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Updated: Jun 6, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Iris Recognition System Using Advanced Segmentation Techniques and Fuzzy Clustering Methods for Robotic Control.

Slim Ben Chaabane^1,2, Rafika Harrabi^1,2, Hassene Seddik²

¹Computer Engineering Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia.

Journal of Imaging

|November 26, 2024

Summary

This summary is machine-generated.

This study introduces an innovative iris recognition system to control robots for physically disabled individuals. The system uses iris movement for commands, enhancing mobility and interaction for those with movement disabilities.

Keywords:

FKNN PCA TSR bald eagle search algorithm classification fast gradient filters fuzzy inference system (FIS)iris recognition robotic control sensitivity

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Area of Science:

Biomedical Engineering
Human-Computer Interaction
Robotics

Background:

Movement disabilities significantly impact the quality of life for physically disabled individuals.
There is a critical need for advanced assistive technologies to enhance independence and environmental interaction.
Existing control methods may not be suitable for individuals with severe mobility impairments.

Purpose of the Study:

To develop an innovative robot control system utilizing iris movement for physically disabled individuals.
To enhance precision and effectiveness in iris recognition for command-based control.
To provide a novel communication and control pathway for individuals with limited mobility.

Main Methods:

Iris recognition focused on identifying the iris centroid from eye images.
Advanced segmentation using fast gradient filters and fuzzy inference system (FIS).
Iris region isolation via the bald eagle search (BES) algorithm, followed by fuzzy KNN for matching.

Main Results:

The proposed iris recognition method demonstrated effectiveness in controlling a robot.
Validation using true success rate (TSR) showed competitive performance against existing models.
The system was tested on 400 images from 40 individuals, confirming its viability.

Conclusions:

Iris movement control offers a promising assistive technology for improving the quality of life for the physically disabled.
The integrated approach of advanced segmentation, search, and classification significantly enhances iris recognition performance.
This technology has the potential to empower individuals with limited mobility, offering greater autonomy.