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

This paper attempts to present a comprehensive summary of research results in the use of visual information to control robot manipulators and related mechanisms. An extensive bibliography is provided which also includes important papers from the elemental disciplines upon which visual servoing is based. The research results are discussed in terms of historical context, common-ality of function, algorithmic approach and method of implementation.

Visual control of robot manipulators – a review

From the Publisher:
This book is about the application of high-speed machine vision for close-loop position control, or visual servoing, of a robot manipulator. It provides a comprehensive coverage of all aspects of the visual servoing problem: robotics, vision, control, technology and implementation issues. While much of the discussion is quite general the experimental work described is based on the use of a high-speed binary vision system with a monocular "eye-in-hand" camera.

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Visual Control of Robots: High-Performance Visual Servoing

Worldwide demand for robotic aircraft such as unmanned aerial vehicles (UAVs) and micro aerial vehicles (MAVs) is surging. Not only military but especially civil applications are being developed at a rapid pace. Unmanned vehicles offer major advantages when used for aerial surveillance, reconnaissance, and inspection in complex and inhospitable environments. UAVs are better suited for dirty or dangerous missions than manned aircraft and are more cost-effective. UAVs can operate in contaminated environments, for example, and at altitudes both lower and higher than those typically traversed by manned aircraft. Many technological, economic, and political factors have encouraged the development and operation of UAVs. New sensors, microprocessors, and propulsion systems are smaller, lighter, and more capable, leading to levels of endurance, efficiency, and autonomy that exceed human capacities. Comprising the latest research, this book describes step by step the development of small or miniature unmanned aerial vehicles and discusses in detail the integrated prototypes developed at the robotics laboratory of Chiba University. With demonstration videos, the book will interest not only graduate students, scientists, and engineers but also newcomers to the field.

Autonomous Flying Robots: Unmanned Aerial Vehicles and Micro Aerial Vehicles

The problem of estimating position and orientation (pose) of an object in real time constitutes an important issue for vision-based control of robots. Many vision-based pose-estimation schemes in robot control rely on an extended Kalman filter (EKF) that requires tuning of filter parameters. To obtain satisfactory results, EKF-based techniques rely on “known” noise statistics, initial object pose, and sufficiently high sampling rates for good approximation of measurement-function linearization. Deviations from such assumptions usually lead to degraded pose estimation during visual servoing. In this paper, a new algorithm, namely iterative adaptive EKF (IAEKF), is proposed by integrating mechanisms for noise adaptation and iterative-measurement linearization. The experimental results are provided to demonstrate the superiority of IAEKF in dealing with erroneous a priori statistics, poor pose initialization, variations in the sampling rate, and trajectory dynamics.

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A Kalman-Filter-Based Method for Pose Estimation in Visual Servoing

A vision-based position sensing system which provides three-dimensional relative position and orientation (pose) of an arbitrary moving object with respect to a camera for a real-time tracking control is studied. Kalman filtering was applied to vision measurements for the implicit solution of the photogrametric equations and to provide significant temporal filtering of the resulting motion parameters resulting in optimal pose estimation. Both computer simulation and real-time experimental results are presented to verify the effectiveness of the Kalman filter approach with large vision measurement noise. >

J. Wang

Papers

3D relative position and orientation estimation using Kalman filter for robot control