Joint Stiffness Identification and Deformation Compensation of Serial Robots Based on Dual Quaternion Algebra

Abstract

As the application of industrial robots is limited by low stiffness that causes low precision, a joint stiffness identification algorithm for serial robots is presented. In addition, a deformation compensation algorithm is proposed for the accuracy improvement. Both of these algorithms are formulated by dual quaternion algebra, which offers a compact, efficient, and singularity-free way in robot analysis. The joint stiffness identification algorithm is derived from stiffness modeling, which is the combination of the principle of virtual work and dual quaternion algebra. To validate the effectiveness of the proposed identification algorithm and deformation compensation algorithm, an experiment was conducted on a dual arm industrial robot SDA5F. The robot performed a drilling operation during the experiment, and the forces and torques that acted on the end-effector (EE) of both arms were measured in order to apply the deformation compensation algorithm. The results of the experiment show that the proposed identification algorithm is able to identify the joint stiffness parameters of serial industrial robots, and the deformation compensation algorithm can improve the accuracy of the position and orientation of the EE. Furthermore, the performance of the forces and torques that acted on the EE during the operation were improved as well.

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“…Ye et al [15] focus on the contour errors of robotic machining, and a machining performance index is proposed to optimize the task-dependent workpiece placement. Li et al [16] proposed a joint stiffness identification algorithm for serial robot, and a deformation compensation algorithm for the accuracy improvement. A drilling operation experiment is performed to identify the joint stiffness identification algorithm and the deformation compensation algorithm.…”

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