Improved Gray Wolf Optimization Method for Hand Eye Calibration
In order to solve the problem of low accuracy in Hand-Eyecalibration of machine vision intelligent robot with Eye-in-Hand,an improved gray wolf algorithm for Hand-Eye calibration is proposed.Firstly,the mathematical model of Hand-Eye calibration for Eye-in-hand machine vision intelligent robot is established.By analyzing the factors affecting the Hand-Eyecali-bration error,a pose generation scheme for reducing the Hand-Eye calibration error is proposed.Then,combining dimension learning and differential evolution strategy,the improved gray wolf algorithm is used for nonlinear optimization of the analytical solution obtained by the traditional Hand-Eye calibration algorithm,which avoids the defects of the traditional optimization al-gorithm,such as early convergence and falling into local optimal solution in the iterative process.Finally,the Hand-Eye cali-bration experiment is carried out with real equipment,and the experimental results show that the method is feasible and effective to reduce the Hand-Eyecalibration error.
Intelligent RobotHand-Eye CalibrationNonlinear OptimizationGray Wolf AlgorithmDimension-al Learning
万方数据
维普
