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.

精度是影响电缆驱动并联机器人（CDPRs）研究和工业应用的重要性能指标。CDPR的误差来源包括几何参数（GPs）和非几何参数（NGPs）。通常，GP 可由外部测量设备校准，其位置取决于坐标系参数。为提高标定精度和鲁棒性，提出了一种迭代标定方法对坐标系参数和GPs进行标定，并证明了渐近收敛性。此外，考虑到 NGPs 的紧耦合和非线性，我们设计了一个人工神经网络来补偿由 NGPs 引起的残余位置误差。基于层次遗传算法，开发了一种同步优化算法，以提高对未知轨迹残余位置误差的近似精度和泛化能力。使用理论初始参数，在 3-DOFs CDPR 上进行了 GP 校准和 NGP 误差补偿实验。最后，末端执行器的平均位置误差降低到 1.3 mm，最大误差为 1.9 mm。