{"title":"基于抽样的规划者可达性分析","authors":"Roland Geraerts, M. Overmars","doi":"10.1109/ROBOT.2005.1570152","DOIUrl":null,"url":null,"abstract":"The last decade, sampling based planners like the Probabilistic Roadmap Method have proved to be successful in solving complex motion planning problems. We give a reachability based analysis for these planners which leads to a better understanding of the success of the approach and enhancements of the techniques suggested. This also enables us to study the effect of using new local planners.","PeriodicalId":350878,"journal":{"name":"Proceedings of the 2005 IEEE International Conference on Robotics and Automation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":"{\"title\":\"Reachability Analysis of Sampling Based Planners\",\"authors\":\"Roland Geraerts, M. Overmars\",\"doi\":\"10.1109/ROBOT.2005.1570152\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The last decade, sampling based planners like the Probabilistic Roadmap Method have proved to be successful in solving complex motion planning problems. We give a reachability based analysis for these planners which leads to a better understanding of the success of the approach and enhancements of the techniques suggested. This also enables us to study the effect of using new local planners.\",\"PeriodicalId\":350878,\"journal\":{\"name\":\"Proceedings of the 2005 IEEE International Conference on Robotics and Automation\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"32\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2005 IEEE International Conference on Robotics and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROBOT.2005.1570152\",\"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 2005 IEEE International Conference on Robotics and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROBOT.2005.1570152","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The last decade, sampling based planners like the Probabilistic Roadmap Method have proved to be successful in solving complex motion planning problems. We give a reachability based analysis for these planners which leads to a better understanding of the success of the approach and enhancements of the techniques suggested. This also enables us to study the effect of using new local planners.