Visual servoing

About: Visual servoing is a research topic. Over the lifetime, 5010 publications have been published within this topic receiving 89266 citations.

A tutorial on visual servo control

Abstract: This article provides a tutorial introduction to visual servo control of robotic manipulators. Since the topic spans many disciplines our goal is limited to providing a basic conceptual framework. We begin by reviewing the prerequisite topics from robotics and computer vision, including a brief review of coordinate transformations, velocity representation, and a description of the geometric aspects of the image formation process. We then present a taxonomy of visual servo control systems. The two major classes of systems, position-based and image-based systems, are then discussed in detail. Since any visual servo system must be capable of tracking image features in a sequence of images, we also include an overview of feature-based and correlation-based methods for tracking. We conclude the tutorial with a number of observations on the current directions of the research field of visual servo control.

Visual servo control. I. Basic approaches

Abstract: This paper is the first of a two-part series on the topic of visual servo control using computer vision data in the servo loop to control the motion of a robot. In this paper, we describe the basic techniques that are by now well established in the field. We first give a general overview of the formulation of the visual servo control problem. We then describe the two archetypal visual servo control schemes: image-based and position-based visual servo control. Finally, we discuss performance and stability issues that pertain to these two schemes, motivating the second article in the series, in which we consider advanced techniques

A new approach to visual servoing in robotics

Abstract: Vision-based control in robotics based on considering a vision system as a specific sensor dedicated to a task and included in a control servo loop is described. Once the necessary modeling stage is performed, the framework becomes one of automatic control, and stability and robustness questions arise. State-of-the-art visual servoing is reviewed, and the basic concepts for modeling the concerned interactions are given. The interaction screw is thus defined in a general way, and the application to images follows. Starting from the concept of task function, the general framework of the control is described, and stability results are recalled. The concept of the hybrid task is presented and then applied to visual sensors. Simulation and experimental results are presented, and guidelines for future work are drawn in the conclusion. >

Visual servo control. II. Advanced approaches [Tutorial]

Abstract: This article is the second of a two-part tutorial on visual servo control. In this tutorial, we have only considered velocity controllers. It is convenient for most of classical robot arms. However, the dynamics of the robot must of course be taken into account for high speed task, or when we deal with mobile nonholonomic or underactuated robots. As for the sensor, geometrical features coming from a classical perspective camera is considered. Features related to the image motion or coming from other vision sensors necessitate to revisit the modeling issues to select adequate visual features. Finally, fusing visual features with data coming from other sensors at the level of the control scheme will allow to address new research topics

2 1/2 D visual servoing

Abstract: We propose an approach to vision-based robot control, called 2 1/2 D visual servoing, which avoids the respective drawbacks of classical position-based and image-based visual servoing. Contrary to the position-based visual servoing, our scheme does not need any geometric three-dimensional model of the object. Furthermore and contrary to image-based visual servoing, our approach ensures the convergence of the control law in the whole task space. 2 1/2 D visual servoing is based on the estimation of the partial camera displacement from the current to the desired camera poses at each iteration of the control law. Visual features and data extracted from the partial displacement allow us to design a decoupled control law controlling the six camera DOFs. The robustness of our visual servoing scheme with respect to camera calibration errors is also analyzed: the necessary and sufficient conditions for local asymptotic stability are easily obtained. Then, due to the simple structure of the system, sufficient conditions for global asymptotic stability are established. Finally, experimental results with an eye-in-hand robotic system confirm the improvement in the stability and convergence domain of the 2 1/2 D visual servoing with respect to classical position-based and image-based visual servoing.