半对称三平移Delta-CU并联机构运动误差分析与标定

对提出的一种半对称三平移Delta-CU并联机器人机构进行误差建模和实验分析。在规划执行末端运动轨迹的基础上,采用外部直接标定和修正系统输入的方法对机构的运动学误差进行补偿。在外部直接标定的过程中,为降低系数矩阵中的随机测量误差对执行末端坐标精度的影响,利用整体最小二乘法求解坐标变换参数;以误差数据为样本,通过模糊神经网络模型进行训练,并将训练好的模糊神经网络模型用于Delta-CU并联机器人机构的误差值预测。实验表明,模糊神经网络模型能够对Delta-CU并联机器人机构误差进行精准的预测,有利于提高Delta-CU并联机器人机构的补偿精度,可为Delta-CU并联机器人机构误差补偿提供参照。补偿后其绝对位置精度由1.187 mm提高到0.4 mm,重复位置精度由0.037 mm提高到0.018 mm。

Error Modeling Analysis and Calibration of Semi-symmetrical Three-translation Delta-CU Parallel Mechanism

The error modeling and experimental analysis were discussed for a semi-symmetrical 3-translational Delta-CU parallel mechanism,which was proposed by the author's team.On the basis of the planning executive terminal trajectory,kinematics error of the actuator was compensated by adopting external direct calibration and correcting the system input.In the process of direct external calibration,the global least square method was used to solve the coordinate transformation parameters,which could reduce the impact of random measurement errors carried in the coefficient matrix on the precision of coordinate data at the execution end,thus the motion error data was calculated and the coordinate data of the execution end was obtained.With the error data as the sample,the fuzzy neural network model was trained,and the trained fuzzy neural network model was used to predict the error value of Delta-CU parallel robot mechanism.Experimental results showed that the accuracy of fuzzy neural network model prediction was exactly accurate,which would improve the compensation accuracy and applicate in practical Delta-CU parallel mechanism,and provide a practical reference for thermal error compensation of Delta-CU parallel mechanism.Absolute position accuracy was improved from 1.187 mm to 0.4 mm and the repeat position accuracy was improved from 0.037 mm to 0.018 mm.The error modeling and analysis method described was reliable and effective,with good compensation effect and obvious accuracy improvement.