Lightweight Gaussian Process-Based Visual Servoing for Autonomous Wheelchair Sidewalk Navigation
The personal mobility of wheelchair users is pivotal to their overall well-being. Navigating electric wheelchairs through confined spaces poses notable challenges, particularly for individuals with disabilities. In response, this research introduces a pioneering vision-based approach for sidewalk na...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10966836/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850170897965514752 |
|---|---|
| author | A. H. Abdul Hafez Ismail Haj Osman Efgan Ugur Tolgay Kara |
| author_facet | A. H. Abdul Hafez Ismail Haj Osman Efgan Ugur Tolgay Kara |
| author_sort | A. H. Abdul Hafez |
| collection | DOAJ |
| description | The personal mobility of wheelchair users is pivotal to their overall well-being. Navigating electric wheelchairs through confined spaces poses notable challenges, particularly for individuals with disabilities. In response, this research introduces a pioneering vision-based approach for sidewalk navigation that leverages tactile paving features and advanced machine learning models. While many existing solutions rely on expensive sensors or GPU-accelerated deep networks, we address this limitation by employing only a low-cost monocular camera and a lightweight GP model. Running at 7 Hz on a Raspberry Pi, our approach offers real-time autonomy without requiring specialized or high-end hardware. The proposed methodology includes creating a specialized dataset, formulating a custom control law, and deploying a lightweight real-time Gaussian Process (GP) model on a Raspberry Pi platform. Thorough experimentation substantiates the efficacy of the presented approach, demonstrating accurate and dependable autonomous wheelchair navigation on sidewalks. Beyond its technical contributions, the proposed solution offers a cost-effective alternative to conventional sensor-dependent systems, profoundly enhancing user mobility and quality of life. By equipping wheelchair users with enhanced navigation capabilities, this research strives to foster greater independence and inclusion in their daily lives. |
| format | Article |
| id | doaj-art-a2d36029e242489fbfa5a0292f5e4a3a |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a2d36029e242489fbfa5a0292f5e4a3a2025-08-20T02:20:23ZengIEEEIEEE Access2169-35362025-01-0113695826959510.1109/ACCESS.2025.356146710966836Lightweight Gaussian Process-Based Visual Servoing for Autonomous Wheelchair Sidewalk NavigationA. H. Abdul Hafez0https://orcid.org/0000-0002-1908-5521Ismail Haj Osman1https://orcid.org/0000-0002-0894-081XEfgan Ugur2https://orcid.org/0000-0002-0842-0216Tolgay Kara3https://orcid.org/0000-0003-3991-8524Department of Computer Science, Faculty of Computer Science and Information Technology, King Faisal University, Al Ahsa, Saudi ArabiaDepartment of Electrical and Computer Engineering, The University of Tulsa, Tulsa, OK, USADepartment of Computer Engineering, Faculty of Engineering, Niğde Ömer Halisdemir University, Niğde, TürkiyeDepartment of Electrical and Electronic Engineering, Faculty of Engineering, Gaziantep University, Gaziantep, TürkiyeThe personal mobility of wheelchair users is pivotal to their overall well-being. Navigating electric wheelchairs through confined spaces poses notable challenges, particularly for individuals with disabilities. In response, this research introduces a pioneering vision-based approach for sidewalk navigation that leverages tactile paving features and advanced machine learning models. While many existing solutions rely on expensive sensors or GPU-accelerated deep networks, we address this limitation by employing only a low-cost monocular camera and a lightweight GP model. Running at 7 Hz on a Raspberry Pi, our approach offers real-time autonomy without requiring specialized or high-end hardware. The proposed methodology includes creating a specialized dataset, formulating a custom control law, and deploying a lightweight real-time Gaussian Process (GP) model on a Raspberry Pi platform. Thorough experimentation substantiates the efficacy of the presented approach, demonstrating accurate and dependable autonomous wheelchair navigation on sidewalks. Beyond its technical contributions, the proposed solution offers a cost-effective alternative to conventional sensor-dependent systems, profoundly enhancing user mobility and quality of life. By equipping wheelchair users with enhanced navigation capabilities, this research strives to foster greater independence and inclusion in their daily lives.https://ieeexplore.ieee.org/document/10966836/Gaussian processwheelchair navigationwheelchair controlvision-based navigationtactile pavingsidewalk navigation |
| spellingShingle | A. H. Abdul Hafez Ismail Haj Osman Efgan Ugur Tolgay Kara Lightweight Gaussian Process-Based Visual Servoing for Autonomous Wheelchair Sidewalk Navigation IEEE Access Gaussian process wheelchair navigation wheelchair control vision-based navigation tactile paving sidewalk navigation |
| title | Lightweight Gaussian Process-Based Visual Servoing for Autonomous Wheelchair Sidewalk Navigation |
| title_full | Lightweight Gaussian Process-Based Visual Servoing for Autonomous Wheelchair Sidewalk Navigation |
| title_fullStr | Lightweight Gaussian Process-Based Visual Servoing for Autonomous Wheelchair Sidewalk Navigation |
| title_full_unstemmed | Lightweight Gaussian Process-Based Visual Servoing for Autonomous Wheelchair Sidewalk Navigation |
| title_short | Lightweight Gaussian Process-Based Visual Servoing for Autonomous Wheelchair Sidewalk Navigation |
| title_sort | lightweight gaussian process based visual servoing for autonomous wheelchair sidewalk navigation |
| topic | Gaussian process wheelchair navigation wheelchair control vision-based navigation tactile paving sidewalk navigation |
| url | https://ieeexplore.ieee.org/document/10966836/ |
| work_keys_str_mv | AT ahabdulhafez lightweightgaussianprocessbasedvisualservoingforautonomouswheelchairsidewalknavigation AT ismailhajosman lightweightgaussianprocessbasedvisualservoingforautonomouswheelchairsidewalknavigation AT efganugur lightweightgaussianprocessbasedvisualservoingforautonomouswheelchairsidewalknavigation AT tolgaykara lightweightgaussianprocessbasedvisualservoingforautonomouswheelchairsidewalknavigation |