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Related Experiment Video

Updated: Feb 26, 2026

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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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
PubMed
Summary
This summary is machine-generated.

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 roboticsHOG featuresPython optimizationQualitative controlReal-time systemsVisual servoingWheelchair 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.