A New Collision Avoidance System for Smart Wheelchairs Using Deep Learning

Haddad, Malik, Sanders, David, Tewkesbury, Giles, Langner, Martin and Keeble, Will (2022) A New Collision Avoidance System for Smart Wheelchairs Using Deep Learning. In: The 3rd International Symposium on Automation, Information and Computing - ISAIC.

Abstract

The work presented describes a new collision avoidance system for smart wheelchair steering using Deep Learning. The system used an Artificial Neural Network (ANN) and applied a Rule-based method to create testing and training sets. Three ultrasonic sensors were used to create an array. The sensors measured distance to the closest object to the left, right and in front of the wheelchair. Readings from the array were utilised as inputs to the ANN. The system employed Deep Learning to avoid obstacles. The driving directions considered were spin left, turn left, forward, spin right, turn right and stop. The new system drove the smart wheelchair away from obstacles. The new system provided reliable results when tested and achieved 99.17% and 97.53% training and testing accuracies respectively. The testing confirmed that the new system successfully drove a smart wheelchair away from obstacles. The system can be overridden if required. Clinical tests will be carried at Chailey Heritage Foundation.

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