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.

对于并行机器人在制造业中的应用而言，精度问题是最具挑战性的问题之一，而运动学标定是解决该问题的一种可行方法。尽管许多研究提出了多种校准方法，但仍有改善这些校准效果的准确性，效率和鲁棒性的空间。本文提出了一种改进的五轴并联加工机器人运动学标定方法，该方法包括基于双四元数的新正向运动学解决方案（FKS）和改进的误差建模过程，从而产生了无量纲的误差映射矩阵（EMM）。 。在此基础上，设计了一种迭代识别程序，并进行了运动学和识别仿真。莱文贝格-马夸特该算法的识别仿真结果表明，所提出的无量纲EMM在各个单元中的残留姿态误差要低于常规EMM。另外，介绍了使用激光跟踪仪和辅助工具进行全姿势测量的过程，从而对原型进行了运动学校准的对比实验。实验结果表明，基于无量纲EMM的残余位置和方向误差至少分别降低了原始值的97.67％和86.85％，分别比传统EMM降低了76.77％和38.65％，最多。所以，