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An optimized real-time qualitative HOG-based visual servoing system for autonomous wheelchair.

A H Abdul Hafez¹

¹Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia. aabdulhafiz@kfu.edu.sa.

Scientific Reports

|February 24, 2026

Summary

This study introduces a faster, efficient visual servoing method for autonomous wheelchairs, improving real-time navigation on low-power devices. The qualitative histogram of oriented gradients (HOG)-based visual servoing (QHOGVS) enhances speed and stability for assistive mobility systems.

Keywords:

Assistive robotics HOG features Python optimization Qualitative control Real-time systems Visual servoing Wheelchair navigation

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Area of Science:

Robotics
Computer Vision
Assistive Technology

Background:

Real-time performance is crucial for autonomous mobility systems.
Existing visual servoing methods often struggle with computational demands on low-power hardware.
Assistive mobility requires reliable and precise navigation.

Purpose of the Study:

To present a computationally effective histogram of oriented gradients (HOG)-based visual servoing (QHOGVS) approach for autonomous wheelchair navigation.
To achieve real-time performance on low-power hardware while maintaining navigation precision.
To address the trade-offs between computational efficiency and navigation accuracy in embedded systems.

Main Methods:

Developed a qualitative histogram of oriented gradients (HOG)-based visual servoing (QHOGVS) algorithm.
Optimized the visual servoing pipeline using Python vectorization.
Incorporated an adaptive activation function to modulate error convergence.

Main Results:

Achieved a 68× speed improvement (0.08 FPS to 5.5 FPS) on a Raspberry Pi.
Demonstrated stable trajectory tracking during navigation.
Validated the system's functionality on a physical wheelchair platform through real-world tasks.

Conclusions:

The QHOGVS approach enables real-time autonomous wheelchair navigation on low-power embedded systems.
The method successfully balances computational efficiency with navigation precision.
This work provides a feasible solution for practical assistive technology and opens avenues for adaptive visual navigation research.