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Spherical vision cameras in a semi-autonomous wheelchair system
Jordan S Nguyen1, Steven W Su, Hung T Nguyen
1Faculty of Engineering and Information Technology, University of Technology, Sydney, Broadway, NSW 2007, Australia. Jordan.S.Nguyen@eng.uts.edu.au
Summary
This study developed an advanced spherical vision system for power wheelchairs, achieving 96% accuracy in detecting surrounding obstacles for safer autonomous navigation.
Area of Science:
- Robotics and Artificial Intelligence
- Assistive Technology
- Computer Vision
Background:
- Autonomous navigation for power wheelchairs requires robust obstacle detection.
- Spherical vision systems offer a wide field of view for comprehensive environmental awareness.
- Existing systems may have limitations in real-time detection and decision-making.
Purpose of the Study:
- To enhance power wheelchair navigation by integrating a spherical vision camera system.
- To enable real-time detection of surrounding objects and assess potential movement hazards.
- To support automated decision-making for safe and efficient autonomous navigation.
Main Methods:
- Utilized a Point Grey Research (PGR) Ladybug2 spherical vision camera system mounted on a power wheelchair.
- Implemented instantaneous neural network classification on individual camera images for obstacle identification.
- Developed algorithms for analyzing surrounding visual data to detect obstacles and potential dangers.
Main Results:
- Successfully achieved automated obstacle detection with accuracies up to 96%.
- Demonstrated the system's capability to provide comprehensive surrounding vision information.
- Validated the effectiveness of neural network classification for real-time hazard assessment.
Conclusions:
- The integrated spherical vision system significantly enhances obstacle detection capabilities for power wheelchairs.
- This technology supports automated navigation, path planning, and collision avoidance.
- The system represents a key advancement in assistive technology for autonomous mobility.
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