{"title":"无人机的3D G-learning","authors":"Shangzhen Luan, Yun Yang, Hainan Wang, Baochang Zhang, Baoguo Yu, Chenglong He","doi":"10.1109/ICIEA.2017.8282976","DOIUrl":null,"url":null,"abstract":"In this paper, we focus on the learning strategy of path planning for Unmanned Aerial Vehicles (UAVs). We propose the G-Learning method to solve the problem of path planning in 3D and optimize the model algorithm. With G-Learning algorithm, the cost matrix can be calculated in real-time and adaptively updated based on the geometric distance and risk information shared with other UAVs. Extensive experimental results validate the effectiveness and feasibility of CGLA for safe navigation of multiple UAVs.","PeriodicalId":443463,"journal":{"name":"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"3D G-learning in UAVs\",\"authors\":\"Shangzhen Luan, Yun Yang, Hainan Wang, Baochang Zhang, Baoguo Yu, Chenglong He\",\"doi\":\"10.1109/ICIEA.2017.8282976\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we focus on the learning strategy of path planning for Unmanned Aerial Vehicles (UAVs). We propose the G-Learning method to solve the problem of path planning in 3D and optimize the model algorithm. With G-Learning algorithm, the cost matrix can be calculated in real-time and adaptively updated based on the geometric distance and risk information shared with other UAVs. Extensive experimental results validate the effectiveness and feasibility of CGLA for safe navigation of multiple UAVs.\",\"PeriodicalId\":443463,\"journal\":{\"name\":\"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIEA.2017.8282976\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2017.8282976","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we focus on the learning strategy of path planning for Unmanned Aerial Vehicles (UAVs). We propose the G-Learning method to solve the problem of path planning in 3D and optimize the model algorithm. With G-Learning algorithm, the cost matrix can be calculated in real-time and adaptively updated based on the geometric distance and risk information shared with other UAVs. Extensive experimental results validate the effectiveness and feasibility of CGLA for safe navigation of multiple UAVs.
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