Real-time sensor data for efficient localisation employing a weightless neural system

Gillham, Michael and Howells, Gareth and Spurgeon, Sarah K. and Kelly, Stephen W. and Pepper, Matthew G. (2012) Real-time sensor data for efficient localisation employing a weightless neural system. In: Proceedings of 1st International Conference on Systems and Computer Science (ICSCS). IEEE pp. 1-5. ISBN 978-1-4673-0673-7. E-ISBN 978-1-4673-0672-0. (doi:https://doi.org/10.1109/IConSCS.2012.6502448) (Full text available)

Abstract

Mobile robotic localisation obtained from simple sensor data potentially offers real-time real-world integration. Computationally highly efficient Weightless Neural Networks, when used for location determination, further enhances performance potential. This paper introduces techniques for the identification of rooms or locations in the absence of complex and succinct information. Using simple floor colour and texture, and room geometrics from ranging data, although inherent uncertainties exist, these limited simple fused real-time sensor data can be easily resolved into a room identification criterion using architectures generated by a Genetic Algorithm technique applied to a Weightless Neural Network Architecture.

Item Type:	Conference or workshop item (Paper)
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications) > TK7880 Applications of electronics (inc industrial & domestic) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications) > TK7880 Applications of electronics (inc industrial & domestic) > TK7882.P3 Pattern Recognition
Divisions:	Faculties > Sciences > School of Engineering and Digital Arts Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User:	M. Gillham
Date Deposited:	01 Feb 2014 20:40 UTC
Last Modified:	14 Dec 2017 10:33 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/38137 (The current URI for this page, for reference purposes)