Servo Control Of Robotic Manipulators Research Articles

Incremental visual servo control of robotic manipulator for autonomous capture of non-cooperative target

This paper develops a new autonomous incremental visual servo control law for the robotic manipulator to capture a non-cooperative target, where the control input is the incremental joint angle to avoid the multiple solutions in the existing inverse kinematics. The position and motion of the non-cooperative target are estimated by an eye-to-hand vision system in real time by integrated photogrammetry and extended Kalman filter. The estimated position and motion of the target are fed into the newly developed position-based visual servo control law to drive the manipulator incrementally towards the dynamically predicted interception point between trajectories of the end effector and the target. To validate the proposed approach, a hardware-in-the-loop simulation has been conducted where the position and motion of the target is estimated by a real eye-to-hand camera and fed into the simulation of the robotic manipulator. The simulation results show the proposed incremental visual servo control law is stable and able to avoid the multiple solutions in the total inverse kinematics.

Estimating uncertainty in SSD-based feature tracking

Sum-of-squared-differences (SSD) based feature trackers have enjoyed growing popularity in recent years, particularly in the field of visual servo control of robotic manipulators. These trackers use SSD correlation measures to locate target features in sequences of images. The results can then be used to estimate the motion of objects in the scene, to infer the 3D structure of the scene, or to control robot motions. The reliability of the information provided by these trackers can be degraded by a variety of factors, including changes in illumination, poor image contrast, occlusion of features, or unmodeled changes in objects. This has led other researchers to develop confidence measures that are used to either accept or reject individual features that are located by the tracker. In this paper, we derive quantitative measures for the spatial uncertainty of the results provided by SSD-based feature trackers. Unlike previous confidence measures that have been used only to accept or reject hypotheses, our new measure allows the uncertainty associated with a feature to be used to weight its influence on the overall tracking process. Specifically, we scale the SSD correlation surface, fit a Gaussian distribution to this surface, and use this distribution to estimate values for a covariance matrix. We illustrate the efficacy of these measures by showing the performance of an example object tracking system with and without the measures.

A tutorial on visual servo control

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.