Research Papers

Dynamic Image-Based Visual Servo Control Using Centroid and Optic Flow Features

[+] Author and Article Information
R. Mahony

Department of Engineering, Australian National University, Canberra 0200, Australiarobert.mahony@anu.edu.au

P. Corke

 CSIRO ICT Centre, Technology Court, Pullenvale 4069, Australiapeter.corke@csiro.au

T. Hamel

 I3S UNSA-CNRS, 2000 route des Lucioles-Les Algorithmes, Bâtiment Euclide B, BP 121, 06903 Sophia Antipolis-Cedex, Francethamel@i3s.unice.fr

J. Dyn. Sys., Meas., Control 130(1), 011005 (Dec 05, 2007) (12 pages) doi:10.1115/1.2807085 History: Received November 28, 2005; Revised January 18, 2007; Published December 05, 2007

This paper considers the question of designing a fully image-based visual servo control for a class of dynamic systems. The work is motivated by the ongoing development of image-based visual servo control of small aerial robotic vehicles. The kinematics and dynamics of a rigid-body dynamical system (such as a vehicle airframe) maneuvering over a flat target plane with observable features are expressed in terms of an un-normalized spherical centroid and an optic flow measurement. The image-plane dynamics with respect to force input are dependent on the height of the camera above the target plane. This dependence is compensated by introducing virtual height dynamics and adaptive estimation in the proposed control. A fully nonlinear adaptive control design is provided that ensures asymptotic stability of the closed-loop system for all feasible initial conditions. The choice of control gains is based on an analysis of the asymptotic dynamics of the system. Results from a realistic simulation are presented that demonstrate the performance of the closed-loop system. To the author’s knowledge, this paper documents the first time that an image-based visual servo control has been proposed for a dynamic system using vision measurement for both position and velocity.

Copyright © 2008 by American Society of Mechanical Engineers
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The notation Ω× denotes the skew-symmetric matrix such that Ω×v=Ω×v for the vector cross product × and any vector v∊R3.


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Figure 5

Evolution of the internal parameter state of the adaptive controller

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Figure 6

Vision data from the example considered. The topmost graph shows the evolution of the image feature q=∑pi. The second graph shows the visual flow of the centroid feature q̇=∑ṗi, while the final graph shows the image feature velocity W obtained from Eq. 16.

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Figure 4

Closed-loop trajectory in 3D for a typical example

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Figure 1

Reference frames, forces, and torques for a schematic representation of a quad-rotor aerial robot

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Figure 2

Schematic representation of the target plane and target constellations showing notation

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Figure 3

Evolution of the state variables associated with translational motion of the closed-loop system. The response shown is characteristic of a wide range of initial conditions.




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