{"title":"基于确定性采样的机器人运动规划方法","authors":"Yulan Hu, Qisong Zhang","doi":"10.1109/ICINIS.2010.67","DOIUrl":null,"url":null,"abstract":"With the robotic application of environmental complexity increasing, the traditional motion planning can not overcome the obstacles in the uncertainty space of the model and describe the problem, especially in an unknown environment, subject to environmental restrictions on the amount of information, the traditional sports planning algorithm may not run. Sampling-based motion planning is only through the configuration space of the sampling points to obtain obstacle collision detection information, to avoid running space, modeling, fully applicable to complex and unknown environment.","PeriodicalId":319379,"journal":{"name":"2010 Third International Conference on Intelligent Networks and Intelligent Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robot Motion Planning Method Based on Deterministic Sampling\",\"authors\":\"Yulan Hu, Qisong Zhang\",\"doi\":\"10.1109/ICINIS.2010.67\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the robotic application of environmental complexity increasing, the traditional motion planning can not overcome the obstacles in the uncertainty space of the model and describe the problem, especially in an unknown environment, subject to environmental restrictions on the amount of information, the traditional sports planning algorithm may not run. Sampling-based motion planning is only through the configuration space of the sampling points to obtain obstacle collision detection information, to avoid running space, modeling, fully applicable to complex and unknown environment.\",\"PeriodicalId\":319379,\"journal\":{\"name\":\"2010 Third International Conference on Intelligent Networks and Intelligent Systems\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Third International Conference on Intelligent Networks and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICINIS.2010.67\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Third International Conference on Intelligent Networks and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINIS.2010.67","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robot Motion Planning Method Based on Deterministic Sampling
With the robotic application of environmental complexity increasing, the traditional motion planning can not overcome the obstacles in the uncertainty space of the model and describe the problem, especially in an unknown environment, subject to environmental restrictions on the amount of information, the traditional sports planning algorithm may not run. Sampling-based motion planning is only through the configuration space of the sampling points to obtain obstacle collision detection information, to avoid running space, modeling, fully applicable to complex and unknown environment.