doi: 10.7763/IJMO.2023.V13.829
Novel Method for Improving Motion Accuracy of a Large-Scale Industrial Robot to Perform Offline Teaching Based on Gaussian Process Regression
Naoki Maeda
*
,
Daiki Kato
,
Toshiki Hirogaki
,
Eiichi Aoyama
- Doshisha University, Japan
- Manuscript received April 9, 2022
- revised May 28, 2022
- accepted July 18, 2022
- published June 14, 2023
Abstract
Industrial robots that can respond to the current needs for variable-type and variable-volume production and that can play a variety of roles such as processing and transporting with a single robot to reduce time and cost. If realized, these robots will help save space in factories and increase production efficiency. However, this requires high positioning accuracy of the robot. In this study, we analyze the motion accuracy of industrial robots and their compensation method to construct this system. Here, we use a laser tracker to measure the coordinates of the hand tip of the robot when the robot is stationary. Subsequently, the error amount in an arbitrary posture is predicted using a Gaussian process. Furthermore, Bayesian optimization is used to efficiently search for points where the positioning error norm is likely to be large, which is then compensated for by a feedback method. This method successfully reduced the time cost of the experiment to approximately one-tenth of that required in the previous study and achieved a correction of approximately 66 %. However, because this method alone does not perform an exhaustive measurement, it is unclear whether all the points predicted to have small errors are so small that they do not require correction. Therefore, future studies, we will aim to verify this issue by considering the time efficiency.
Keywords
- Component
- automation
- industrial robots
- positioning error
- gaussian process
- bayesian optiomization
How to Cite
Naoki Maeda, Daiki Kato, Toshiki Hirogaki, and Eiichi Aoyama, "Novel Method for Improving Motion Accuracy of a Large-Scale Industrial Robot to Perform Offline Teaching Based on Gaussian Process Regression," International Journal of Modeling and Optimization, vol. 13, no. 3, pp. 77-80, 2023. https://doi.org/ 10.7763/IJMO.2023.V13.829
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).