A Learning method of a Mobile Robot using ACO and a Fuzzy Navigator

Please use this identifier to cite or link to this item: https://dspace.univ-ouargla.dz/jspui/handle/123456789/3546

Title:	A Learning method of a Mobile Robot using ACO and a Fuzzy Navigator
Authors:	Hamid Boubertakh, Salim Labiod, Mohamed Tadjine, Pierre-Yves Glorennec
Keywords:	Mobile robot obstacle avoidance Fuzzy logic fuzzy systems
Issue Date:	22-Dec-2013
Series/Report no.:	2011;
Abstract:	This paper, proposes an ant colony optimization (ACO)-based learning method for a fuzzy navigator of a mobile robot evolving in unknown environment. The navigator is a collection of IF THEN rules translating the human reasoning. The robot decision is made using fuzzy logic. The learning method consists in the online searching of the best fuzzy rules conclusions when the robot executes its predefined task. Finally some simulation results are presented which show the performance of the proposed method.
Description:	The International Conference on Electronics & Oil ICEO11 March 1-2 2011
URI:	http://hdl.handle.net/123456789/3546
ISSN:	MO
Appears in Collections:	3. Faculté des sciences appliquées

Files in This Item:

File	Description	Size	Format
Hamid Boubertakh.pdf		1,11 MB	Adobe PDF	View/Open

DSpace JSPUI