Kinematic calibration of a 5-axis parallel machining robot based on dimensionless error mapping matrix

Accuracy problem is one of the most challenging issues for the application of parallel robots in manufacturing industry, and kinematic calibration is a feasible approach to solve it. Although lots of researches have brought up a diversity of calibration methods, there are still rooms for the improvement of the accuracy, efficiency and robustness of these calibration effects. In this paper, an improved method for kinematic calibration of a 5-axis parallel machining robot is proposed, which includes a new forward kinematic solution (FKS) based on dual quaternion and a modified error modeling process leading to dimensionless error mapping matrixes (EMMs). On this basis, an iterative identification procedure is schemed, and the kinematics and identification simulations are carried out. The kinematics simulation results show that the proposed FKS has wider convergence range and faster computation speed than Levenberg-Marquardt algorithm, while the identification simulation results show that the residual pose errors with the proposed dimensionless EMMs are lower than that with the conventional EMM in various units. Additionally, the procedure of the full pose measurement with a laser tracker and an auxiliary tool is introduced, and thereby the contrast experiments of kinematic calibration on the prototype are conducted. The experiment results indicate that the residual position and orientation errors based on the dimensionless EMM decrease by 97.67% and 86.85% of the original values, respectively, at least, and by 76.77% and 38.65% of that based on the conventional EMM, respectively, at most. Consequently, it is further confirmed that the proposed calibration method is effective in enhancing the identification accuracy of the geometric errors and improving the positioning accuracy of the studied parallel robot.

Automated summary

An improved method for kinematic calibration of a 5-axis parallel machining robot is proposed, which has been proven to have superior performance in kinematics and identification simulations, and validated through experiments.