Industrial Robot Calibration Using a DH-LPOE Framework

Abstract

Kinematic calibration is essential for improving the absolute positioning accuracy of industrial manipulators. Although Product of Exponentials (POE)-based methods have become the academic standard because of their singularity-free representation of joint axes, their adoption in industry remains limited. Most commercial controllers are architected around Denavit-Hartenberg (DH) parameters, so projecting identified 6-degree-of-freedom (DoF) screw errors onto the constrained 4-DoF DH model after optimization is an ill-posed operation. Conventional solvers are also prone to overfitting the stochastic measurement noise produced by low-cost industrial sensors. This letter addresses both issues with a framework termed DH-LPOE. Using the dyad kinematics of the Local POE (LPOE) formulation, an analytical Jacobian is derived that embeds the DH constraints directly in the identification loop, and the measurement noise is parameterized as an independent decision variable within a regularized objective function. The resulting corrections are therefore physically realizable and directly compatible with commercial controllers, while parameter overfitting is substantially reduced. Simulations and physical experiments on a serial manipulator under macroscopic measurement noise show that DH-LPOE improves robustness and data efficiency, reducing workspace error with a compact dataset of only 20 postures.