{"title":"协作无人驾驶汽车的位置感知、灵活任务管理","authors":"M. Wang, Yang Zhao, A. Doboli","doi":"10.1109/AHS.2009.67","DOIUrl":null,"url":null,"abstract":"Unmanned Autonomous Vehicles (UAVs) are emerging as a breakthrough concept in technology. A main challenge related to UAV control is devising flexible strategies with predictable performance in hard-to-predict conditions. This paper proposes an approach to performance predictive collaborative control of UAVs operating in environments with fixed targets. The paper offers detailed experimental insight on the quality, scalability and computational complexity of the proposed method.","PeriodicalId":318989,"journal":{"name":"2009 NASA/ESA Conference on Adaptive Hardware and Systems","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Location-Aware, Flexible Task Management for Collaborating Unmanned Autonomous Vehicles\",\"authors\":\"M. Wang, Yang Zhao, A. Doboli\",\"doi\":\"10.1109/AHS.2009.67\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unmanned Autonomous Vehicles (UAVs) are emerging as a breakthrough concept in technology. A main challenge related to UAV control is devising flexible strategies with predictable performance in hard-to-predict conditions. This paper proposes an approach to performance predictive collaborative control of UAVs operating in environments with fixed targets. The paper offers detailed experimental insight on the quality, scalability and computational complexity of the proposed method.\",\"PeriodicalId\":318989,\"journal\":{\"name\":\"2009 NASA/ESA Conference on Adaptive Hardware and Systems\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 NASA/ESA Conference on Adaptive Hardware and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AHS.2009.67\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 NASA/ESA Conference on Adaptive Hardware and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AHS.2009.67","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Location-Aware, Flexible Task Management for Collaborating Unmanned Autonomous Vehicles
Unmanned Autonomous Vehicles (UAVs) are emerging as a breakthrough concept in technology. A main challenge related to UAV control is devising flexible strategies with predictable performance in hard-to-predict conditions. This paper proposes an approach to performance predictive collaborative control of UAVs operating in environments with fixed targets. The paper offers detailed experimental insight on the quality, scalability and computational complexity of the proposed method.