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.

/pdf/a-tutorial-on-visual-servo-control-5cy6rje63g.pdf

A tutorial on visual servo control

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

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

https://hal.inria.fr/inria-00350638/document

Visual servo control. II. Advanced approaches [Tutorial]

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.

https://hal.inria.fr/inria-00352542/document

2 1/2 D visual servoing

This paper presents a complete design methodology for Cartesian position based visual servo control for robots with a single camera mounted at the end-effector. Position based visual servo control requires the explicit calculation of the relative position and orientation (POSE) of the workpiece object with respect to the camera. This is accomplished using image plane measurements of a number of known feature points on the object, and then applying an extended Kalman filter to obtain a recursive solution of the photogrammetric equations, and to properly combine redundant measurements. The control is then designed by specifying the desired trajectories with respect to the object and forming the control error in the end-effector frame. The implementation using a distributed computer architecture is described. An experimental system has been built and used to evaluate the performance of the POSE estimation and the position based visual servo control. Several results for relative trajectory control and target tracking are presented. Results of the experiments showing the effect of loss of some of the redundant features are also presented.

Relative end-effector control using Cartesian position based visual servoing

An attempt to achieve a high level of interaction between a real-time vision system capable of tracking moving objects in 3-D and a robot arm with gripper that can be used to pick up a moving object is described. The interplay of hand-eye coordination in dynamic grasping tasks such as grasping of parts on a moving conveyor system, assembly of articulated parts, or for grasping from a mobile robotic system is explored. The goal is to build an integrated sensing and actuation system that can operate in dynamic as opposed to static environments. The system built addresses three distinct problems in using robotic hand-eye coordination for grasping moving objects: fast computation of 3-D motion parameters from vision, predictive control of a moving robotic arm to track a moving object, and interception and grasping. The system operates at approximately human arm movement rates. Experimental results in which a moving model train is tracked, stably grasped, and picked up by the system are presented. The algorithms developed to relate sensing to actuation are quite general and applicable to a variety of complex robotic tasks. >

/pdf/automated-tracking-and-grasping-of-a-moving-object-with-a-4gi8l4udod.pdf

Automated tracking and grasping of a moving object with a robotic hand-eye system

A real-time tracking algorithm in conjunction with a predictive filter to allow real-time visual servoing of a robotic arm that is tracking a moving object is described. The system consists of two calibrated (but unregistered) cameras that provide images to a real-time, pipeline-parallel optic-flow algorithm that can robustly compute optic-flow and calculate the 3-D position of a moving object at approximately 5-Hz rates. These 3-D positions of the moving object serve as input to a predictive kinematic control algorithm that uses an alpha - beta - gamma filter to update the position of a robotic arm tracking the moving object. Experimental results are presented for the tracking of a moving model train in a variety of different trajectories. >

/pdf/real-time-visual-servoing-1rehm294y9.pdf

Real-time visual servoing

This paper proposes a path-planning method for mobile robots in the presence of uncertainty. We analyze environment and control uncertainty and propose methods for incorporating each of them into the planning algorithm. We model the environment using the pyramid structure that encodes the information on occupancy probabilities for each pixel as well as the partial information on conditional probabilities among different pixels. This structure allows for efficient and accurate computation of collision probabilities in the presence of environment uncertainty. The control uncertainty is mainly characterized by its expansion in space and time and is accordingly modeled by a stochastic differential equation that mathematically captures this phenomenon. Models that we develop are inevitably approximate but experiments confirm that they can be used as a reasonable model for motion planning. We have conducted a series of experiments on the mobile platform and some of these results are presented. >

/pdf/probability-driven-motion-planning-for-mobile-robots-1gyl4rjn0o.pdf

Probability-driven motion planning for mobile robots

The structure of SYM, a program package for manipulator modeling, control law synthesis, and simulation, is described SYM's research and educational aims are emphasized The control law synthesis in symbolic form and the system simulation are discussed in detail Several examples of SYM outputs which depict the main steps in the manipulator control system creation process, namely, manipulator structure as a 3-D scheme, control law definition form, and system simulation results as 2-D plots are presented >

SYM-program environment for manipulator modeling, control and simulation

This paper describes the structure of the program package for manipulator modeling, control law synthesis and simulation SYM. While the underlying algorithms have been explained elsewhere, this paper puts emphasis on SYM as a program environment with both research and educational purposes. The control law synthesis in symbolic form, as well as system simulation, are given in more details since those features have been introduced into SYM recently. As an illustration we present few instances of SYM outputs which depict main steps in manipulator control system creation process: manipulator structure as a 3D scheme, control law definition form and system simulation results as 2D plots.

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Automated tracking and grasping of a moving object with a robotic hand-eye system

Real-time visual servoing

Probability-driven motion planning for mobile robots

SYM-program environment for manipulator modeling, control and simulation

SYM-program environment for manipulator modeling, control and simulation