A Kinematic Parameter Identification Method Based on Auxiliary Variable for Industrial Robots

Accurate kinematic parameter identification is essential for enhancing the positioning accuracy of industrial robots. Traditional methods often struggle with poor convergence when faced with large initial parameter errors or noisy data, primarily due to the nonlinear nature of trigonometric functions involved in modeling joint angles and twist angles. In this paper, we present an auxiliary variable method that addresses these challenges by introducing auxiliary variables for the trigonometric terms. This transformation simplifies the nonlinear optimization problem into a linear one with equality constraints, making it more robust under challenging conditions. We establish the theoretical foundations of this approach and validate its effectiveness through simulations using a six-degree-of-freedom (6-DoF) robot model. The simulation results demonstrate that the auxiliary variable method significantly improves the robustness and reliability of parameter identification, particularly when large parameter errors and noise are present. The proposed method holds the potential for enhancing calibration accuracy in real-world industrial applications.