{"title":"具有里程不确定性的多智能体移动机器人在线地图构建进化算法","authors":"Yong-Jae Kim, Jong-Hwan Kim","doi":"10.1109/CEC.2000.870286","DOIUrl":null,"url":null,"abstract":"An online map building evolutionary algorithm is proposed using multi-agent mobile robots with odometric uncertainty. The control algorithm for map building in each robot is identical and trained by an online evolutionary algorithm (EA). Each robot has configuration uncertainty which increases as it moves, and it perceives the surrounding environment information by the limited range sensors. It communicates with other robots and shares the information. The elementary behaviors are defined and they are used to build a map. EA is applied to the defined behavior set for optimizing the robot actions. To demonstrate the effectiveness of the proposed algorithm, computer simulations are conducted for various environments.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Online map building evolutionary algorithm for multi-agent mobile robots with odometric uncertainty\",\"authors\":\"Yong-Jae Kim, Jong-Hwan Kim\",\"doi\":\"10.1109/CEC.2000.870286\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An online map building evolutionary algorithm is proposed using multi-agent mobile robots with odometric uncertainty. The control algorithm for map building in each robot is identical and trained by an online evolutionary algorithm (EA). Each robot has configuration uncertainty which increases as it moves, and it perceives the surrounding environment information by the limited range sensors. It communicates with other robots and shares the information. The elementary behaviors are defined and they are used to build a map. EA is applied to the defined behavior set for optimizing the robot actions. To demonstrate the effectiveness of the proposed algorithm, computer simulations are conducted for various environments.\",\"PeriodicalId\":218136,\"journal\":{\"name\":\"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2000.870286\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2000.870286","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Online map building evolutionary algorithm for multi-agent mobile robots with odometric uncertainty
An online map building evolutionary algorithm is proposed using multi-agent mobile robots with odometric uncertainty. The control algorithm for map building in each robot is identical and trained by an online evolutionary algorithm (EA). Each robot has configuration uncertainty which increases as it moves, and it perceives the surrounding environment information by the limited range sensors. It communicates with other robots and shares the information. The elementary behaviors are defined and they are used to build a map. EA is applied to the defined behavior set for optimizing the robot actions. To demonstrate the effectiveness of the proposed algorithm, computer simulations are conducted for various environments.
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