Output details

Automated Adaptation of Input and Output Data for a Weightless Artificial Neural Network

Robot guidance is still a very challenging issue computationally in both the academic and industrial worlds. This paper is influential in introducing highly computationally efficient techniques using an adaptation of a weightless neural architecture developed at the University of Kent which can be employed in the real-time environment of an automated guided wheelchair as part of an Interreg ERDF project “SYSIASS” (www.sysiass.eu). The wheelchair is able to autonomously navigate in enclosed spaces and move through doorways representing a major advance in telecare provision by reducing the necessity for a human carer and improved independence and quality of life for the general population.