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Related Experiment Video

Updated: Jun 18, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Homography-based control scheme for mobile robots with nonholonomic and field-of-view constraints.

Gonzalo López-Nicolás¹, Nicholas R Gans, Sourabh Bhattacharya

¹Department of Informática e Ingeniería de Sistemas, Instituto de Investigación en Ingeniería de Aragón, Universidad de Zaragoza, Zaragoza, Spain. gonlopez@unizar.es

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|November 20, 2009

Summary

This study introduces a novel visual servo controller for robots, directly using homography matrix entries for optimal path control. It enables precise navigation for differential drive robots under field-of-view constraints.

Related Experiment Videos

Last Updated: Jun 18, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Area of Science:

Robotics
Computer Vision
Control Systems

Background:

Robots require precise navigation, often limited by vision system constraints.
Existing visual servoing methods can be complex or computationally intensive.

Purpose of the Study:

To develop a visual servo controller for nonholonomic differential drive robots.
To achieve optimal path control directly from image homographies, bypassing explicit pose estimation.

Main Methods:

Utilized homography computation between current and goal images.
Developed direct control laws based on homography matrix entries for rotations, straight-line segments, and logarithmic spirals.
Defined switching conditions for path segment sequencing based on homography decomposition.

Main Results:

Successfully implemented a visual servo controller using direct homography-based control laws.
Demonstrated control for optimal path classes: rotations, straight-line segments, and logarithmic spirals.
Provided controllability and stability analysis with experimental validation.

Conclusions:

The proposed method offers an effective alternative to traditional pose-estimation-based visual servoing.
Directly using homography entries simplifies control and achieves optimal path following for differential drive robots.
The system is validated for controllability, stability, and practical performance.