Hand-eye calibration method and machine vision research based on sensor network

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.