Calibration of geometric parameters and error compensation of non for cable-driven robots

Accuracy is an important performance indicator that affects the research and industrial application of cable-driven parallel robots (CDPRs). The error sources of CDPRs include geometric parameters (GPs) and non-geometric parameters (NGPs). Typically, GPs can be calibrated by external measurement devices whose position is dependent on coordinate system parameters. To improve the calibration accuracy and robustness, an iterative calibration method is proposed to calibrate the coordinate system parameters and GPs, and the asymptotic convergence is proven. Moreover, considering the tight coupling and non-linearity of NGPs, we design an artificial neural network to compensate for the residual position errors caused by NGPs. Based on the hierarchical genetic algorithm, a synchronization optimization algorithm is developed to improve the approximate accuracy and generalization to residual position errors of unknown trajectories. With theoretical initial parameters, experiments for the calibration of GPs and error compensation of NGPs were performed on a 3-DOFs CDPR. Finally, the average position error of the end-effector is reduced to 1.3 mm and the maximum error is 1.9 mm.

Automated summary

This paper proposes an iterative calibration method to improve the accuracy and robustness of CDPRs. By calibrating geometric parameters and designing an artificial neural network to compensate for residual position errors caused by non-geometric parameters, the experimental results show that the average position error is reduced to 1.3 mm.