中文摘要：

提出了一种改进的基于模糊自适应Kalman滤波的动态图像雅可比矩阵辨识方法。该方法在机器人参数和滤波参数未知而且视觉成像模型动态变化的情况下,通过模糊逻辑自适应控制器在线监测滤波残差均值和残差协方差误差,对过程噪声参数Q和量测噪声参数R进行自适应调节,实现了未知环境下动态图像雅可比矩阵的稳定辨识。通过微装配机械手运动实验验证了该方法的有效性。

英文摘要：

An improved method for estimation of dynamic image Jacobian matrix based on fuzzy adaptive Kalman filtering was proposed. When using the method, the robot parameters and filter parameters are allowed to be unknown during estimation and the vision imaging model is allowed to vary dynamically. The fuzzy logic adaptive controller performs the on-line monitoring of the mean value and the covarianee error of the filtering residuals, and it adjusts the Kalman noise covariance matrices, Q and R, by fuzzy tuning rules. It greatly improved the Jaeobian estimation adaptability in unkonw dynamic applications. The micromanipulator motion experiment was carried out and the results demonstrated the effective performance of the proposed approach.