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基于平方根无迹卡尔曼滤波的机器人无标定视觉伺服

发表时间:2018-02-28  浏览量:887  下载量:166
全部作者: 樊阳立,孙炜
作者单位: 湖南大学电气与信息工程学院
摘 要: 针对基于图像雅可比矩阵的机器人无标定视觉伺服问题,提出一种基于平方根无迹卡尔曼滤波(square-root un-scented Kalman filter,SR-UKF)的图像雅可比矩阵在线估计方法。该方法以总图像雅可比矩阵的元素作为系统状态,将问题转变为对系统状态的估计,再引入能处理非线性问题的SR-UKF对系统状态进行估计,从而实现对总图像雅可比矩阵的在线估计,避免了复杂的系统标定过程。以二自由度机器人跟踪运动目标为视觉伺服任务,将新提出的方法与基于卡尔曼滤波(Kalman filter,KF)和无迹卡尔曼滤波(unscented Kalman filter,UKF)的估计方法进行实验比较。仿真实验结果表明,所提出的图像雅可比矩阵在线估计方法具有更高的估计精度和更强的鲁棒性。
关 键 词: 自动控制技术;机器人;无标定视觉伺服;图像雅可比矩阵;平方根无迹卡尔曼滤波
Title: Uncalibrated visual servoing for robots based on square-root unscented Kalman filter
Author: FAN Yangli, SUN Wei
Organization: College of Electrical and Information Engineering, Hunan University
Abstract: Considering the problem of robot uncalibrated visual servoing based on an image Jacobian matrix, a novel on-line estimation method of image Jacobian matrix based on the square-root unscented Kalman filter (SR-UKF) is proposed. In this method, a state vector is formed from the elements of a total image Jacobian matrix, and the problem is converted into one of system state estimations, then a SR-UKF suitable for nonlinear systems is utilized for estimation of system state, thus the on-line estimation of total image Jacobian matrix is realized and the complex system calibration process can be avoided. The proposed method compared with the ones based on Kalman filter (KF) and unscented Kalman filter (UKF) are tested to track a moving target on a two degree-of-freedom robot visual servoing system. Simulation results indicate that the proposed method outperforms other two methods in estimation accuracy and robustness.
Key words: autocontrol technology; robot; uncalibrated visual servoing; image Jacobian matrix; square-root unscented Kalman filter
发表期数: 2018年2月第4期
引用格式: 樊阳立,孙炜. 基于平方根无迹卡尔曼滤波的机器人无标定视觉伺服[J]. 中国科技论文在线精品论文,2018,11(4):421-430.
 
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