A New Manipulator Calibration Method for the Identification of Kinematic and Compliance Errors Using Optimal Pose Selection

In this study, a manipulator calibration algorithm is suggested to decrease the positional errors of an industrial robotic manipulator using a genetic algorithm to select optimal measurement poses. First, a genetic algorithm based on the observability index is used for the selection of optimal measurement poses. By employing the selected optimal poses, conventional kinematic calibration is used to identify the geometric errors of the robot. Finally, to further improve the positional accuracy of the robot, compliance errors are compensated by a radial basis function neural network based on effective torques. The proposed method provides a novel and effective way to select optimal measurement poses for the calibration process using a genetic algorithm and enhances the accuracy of the robot manipulators by constructing a relationship between the effective torque and the compliance errors using a radial basis function. The results of the experimental calibration and validation processes carried out on a YS100 robot show the effectiveness of the proposed method in comparison with the other calibration approaches.

Automated summary

The study introduces a manipulator calibration algorithm aimed at reducing positional errors of an industrial robotic manipulator by selecting optimal measurement poses using a genetic algorithm. It utilizes conventional kinematic calibration and a radial basis function neural network to compensate for compliance errors, showing effectiveness through experimental calibration and validation processes.