EventsThe 3rd International Electronic Conference on Applied Sciences
Published
This submission belongs to the session D. Electrical, Electronics and Communications Engineering of the event The 3rd International Electronic Conference on Applied Sciences
Published date
09 Feb, 2022
Academic Editor
author-avatarNunzio Cennamo
Citation
Md Abdul Baset Sarker, Ernesto Sola-Thomas, Collin Jamieson, Masudul H. Imtiaz, Autonomous Movement of Wheelchair by Cameras and YOLOv7, in Proceedings of The 3rd International Electronic Conference on Applied Sciences, 1 December–15 December 2022, MDPI: Basel, Switzerland, doi: 10.3390/ASEC2022-13834
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Autonomous Movement of Wheelchair by Cameras and YOLOv7

Collin Jamieson 2
1. Electrical and computer engineering, Clarkson University, USA, Switzerland
2. Electrical and computer engineering, Clarkson University, USA
3. mimtiaz@clarkson.edu
Abstract

A wheelchair can provide limited but crucial mobility to an injured or disabled individual. This paper presents the first stage of the development of a smart wheelchair which is the customization of a manually controlled wheelchair with a novel implementation of octascopic vision. This relatively inexpensive design of an autonomous wheelchair consists of two monochromic camera arrays (each having four cameras) placed around the frame of the wheelchair to achieve a view of ~360 degrees. The initial research goal was to design a wheelchair controlled by the embedded processor, allowing the wheelchair to navigate autonomously around an indoor facility with and without human intervention. Also, it was intended to allow those previously denied access to the world of automatic wheelchairs because of a low personal income. Our developed wheelchair cost only 1350$. Through the testing of wheelchair functionality, a) a large dataset of octascopic images was captured from this wheelchair, and b) a Yolov7-based object detection model was developed to avoid obstacles and autonomously control the movement. This paper presents the completed wheelchair hardware and the obstacle detection model using octascopic images. All the project design files have been granted an open-source license and can be reproduced publicly.

Keywords
Autonomous navigation
Deep Learning
Obstacle detection
Yolov7
Wheelchair
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