基于最优位姿集的机器人标定及不确定度评定

为解决因标定位姿点随机选择导致机器人标定结果不稳定、可靠性低问题，研究了基于雅克比矩阵奇异值计算可观测指标的最优位姿点数目及最优位姿集选择算法，建立了机器人MDH模型，采用LM算法对几何参数进行辨识，使用LeicaAT960激光跟踪仪分别在最优位姿集和随机位姿集下对Staubli TX60机器人末端位姿大量实测；在分析研究机器人标定不确定度来源基础上，采用测量不确定指南(GUM)计算几何参数标定的不确定度及蒙特卡洛模拟法对机器人末端位置不确定度进行评估，结果表明，经最优位姿集标定后的机器人不仅在测试点精度有大幅提升，而且几何参数及末端位置平均不确定度约为随机位姿集标定的0.11倍，标定结果稳定可靠，泛化能力强，适于在高精度、大范围作业场合推广应用。

Robot calibration and uncertainty evaluation based on optimal pose set

To solve the problems of unstable and low reliability calibration results due to the random selection of calibrated pose points, the number of optimal pose point and the selection algorithm of optimal pose set based on the singular value of Jacobian matrix to calculate observable indexes are studied. The MDH model is formulated and the Levenberg-Marquardt (LM) algorithm is used to identify geometric parameters. The points of the Staubli TX60 robot end-effector selected based on the optimal and random pose set are measured by the LeicaAT960 laser tracker. On the basis of analyzing and studying the uncertainty contributors of robot calibration, the GUM method is used to calculate the uncertainty of geometric parameter calibration and Monte Carlo simulation method is utilized to evaluate the uncertainty of robot end-effector pose, respectively. Results show that the accuracy of the robot calibrated by the optimal pose set is not only greatly improved at the test points, but also the mean uncertainty of geometric parameters and end-effector is about 0. 11 times of that calibrated by the random pose set. The calibration results are stable and reliable, and the generalization ability is strong, which are suitable for popularization and application in high-precision and large-scale operation situations.