Visual Servoing in Robotics

Visual servoing is a well-known approach to guide robots using visual information. Image processing, robotics, and control theory are combined in order to control the motion of a robot depending on the visual information extracted from the images captured by one or several cameras. With respect to vision issues, a number of issues are currently being addressed by ongoing research, such as the use of different types of image features (or different types of cameras such as RGBD cameras), image processing at high velocity, and convergence properties. As shown in this book, the use of new control schemes allows the system to behave more robustly, efficiently, or compliantly, with fewer delays. Related issues such as optimal and robust approaches, direct control, path tracking, or sensor fusion are also addressed. Additionally, we can currently find visual servoing systems being applied in a number of different domains. This book considers various aspects of visual servoing systems, such as the design of new strategies for their application to parallel robots, mobile manipulators, teleoperation, and the application of this type of control system in new areas

Survey of Visual and Force/Tactile Control of Robots for Physical Interaction in Spain

Sensors provide robotic systems with the information required to perceive the changes that happen in unstructured environments and modify their actions accordingly. The robotic controllers which process and analyze this sensory information are usually based on three types of sensors (visual, force/torque and tactile) which identify the most widespread robotic control strategies: visual servoing control, force control and tactile control. This paper presents a detailed review on the sensor architectures, algorithmic techniques and applications which have been developed by Spanish researchers in order to implement these mono-sensor and multi-sensor controllers which combine several sensors

Visual Servoing

International audienceThis chapter introduces visual servo control, using computer vision data in the servo loop to control the motion of a robot. We first describe the basic techniques that are by now well established in the field. We give a general overview of the formulation of the visual servo control problem, and describe the two archetypal visual servo control schemes: image-based and pose-based visual servo control. We then discuss performance and stability issues that pertain to these two schemes, motivating advanced techniques. Of the many advanced techniques that have been developed , we discuss 2.5-D, hybrid, partitioned, and switched approaches. Having covered a variety of control schemes, we deal with target tracking and controlling motion directly in the joint space and extensions to under-actuated ground and aerial robots. We conclude by describing applications of visual ser-voing in robotics

Visual servoing of a five-bar linkage mechanism /

This document is the written product of the graduation project developed: Visual Servoing of a Five-bar Linkage Mechanism. This project means to venture into the fields of a method of control, with visual feedback, known as Visual Servoing. The contents of this document show a summary of all the theory taken into account to realize the project. They also shows how other people have approached this method. These pages present the project establishing its aims, the importance of its realization, a detailed description of how it was carried out - including experiments and obstacles, - and the results obtained. This document also informs how is this work of use and what can be done from it. In the same way, here are consigned the books, articles, and works consulted in the way, which in their own pages provide a large quantity of references and information.Incluye referencias bibliográfica

Virtual Environment for Development of Visual Servoing Control Algorithms

Our problem considered was whether a virtual environment could be used for development of visual servoing control algorithms. We have used a virtual environment for the comparison of several kinds of controllers. The virtual environment is done in Java, and it consists of two industrial robots, IRB 2000 and IRB 6, a camera stereo system with two cameras mounted on the end-effector of the IRB 6, and one rolling ball and one bar. The experiment consists of tracking and grasping the ball using the different controllers. The robot IRB 2000 should grasp the rolling ball. The control of the robot is done in Matlab. We have three controllers. These controllers are function of the difference between the ball and the gripper. First, we use P-controller with a proportional gain. Second, the image-based Jacobian control is used but this controller needs an improvement because the robot tracks the ball with a little delay, then we use this controller with feedforward. The robot grasps the ball when the error between the ball and the gripper is less than one tolerance. In these two controllers, the depth is calculated with the two cameras (stereovision), therefore cameras need to be calibrated. Third, the hybrid controller is used. It is a mix of image-based and position-based controller. We use X and Y in Image space and Z in Cartesian space. Now, the 3D reconstruction is done from motion. It means we do not need calibrated cameras and the depth is calculated with adaptive control techniques. This adaptive control is used for recovering on-line the velocity of the ball. When the estimation of the ball is stable, the robot starts tracking the ball