A Gear Backlash Identification and Compensation Method to Enhance Industrial Robot Repeatability

Industrial robots are extensively utilized in machinery manufacturing owing to their multiple degrees of freedom (DoF) and inherent flexibility. However, backlash not only impairs the bidirectional repeatability and multi-directional repeatability of the end-effector but also renders its identification and compensation more challenging in comparison to those associated with geometric errors. Existing backlash identification and compensation methods overlook the principle of backlash error transmission, a limitation that severely hinders improvements in both the bidirectional repeatability and multi-directional repeatability of the robot end-effector. In contrast to existing studies, this article presents the first systematic discussion on the correlation between backlash and both bidirectional repeatability and multi-directional repeatability, and proposes a pipeline including modeling, identification and compensation methods. Based on the principle of backlash error transmission, a mathematical model incorporating the reduction ratio correction coefficient, backlash, and joint rotation direction is developed and integrated into a robotic kinematic error model that includes higher-order joint-dependent error terms. Given that the pose deviation between adjacent coordinate systems is a linear function incorporating backlash and kinematic errors, a Taylor expansion is performed on this function with higher-order error terms omitted, and the iterative reweighted least squares (IRLS) algorithm is adopted to identify both backlash and kinematic parameter errors. A compensation method that accounts for both backlash and the compensation direction is also proposed, in which the compensation direction is determined by the joint velocity prior to the joint reaching the target position. Experiments were conducted to compare the single-axis rotation identification and compensation approach implemented on a 6-DoF robot with the innovative method proposed in this article. The experimental results indicate that the traditional single-axis method requires repeated single-axis movements for each joint, leading to low efficiency in backlash identification and compensation, and it fails to account for the impact of backlash on multi-directional repeatability. In contrast, the proposed method rapidly identifies backlash in each joint and improves bidirectional repeatability and multi-directional repeatability by 47.97% and 53.44%, respectively.