Development of a Bayesian neural network to perform obstacle avoidance for an intelligent wheelchair

Nguyen, AV; Nguyen, LB; Su, S; Nguyen, HT

Development of a Bayesian neural network to perform obstacle avoidance for an intelligent wheelchair

Nguyen, AV Nguyen, LB Su, S Nguyen, HT

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Publisher:: IEEE Xplore
Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012, pp. 1884 - 1887
Issue Date:: 2012-12-14

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Field	Value	Language
dc.contributor.author	Nguyen, AV	en_US
dc.contributor.author	Nguyen, LB	en_US
dc.contributor.author	Su, S	en_US
dc.contributor.author	Nguyen, HT	en_US
dc.date	2012-08-28	en_US
dc.date.issued	2012-12-14	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012, pp. 1884 - 1887	en_US
dc.identifier.isbn	9781424441198	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119565
dc.description.abstract	This paper presents an extension of a real-time obstacle avoidance algorithm for our laser-based intelligent wheelchair, to provide independent mobility for people with physical, cognitive, and/or perceptual impairments. The laser range finder URG-04LX mounted on the front of the wheelchair collects immediate environment information, and then the raw laser data are directly used to control the wheelchair in real-time without any modification. The central control role is an obstacle avoidance algorithm which is a neural network trained under supervision of Bayesian framework, to optimize its structure and weight values. The experiment results demonstrated that this new approach provides safety, smoothness for autonomous tasks and significantly improves the performance of the system in difficult tasks such as door passing. © 2012 IEEE.	en_US
dc.publisher	IEEE Xplore	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.ispartof	International Conference of the IEEE Engineering in Medicine and Biology Society	en_US
dc.relation.isbasedon	10.1109/EMBC.2012.6346320	en_US
dc.rights	© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Development of a Bayesian neural network to perform obstacle avoidance for an intelligent wheelchair	en_US
dc.type	Conference Proceeding
utslib.location.activity	San Diego, CA, USA	en_US
utslib.for	090303 Biomedical Instrumentation	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.declined	2017-11-01T11:25:05.638+1100
pubs.consider-herdc	false	en_US
pubs.deleted	2017-11-01T11:25:05.638+1100
pubs.finish-date	2012-09-01	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2012-08-28	en_US

Abstract:

This paper presents an extension of a real-time obstacle avoidance algorithm for our laser-based intelligent wheelchair, to provide independent mobility for people with physical, cognitive, and/or perceptual impairments. The laser range finder URG-04LX mounted on the front of the wheelchair collects immediate environment information, and then the raw laser data are directly used to control the wheelchair in real-time without any modification. The central control role is an obstacle avoidance algorithm which is a neural network trained under supervision of Bayesian framework, to optimize its structure and weight values. The experiment results demonstrated that this new approach provides safety, smoothness for autonomous tasks and significantly improves the performance of the system in difficult tasks such as door passing. © 2012 IEEE.

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http://hdl.handle.net/10453/119565