Implementing AI models to correct static positioning and setup errors in an accurate industrial robot
This study presents a data-driven approach to improve the absolute positional accuracy of a MABI MAX-100-2.25-P industrial robot. To quantify positioning errors, the robot was programmed to move through a dense grid of points with spacing of 25 mm in two planes above and across a working table. A training dataset was acquired by pulling commanded positions from the robot controller and pairing them with tool centre point values measured by a Leica AT960-LR laser tracker. The end effector orientation was fixed programmatically throughout the experiment.Four predictive models were trained on the dataset to learn the error patterns between the commanded and measured tool centre point positions. These models were Linear Regression, Random Forest, Gradient Boosting, and Artificial Neural Networks. Whilst training and validation metrics initially indicated that Random Forest and Gradient Boosting offered the lowest mean squared errors (as low as 0.0 mm2), real-world testing revealed significant discrepancies in model generalisation. The models were evaluated on three distinct test sets: hold-out grid points, interpolated cell midpoints, and randomly generated points. In the tests, Linear Regression delivered the most robust performance, achieving Euclidean error reductions of up to 85% across all sets. In contrast, the tree-based models generalised poorly when given unseen or interpolated locations, sometimes even amplifying positional errors by over 3000%. Artificial Neural Network models demonstrated moderate generalisation capability, performing better than tree-based models on non-grid data but not surpassing Linear Regression. In the most widely applicable test, random points, Linear Regression achieved an average Euclidean error of 0.12 mm which corresponds to a repeatability to accuracy ratio of 1:2.4, an improvement on the 1:3 ratio from the robot datasheet.