{"title":"基于神经模糊方法的智能移动机器人导航","authors":"S. Brahimi, O. Azouaoui, M. Loudini","doi":"10.1504/IJCAET.2019.10022397","DOIUrl":null,"url":null,"abstract":"This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.","PeriodicalId":346646,"journal":{"name":"Int. J. Comput. Aided Eng. Technol.","volume":"144 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Intelligent mobile robot navigation using a neuro-fuzzy approach\",\"authors\":\"S. Brahimi, O. Azouaoui, M. Loudini\",\"doi\":\"10.1504/IJCAET.2019.10022397\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.\",\"PeriodicalId\":346646,\"journal\":{\"name\":\"Int. J. Comput. Aided Eng. Technol.\",\"volume\":\"144 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Comput. Aided Eng. Technol.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/IJCAET.2019.10022397\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Comput. Aided Eng. Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJCAET.2019.10022397","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intelligent mobile robot navigation using a neuro-fuzzy approach
This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.
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