Position Control Experiments Using Vision

This article presents results of experiments carried out to test the vision-based method of camera-space manipulation using a fully three-dimensional rigid-body mating task. In this work, the performance of the method under various operating conditions is studied experimentally using a large six-axis manipulator. Further, an analytical study is carried out to understand the influence of relative orientation of cameras and their number on the terminal positioning precision expected with the method.

Autonomous Helicopter Tracking and Localization Using a Self-surveying Camera Array

A Self-surveying Camera Array (SSCA) is a vision-based local-area positioning system consisting of multiple ground-deployed cameras that are capable of self-surveying their extrinsic parameters while tracking and localizing a moving target. This paper presents the self-surveying algorithm being used to track a target helicopter in each camera frame and to localize the helicopter in an array-fixed frame. Three cameras are deployed independently in an arbitrary arrangement that allows each camera to view the helicopter's flight volume. The helicopter then flies an unplanned path that allows the cameras to calibrate the relative locations and orientations by utilizing a self-surveying algorithm that is extended from the well-known structure from motion algorithm and the bundle adjustment technique. This yields the cameras'extrinsic parameters enabling real-time helicopter positioning via triangulation. This paper also presents results from field trials, which verify the feasibility of the SSCA as a readily-deployable system applicable to helicopter tracking and localization. The results demonstrate that, compared to the differential GPS solution as true reference, the SSCA alone is capable of positioning the helicopter with meter-level accuracy. The SSCA has been integrated with onboard inertial sensors providing a reliable positioning system to enable successful autonomous hovering.

Application of a Precision-Enhancing Measure in 3D Rigid-Body Positioning Using Camera-Space Manipulation

This article presents an iterative procedure for improving the precision obtainable with camera-space manipulation. It also reports on extensive experimental results using this procedure with a task of some historical note in the area of vision-guided robotics, a task that involves close-tolerance, 3D, rigid-body assembly. The procedure was devised to achieve higher pre cision by introducing to parameter estimates the accuracy of the "perspective" camera model, while retaining the numerical advantage of the orthographic model. The camera-space coor dinates of each of several visually detected cues on the grasped object are multiplied by an iteratively improved correction fac tor, so that the modified image-plane appearances of the cues more closely approach the ideal of an orthographic projection. The correction factor is normalized near the point of maneuver termination so that camera-space maneuver objectives may stay unchanged. A similar strategy may be used to determine the camera-space objectives themselves. Results of experimental and simulation studies are presented and discussed.