An Innovative Pose Decoupling Calibration Method for Five-Axis Hybrid Mechanism Through Rotation Vector Equivalence

Geometric error calibration is one effective way to improve the absolute positioning accuracy of multidegree-of-freedom motion system. Due to differences in dimension, magnitudes, and measuring precision between position and orientation, the existing calibration methods may minimize the overall pose error but cannot guarantee that each degree-of-freedom error optimally converges in its own space. This makes it challenging for the current method to achieve a complete and precise error compensation for pose error. Addressing this issue, this article proposes an equivalent rotation vector (ERV)-based pose decoupling calibration method and thus transforms the rigid body transformation motion model (RTMM) into a pose-separation motion model (PSMM) to achieve an independent representation of orientation and position. With the PSMM, we can calibrate position and orientation separately without coupling interference and can thus make the position and orientation error converge in their own measurement spaces. The proposed method was experimentally validated on a five-axis hybrid mechanism. Compared to the current variable scaling method, our decoupling calibration method further reduces position and orientation errors by 66.88% and 51.99%, respectively. The proposed method can solve the nonconverging ill-identification problem of current methods, significantly improve the calibration accuracy of the end-effector pose, and achieve an optimal error compensation for the hybrid mechanism.