Affiliations: [a] School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou, Guangxi, China | [b] School of Mechanical and Energy Engineering, Shaoyang University, Shaoyang, Hunan, China | [c] School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong, China
Correspondence:
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Corresponding author: Dongyuan Ge, School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou, Guangxi 545006, China. E-mail: [email protected].
Abstract: With the promotion of Industry 4.0 reform, the trend of intelligent and precise production in the production workshop is gradually highlighted. This directly leads to higher requirements for robot hand eye coordination accuracy in automated workshops. In order to achieve more precise robot hand eye coordination control, this study designed a new mean calculation method based on the probability density theory, and designed a new mean robot hand eye calibration algorithm based on this. After the test, it is found that the translation error and rotation error calculated by the new mean algorithm are 0.26 and 0.92 respectively, which are significantly lower than other comparison algorithms when using all test samples of normal distribution. And the calculation time of the algorithm when using all the test samples is 2115 ms, which is also significantly lower than the comparison algorithm. The simulation results show that the new mean hand eye calibration method designed in this study can achieve more accurate hand eye coordination control of robots, and has certain application potential in high-precision industrial production scenarios.
Keywords: Hand-eye calibration, machine vision, sensor, robotic arm, probability density theory, mean value calculation
