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DC Field | Value | Language |
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dc.contributor.author | Hamid Boubertakh, Salim Labiod, Mohamed Tadjine, Pierre-Yves Glorennec | - |
dc.date.accessioned | 2013-12-22T10:36:54Z | - |
dc.date.available | 2013-12-22T10:36:54Z | - |
dc.date.issued | 2013-12-22 | - |
dc.identifier.issn | MO | - |
dc.identifier.uri | http://hdl.handle.net/123456789/3546 | - |
dc.description | The International Conference on Electronics & Oil ICEO11 March 1-2 2011 | en_US |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartofseries | 2011; | - |
dc.subject | Mobile robot | en_US |
dc.subject | obstacle avoidance | en_US |
dc.subject | Fuzzy logic | en_US |
dc.subject | fuzzy systems | en_US |
dc.title | A Learning method of a Mobile Robot using ACO and a Fuzzy Navigator | en_US |
dc.type | Article | en_US |
Appears in Collections: | 3. Faculté des sciences appliquées |
Files in This Item:
File | Description | Size | Format | |
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Hamid Boubertakh.pdf | 1,11 MB | Adobe PDF | View/Open |
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