Advanced semi-autonomous wheelchair system for people with disabilities

Nguyen, VA

Advanced semi-autonomous wheelchair system for people with disabilities

Nguyen, VA

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Publication Type:: Thesis
Issue Date:: 2014

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Field	Value	Language
dc.contributor.author	Nguyen, VA
dc.date.accessioned	2014-06-16T02:55:25Z
dc.date.available	2014-06-16T02:55:25Z
dc.date.issued	2014
dc.identifier.uri	http://hdl.handle.net/10453/28075
dc.description	University of Technology, Sydney. Faculty of Engineering and Information Technology.	en_US
dc.description.abstract	Powered wheelchairs have been dedicated to providing independent mobility to individuals who have physical or cognitive impairments and disabilities. Unfortunately, there are still a significant number of impaired people who find it difficult or impossible to use the standard powered wheelchairs due to their high level of injuries. Intelligent wheelchairs, therefore, have been developed to cater for these users, utilizing control methods and assistive navigation algorithms to help the patients safely navigate an environment. This thesis aims at developing an intelligent wheelchair that provides functional mobility for the disabled users. Unlike a number of intelligent wheelchairs based on algorithms from the robotic field, this dissertation investigates two advanced techniques, which are Bayesian theory and artificial neural network, in building the semi-autonomous navigation system for the intelligent wheelchair. In particular, Bayesian theory is employed to determine free spaces for navigation and make adaptive shared control decisions between the users and the machine. In the meantime, the artificial neural network is recruited to learn the navigation skills through a number of patterns. During the training, the Bayesian framework is also applied to optimize the network structure as well as the weight values. After training, the network is able to navigate the wheelchair into the location of interest without colliding with surrounding obstacles. The developed semi-autonomous navigation system was experimentally verified on the real wheelchair with a number of participants. The experimental results convincingly demonstrate that our system is able to real - time navigate in the unknown environment with safe, smooth and optimal trajectories, and that it can be a valid alternative for the people with disabilities.	en_US
dc.format	Thesis (PhD)	en_US
dc.language.iso	en	en_US
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/28075/2/02whole.pdf
dc.rights	au.edu.uts.lib/ppc
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Intelligent wheelchair.	en
dc.subject	Smart wheelchair.	en
dc.subject	Semi-autonomous navigation system.	en
dc.subject	Bayesian neural network.	en
dc.subject	Autonomous obstacle avoidance.	en
dc.title	Advanced semi-autonomous wheelchair system for people with disabilities	en_US
dc.type	Thesis
utslib.copyright.status	open_access

Abstract:

Powered wheelchairs have been dedicated to providing independent mobility to individuals who have physical or cognitive impairments and disabilities. Unfortunately, there are still a significant number of impaired people who find it difficult or impossible to use the standard powered wheelchairs due to their high level of injuries. Intelligent wheelchairs, therefore, have been developed to cater for these users, utilizing control methods and assistive navigation algorithms to help the patients safely navigate an environment. This thesis aims at developing an intelligent wheelchair that provides functional mobility for the disabled users. Unlike a number of intelligent wheelchairs based on algorithms from the robotic field, this dissertation investigates two advanced techniques, which are Bayesian theory and artificial neural network, in building the semi-autonomous navigation system for the intelligent wheelchair. In particular, Bayesian theory is employed to determine free spaces for navigation and make adaptive shared control decisions between the users and the machine. In the meantime, the artificial neural network is recruited to learn the navigation skills through a number of patterns. During the training, the Bayesian framework is also applied to optimize the network structure as well as the weight values. After training, the network is able to navigate the wheelchair into the location of interest without colliding with surrounding obstacles. The developed semi-autonomous navigation system was experimentally verified on the real wheelchair with a number of participants. The experimental results convincingly demonstrate that our system is able to real - time navigate in the unknown environment with safe, smooth and optimal trajectories, and that it can be a valid alternative for the people with disabilities.

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http://hdl.handle.net/10453/28075