Research On Kinematic Calibration Method Of Six Degree Of Freedom Industrial Robot

With the development of electronic technology and the improvement of production technology,robots are increasingly used in social services and industrial production.As a key index for evaluating robot performance,robot positioning accuracy has a significant impact on the stability,accuracy and reliability of robot motion.Especially in industrial production,with the development of science and technology,mechanical systems have become more and more precise and complex.Due to processing,deformation and other factors,the problem of large accuracy errors of industrial robots has been paid more and more attention.In this paper,the calibration method is used to study the parameter identification and error compensation of the geometric parameter error problem of a six-degree-of-freedom industrial robot to improve the positioning accuracy of the robot.The main research contents include the following three aspects.(1)Based on the D-H method,the kinematics model of IRB-1410 industrial robot is constructed and the forward and inverse kinematics are analyzed.The D-H modeling principle is introduced,and the geometric parameters of the robot are obtained by combining the performance parameters of the robot,and the forward motion model of the IRB-1410 robot is established.The closed solution method is used to calculate multiple sets of inverse solutions,and the optimal solution is selected based on the shortest path principle to obtain the inverse kinematics model.The correctness of the kinematics model is verified by MATLAB.(2)Aiming at the problem that each parameter error has different degrees of influence on the end position error,a method based on the MD-H error model to calculate the influence weight of each parameter error on the end position error is proposed.An error model is established based on MD-H method and differential transformation principle,and then construct a function of the six joint angles uniformly changing with time t to ensure that the geometric parameters of each link participate in the movement and cause error accumulation.Based on the error model,the maximum,absolute average,and root mean square value of the end position error caused by the error of each parameter in the same joint group sample are calculated.This is used as an evaluation item to calculate the impact weight and analyze the impact of different parameter errors on end position errors.The calculation results show that the error of the angle parameter has a greater impact on the change of the end position error,especially the joint angle error has the most significant impact,accounting for about 80% of the impact weight.(3)Aiming at the problem of how to identify the parameters more effectively and completely to achieve better error compensation in calibration,a calibration method involving recursive least squares and genetic algorithm to participate in identification and compare with each other is proposed.Analyze the error model by QR decomposition method,find redundant parameters and get independent model to participate in identification,set up 100 sets of data samples,and use recursive least squares to identify the model parameters.In order to fully identify the parameters,the global optimal search property of the genetic algorithm is used to identify all parameters,and dynamic crossover and mutation operators are set to optimize the results.Compare and analyze the validity and accuracy of the identification results calculated by the two methods,and the robot parameters are corrected for error compensation.The results of simulation analysis show that both methods can effectively identify the parameters.After the error compensation,the recursive least squares is used to reduce the position error by 86.45%,and the genetic algorithm is used to reduce the position error by 94.1%.The recognition degree of the genetic algorithm is more accurate than the recursive least squares and the positioning accuracy is more significant.