{"title":"基于案例任务规划的室外自主移动机器人参数自适应","authors":"Qiqian Zhang, Hui Qian, Miaoliang Zhu","doi":"10.1109/IVS.2005.1505137","DOIUrl":null,"url":null,"abstract":"Because of the complexity of the outdoor terrain and the variety of the robot missions, the autonomous mobile robots (AMR) have to carry out missions in unstructured and impossibly predicted environment. The acquisition of accurate environment parameters is crucial for robot path planning. In this article, the idea of mission-plan is introduced on the basis of the path plan. The whole mission is decomposed into several sub-missions during the mission modeling process. And one case-based mission-planning method (CBMP) is applied to filter and optimize the robot's geometric and kinetic parameters for properly configuration. At last, a proving ground is to validate the parameter adaptation of CBMP algorithm.","PeriodicalId":386189,"journal":{"name":"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Parameter adaptation by case-based mission-planning of outdoor autonomous mobile robot\",\"authors\":\"Qiqian Zhang, Hui Qian, Miaoliang Zhu\",\"doi\":\"10.1109/IVS.2005.1505137\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Because of the complexity of the outdoor terrain and the variety of the robot missions, the autonomous mobile robots (AMR) have to carry out missions in unstructured and impossibly predicted environment. The acquisition of accurate environment parameters is crucial for robot path planning. In this article, the idea of mission-plan is introduced on the basis of the path plan. The whole mission is decomposed into several sub-missions during the mission modeling process. And one case-based mission-planning method (CBMP) is applied to filter and optimize the robot's geometric and kinetic parameters for properly configuration. At last, a proving ground is to validate the parameter adaptation of CBMP algorithm.\",\"PeriodicalId\":386189,\"journal\":{\"name\":\"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVS.2005.1505137\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Proceedings. Intelligent Vehicles Symposium, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2005.1505137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parameter adaptation by case-based mission-planning of outdoor autonomous mobile robot
Because of the complexity of the outdoor terrain and the variety of the robot missions, the autonomous mobile robots (AMR) have to carry out missions in unstructured and impossibly predicted environment. The acquisition of accurate environment parameters is crucial for robot path planning. In this article, the idea of mission-plan is introduced on the basis of the path plan. The whole mission is decomposed into several sub-missions during the mission modeling process. And one case-based mission-planning method (CBMP) is applied to filter and optimize the robot's geometric and kinetic parameters for properly configuration. At last, a proving ground is to validate the parameter adaptation of CBMP algorithm.