Industrial Robot Kinematic Calibration: Generation of Optimal Calibration Configuration Set Based on Cartesian Space Constraints

Selecting an appropriate calibration configuration set is crucial for industrial robot kinematic calibration, as it can mitigate the effects of unmodeled parameters and measurement noise. Existing approaches either rely on laser measurement of a large number of configurations followed by offline selection of an optimal subset—an inherently time-consuming process—or generate optimal configurations solely from joint-space constraints, which are easily affected by the robot’s workspace and the laser tracker’s line-of-sight limitations. In this study, we propose a novel optimization framework that, on the basis of joint-space constraints, incorporates both Cartesian-space constraints and laser-tracker line-of-sight constraints to formulate an optimization model for generating an optimal calibration configuration set. A hybrid WPSO-SQP algorithm—combining the global search capability of inertia-weighted particle swarm optimization (WPSO) with the strong convergence properties of sequential quadratic programming (SQP) for nonlinear constrained problems—is then employed to identify the optimal configuration set. To ensure uninterrupted line-of-sight during measurement, a simulated annealing algorithm is used to optimize the measurement sequence of the selected calibration configuration set. Experimental validation on a six-degree-of-freedom industrial robot demonstrates that the proposed method substantially improves both the efficiency and the accuracy of kinematic calibration.