A method for calibrating robotic kinematic parameters based on a multi-error source model and an optimized measurement pose set

PurposeThe purpose of this study is to improve the accuracy of industrial robot kinematic parameter identification and position accuracy by solving the problem of insufficient consideration of error sources in the kinematic parameter identification model and optimizing the selection of measurement pose set.Design/methodology/approachIn this study, a kinematic calibration method for industrial robots considering multiple error sources is proposed. Based on the Modified Denavit Hartenberg (MD-H) model, a robot kinematics identification model including joint reduction ratio error, target ball installation error and coordinate system transformation error is established. Taking the optimal observability index O1 and the minimum flexible deformation as the optimization objectives, a measurement pose set optimization method based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to obtain a measurement pose set with higher identification accuracy.FindingsThrough experiments conducted with the Nantong Zhenkang ZK1400-6 robot as the test subject, the kinematic parameters identified by the optimized measurement pose set are more accurate than the randomly selected measurement pose set, and the positioning accuracy of the robot is improved from 2.11 to 0.31 mm, an increase of 85.3%.Originality/valueThis study introduces a position error model that comprehensively accounts for the error sources causing positioning inaccuracies. Building on this foundation, a novel flexible deformation index is proposed to quantify the flexible deformation in the measurement pose set, thereby reducing the impact of such deformation on the position error in the model. To the best of the authors' knowledge, for the first time, this study presents an optimization method for the measurement pose set based on NSGA-II, using the flexible deformation index and observability index as objectives for multi-objective optimization, simultaneously optimizing the pose error and Jacobian matrix in the error model.