{"title":"A new method of the shortest path planning for unmanned aerial vehicles","authors":"Darong Huang, Dongjie Zhao, Ling Zhao","doi":"10.1109/DDCLS.2017.8068140","DOIUrl":null,"url":null,"abstract":"In this paper, the optimal route and deployment scheme are designed to ensure the shortest retention time for unmanned aerial vehicles (UAV) in risk area. Firstly, according to the known data and radar scanning range, the regional distribution map of target grope and base are obtained, respectively. Secondly, based on the different scanning bandwidth of loads, target points are classified by using clustering analysis. This makes the target points fall on the scanning bandwidth of UAV as far as possible, accordingly reducing the UAV's scanning times. This problem can be regarded as a travelling salesman problem in radar scanning range. Finally, the deployment result and locally optimal route are obtained by 0–1 programming in LINGO. Furthermore, particle swarm optimization is used to improve the local optimal path and the global optimal route can then be generated.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 6th Data Driven Control and Learning Systems (DDCLS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DDCLS.2017.8068140","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
In this paper, the optimal route and deployment scheme are designed to ensure the shortest retention time for unmanned aerial vehicles (UAV) in risk area. Firstly, according to the known data and radar scanning range, the regional distribution map of target grope and base are obtained, respectively. Secondly, based on the different scanning bandwidth of loads, target points are classified by using clustering analysis. This makes the target points fall on the scanning bandwidth of UAV as far as possible, accordingly reducing the UAV's scanning times. This problem can be regarded as a travelling salesman problem in radar scanning range. Finally, the deployment result and locally optimal route are obtained by 0–1 programming in LINGO. Furthermore, particle swarm optimization is used to improve the local optimal path and the global optimal route can then be generated.
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