H∞滤波器视觉伺服系统英文文献和中文翻译

Abstract This paper is concerned with the performance evaluation of the H= filter for the 2-D visual servoing of a moving target。 The efficacy of the H= filter is validated experimentally in comparison with the Kalman filter, using an eye-in-hand coordinated robot manipulator system。 The H= filter can provide a powerful tool for solving vision-based control problems in robotics, just as the Kalman filter can。 ( 1998 Elsevier Science ¸td。 All rights reserved。78783

Keywords: Robust estimation; visual motion; H= control; robust control; experimental evaluation

1。 Introduction

By integrating control and vision, a robotic system canmove appropriately in a dynamically changing workingspace。 Tracking and grasping of a moving object bya robot manipulator is a typical example of this categoryof problem in real situations。 The combination of robotcontrol with computer vision could become extremelyimportant, when dealing with a robotic system workingin uncertain and dynamic environments。 Recent researchefforts in this direction have been well covered collectedin (Hutchinson et al。, 1996) (see also the referencestherein)。

In the visual servoing in particular, a powerful estimation algorithm taken from systems and control theoryplays an important role, since dynamic image scenes haveto be processed。 The Kalman filter is a popular algorithm, and it has been frequently utilized, not only in thevisual servoing (Hutchinson et al。, 1996) but also in theimplementation of computer vision (Matthies et al。,1989)。 At the present time, the Kalman filter is acceptedas a basic, standard tool for active/dynamic vision (Blakeand Yuille, 1992)。

The combination of Linear Quadratic (LQ) controlwith the Kalman filter gives the well-known LinearQuadratic Gaussian (LQG) theory, which was mainlydeveloped during the 60s and 70s。 Since the 80s, however,a great move has occurred, from the LQG theory to theH= theory (Doyle et al。, 1989)。 The H= theory providesthe capability to handle model uncertainties in a morepractical way。 While the LQG theory considers theeffects of uncertainty in a stochastic framework, theH= theory treats them in a functional analytic framework。 Further, it gives a certain min-max optimal solution to deal with the disturbances caused by uncertainties

(Basar and Bernhard, 1991)。 It has been shown that theH= theory can be regarded as a natural generalization ofthe LQG theory (Doyle et al。, 1989)。

Recently, the H= theory has been successfully appliedto visual feedback control (Ogura et al。, 1994), where theemphasis was on the control aspect。 Although the corresponding estimation theory in an H= setting has beendeveloped (Nagpal and Khargonekar, 1991; Shaked andTheodor, 1992), not much work has been done on the useof the H= filter in robotics and/or vision research (Fujitaet al。, 1993, 1995)。 The superiority of the H= filter overthe existing estimation algorithms is theoretically convincing in some respects, since the model uncertaintiescan be handled more adequately (Nagpal and Khargonekar, 1991; Shaked and Theodor, 1992)。 Hence, anexperimental validation of its efficacy presents quitea challenge in the present situation。

关键词：稳定估计；视觉运动；H∞控制；稳定控制；实验评价。

1。介绍

通过集成控制和视觉，机器人系统可以在动态变化的工作空间适当移动。真实情况下机器人机械手跟踪和抓取运动物体是这类问题的典型例子。机器人控制与计算机视觉相结合，在处理机器人系统在不确定的和动态的环境中工作非常重要。最近的研究工作，在这个方向上得到了很好的覆盖收集（哈钦森等人，1996）（参见参考文献中）。 H∞滤波器视觉伺服系统英文文献和中文翻译:http://www.youerw.com/fanyi/lunwen_90884.html