Vision-based calibration method for spraying tools

Aiming at the problem of spray tool (spray gun) calibration of spray robot, a spray tool calibration method based on vision is proposed. Firstly, the data image of the cooperative target installed at the end of the spray robot is collected by an industrial camera. The initial value of the relative pose relationship between the cooperative target and the end of the robot is calculated by the calibration model, and the calibration parameters are optimized by the gradient descent algorithm. Finally, the pose transformation matrix of the spray tool coordinate system is obtained according to the pose relationship between the internal coordinate systems of the cooperative target. Finally, the calibration accuracy is analyzed by the winding point test: the maximum error of the spray tool calibration method based on vision is 3.98 mm, and the average error is 3.01 mm. The maximum error of the traditional four-point method is 4.41 mm, and the average error is 4.02 mm. Compared with the traditional four-point method, the calibration accuracy is improved and the time required for calibration is reduced.

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Publication Details

Published
2025-08-15
DOI
https://doi.org/10.1117/12.3072046
Primary Topic
Robotic Path Planning Algorithms
Type
article
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article

Vision-based calibration method for spraying tools

Di Zhao, Chengzhi Su, Shengming He, Jingran Yuan et al.
Robotic Path Planning Algorithms
article

Vision-based calibration method for spraying tools

Di Zhao, Chengzhi Su, Shengming He, Jingran Yuan, Enguo Wang
article en

Abstract

Aiming at the problem of spray tool (spray gun) calibration of spray robot, a spray tool calibration method based on vision is proposed. Firstly, the data image of the cooperative target installed at the end of the spray robot is collected by an industrial camera. The initial value of the relative pose relationship between the cooperative target and the end of the robot is calculated by the calibration model, and the calibration parameters are optimized by the gradient descent algorithm. Finally, the pose transformation matrix of the spray tool coordinate system is obtained according to the pose relationship between the internal coordinate systems of the cooperative target. Finally, the calibration accuracy is analyzed by the winding point test: the maximum error of the spray tool calibration method based on vision is 3.98 mm, and the average error is 3.01 mm. The maximum error of the traditional four-point method is 4.41 mm, and the average error is 4.02 mm. Compared with the traditional four-point method, the calibration accuracy is improved and the time required for calibration is reduced.

Changchun University of Science and Technology (CN), PLA Air Force Aviation University (CN)
Openalex Percentile: Top 79%
Robotic Path Planning Algorithms
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Vision-based calibration method for spraying tools — Di Zhao, Chengzhi Su, et al. · (2025) | TGRS Research Map | TGRS