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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

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Automated Curb Recognition and Negotiation for Robotic Wheelchairs.

Sivashankar Sivakanthan^1,2, Jeremy Castagno³, Jorge L Candiotti^1,2

¹School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA.

Sensors (Basel, Switzerland)

|December 10, 2021

Summary

A new Curb Recognition and Negotiation (CRN) system enhances robotic wheelchairs, enabling them to automatically detect and navigate curbs safely. This robotic wheelchair advancement improves user speed and reduces errors when overcoming obstacles.

Keywords:

obstacle negotiation rehabilitation engineering step-climbing wheelchair

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Area of Science:

Robotics
Assistive Technology
Computer Vision

Background:

Electric wheelchairs struggle with architectural barriers like curbs, posing risks to users and equipment.
Existing robotic wheelchairs require manual user alignment for curb negotiation, which can be difficult for individuals with impairments.

Purpose of the Study:

To develop and evaluate a Curb Recognition and Negotiation (CRN) system for robotic wheelchairs.
To enhance user safety and speed when navigating curbs by automating the recognition and negotiation process.

Main Methods:

Integrated the Polylidar3D plane extraction algorithm with a mobility enhancement robot (MEBot) curb negotiation application.
Developed the CRN system to automatically recognize curb characteristics, approach, and negotiate curbs.

Main Results:

The CRN system successfully recognized curbs in 14 out of 15 controlled test positions.
Accurately determined curb height and distance, enabling safe execution of curb negotiation by the MEBot with minimal alignment deviation (1.5 ± 4.4°).

Conclusions:

The CRN system demonstrates the potential of robotic wheelchairs to increase speed and reduce human error in curb negotiation.
This technology can significantly improve accessibility for users with physical or sensory impairments.