Selecting Optimal Measurement Poses for Kinematic Calibration of Industrial Robots

Abstract

The robustness of robot calibration regarding the measurement noise is sensitive to the measurement poses serving as constraints on the parameters to be estimated. This paper presents a pose selection algorithm allowing one to select a given number of optimal poses out of a large set of previously measured poses, which is derived from the DETMAX algorithm with the improvements benefiting from the randomized search technique to avoid local convergence. The benefit of selecting optimal measurement poses is validated through both simulation and experimentation of calibration of a 6-dof serial robot manipulator (Hyundai robot YS100). The results show that the robot calibration with optimal measurement poses yields the global (unbiased) kinematic parameters. Thus, the robot positioning accuracy is enhanced throughout the workspace. Moreover, the experimental results indicate that the number of optimal measurement poses is as important as selecting optimal measurement poses for improving the robot calibration accuracy.