Fuzzy Adaptive Kalman Filtering based Estimation of Image Jacobian for Uncalibrated Visual Servoing

Abstract

An image Jacobian estimate method for uncalibrated visual servoing is proposed in this paper. With less or no prior knowledge to robotic parameters and filtering statistics, the method employs a Kalman filter to provide an optimal estimate of the Jacobian elements and fuzzy logic controllers to adjust the Kalman noise covariance matrices Q and R adaptively. The adaptations are performed based on a matching technique of the filter residual mean value and covariance error. It greatly improves the Jacobian estimate adaptability to unknown dynamic imaging applications. A microscopic image Jacobian model has been developed for the 4 degree-of-freedom micromanipulator in our microassembly system. Its Jacobian estimate results demonstrate a good performance of the proposed method