methods for estimating hand-eye transformations have been proposed in literature with varying degrees of complexity
and accuracy. However, the success of a vision-guided application is highly impacted by the accuracy the hand-eye calibration of the vision system with the robot. The level of this accuracy depends on several factors such as rotation and translation noise, rotation and
translation motion range that must be considered during calibration. Previous studies and benchmarking of the proposed algorithms have largely been focused on the combined effect of rotation and translation noise. This study provides insight on the impact of rotation and
translation noise acting in isolation on the hand-eye calibration accuracy. This deviates from the most common method of assessing hand-eye calibration accuracy based on pose noise (combined rotation and translation noise). We also evaluated the impact of the robot motion range used during the hand-eye calibration operation which is rarely considered. We provide quantitative evaluation of our study using six commonly used algorithms from an implementation
perspective. We comparatively analyse the performance of these algorithms through simulation case studies and experimental validation using the Universal Robot’s UR5e physical robots. Our results show that these different algorithms perform differently when the noise conditions vary rather than following a general trend. For example, the simultaneous methods are more resistant to rotation noise, whereas the separate methods are better at dealing with translation noise. Additionally, while increasing the robot rotation motion span during calibration enhances the accuracy of the separate methods, it has a negative effect on the simultaneous methods. Conversely, increasing the translation motion range improves the accuracy of simultaneous methods but degrades the accuracy of the separate methods. These findings suggest that those conditions should be considered when benchmarking algorithms or performing a calibration process for enhanced accuracy.
Item Type: | Journal Article | ||||||
---|---|---|---|---|---|---|---|
Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures |
||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
Library of Congress Subject Headings (LCSH): | Robots, Industrial, Robots , Robots -- Motion , Robotics , Calibration | ||||||
Journal or Publication Title: | PLoS One | ||||||
Publisher: | Public Library of Science | ||||||
ISSN: | 1932-6203 | ||||||
Official Date: | 19 October 2022 | ||||||
Dates: |
|
||||||
Volume: | 17 | ||||||
Number: | 10 | ||||||
Article Number: | e0273261 | ||||||
DOI: | 10.1371/journal.pone.0273261 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 20 October 2022 | ||||||
Date of first compliant Open Access: | 21 October 2022 | ||||||
RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year