The Accuracy Analysis of Tool Center Point Calibration via Condition Number and Minimum Eigenvalue.

The accuracy of TCP (tool center point) calibration is vital to the operation accuracy of the robot. In this paper, we analyzed the accuracy analysis of TCP calibration of the regression matrix via condition number and minimum eigenvalue. It is theoretically proved that a regression matrix with a large condition number and a small eigenvalue will aggravate the influence of the observation error of the measurement point on TCP calibration accuracy and increase the TCP calibration error. To verify our analysis, simulation calibration experiments were carried out and statistical analysis was carried out on the calibration results. The result shows that the mathematical expectation and the variance of calibration error at minimum, median, and maximum matrix CA condition number are 1.36mm (±0.60mm), 1.57mm (± 0.75mm) and 2.94mm (±2.26mm) respectively, and the mathematical expectation and the variance of calibration error at minimum, median, and maximum eigenvalue are 3.33mm (± 2.32mm), 1.54mm (±0.72mm) and 1.16mm (± 0.49mm), respectively.