{"title":"Fuzzy behavior-based control for a miniature mobile robot","authors":"K. Izumi, Keigo Watanabe, T. Miyazaki","doi":"10.1109/KES.1998.726012","DOIUrl":null,"url":null,"abstract":"We have already developed a fuzzy behavior-based control system that combines the concept of subsumption architecture and fuzzy reasoning technique. When applying it to a mobile robot, the robot needs to have precise information such as distance and azimuth. We discuss how to construct the fuzzy behavior-based control system for a miniature mobile robot in the case when the robot can not often receive any information from sensors. In addition, we apply a virus evolutionary genetic algorithm with species to generating the fuzzy rule. The effectiveness of the proposed method is shown by simulations.","PeriodicalId":394492,"journal":{"name":"1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KES.1998.726012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
We have already developed a fuzzy behavior-based control system that combines the concept of subsumption architecture and fuzzy reasoning technique. When applying it to a mobile robot, the robot needs to have precise information such as distance and azimuth. We discuss how to construct the fuzzy behavior-based control system for a miniature mobile robot in the case when the robot can not often receive any information from sensors. In addition, we apply a virus evolutionary genetic algorithm with species to generating the fuzzy rule. The effectiveness of the proposed method is shown by simulations.
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