{"title":"基于神经网络和遗传算法的多无人机协同覆盖侦察","authors":"Chang Liu, Wen-jun Xie, Peng Zhang, Qing Guo, Doujian Ding","doi":"10.1145/3341069.3342968","DOIUrl":null,"url":null,"abstract":"Aiming at the problem of multi-UAVs cooperative coverage reconnaissance mission planning, a planning method combining neural network and genetic algorithm is proposed. Firstly, the relative position relationship between multiple UAVs, the position relationship between each UAV and the boundary of the target area and the motion performance of each UAV are taken as inputs of the neural network, and the output is rough path of each UAV. Then, the weights and thresholds of neural network are optimized by using genetic algorithm, and the optimal paths of multi-UAVs cooperative regional reconnaissance is solved. The simulation results show that the method can not only enable UAVs to learn reconnaissance rules autonomously, but also plan the cooperative reconnaissance paths of each UAV, achieve effective coverage of the target area, and have good reconnaissance efficiency.","PeriodicalId":411198,"journal":{"name":"Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference","volume":"85 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Multi-UAVs Cooperative Coverage Reconnaissance with Neural Network and Genetic Algorithm\",\"authors\":\"Chang Liu, Wen-jun Xie, Peng Zhang, Qing Guo, Doujian Ding\",\"doi\":\"10.1145/3341069.3342968\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the problem of multi-UAVs cooperative coverage reconnaissance mission planning, a planning method combining neural network and genetic algorithm is proposed. Firstly, the relative position relationship between multiple UAVs, the position relationship between each UAV and the boundary of the target area and the motion performance of each UAV are taken as inputs of the neural network, and the output is rough path of each UAV. Then, the weights and thresholds of neural network are optimized by using genetic algorithm, and the optimal paths of multi-UAVs cooperative regional reconnaissance is solved. The simulation results show that the method can not only enable UAVs to learn reconnaissance rules autonomously, but also plan the cooperative reconnaissance paths of each UAV, achieve effective coverage of the target area, and have good reconnaissance efficiency.\",\"PeriodicalId\":411198,\"journal\":{\"name\":\"Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference\",\"volume\":\"85 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3341069.3342968\",\"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 2019 3rd High Performance Computing and Cluster Technologies Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3341069.3342968","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-UAVs Cooperative Coverage Reconnaissance with Neural Network and Genetic Algorithm
Aiming at the problem of multi-UAVs cooperative coverage reconnaissance mission planning, a planning method combining neural network and genetic algorithm is proposed. Firstly, the relative position relationship between multiple UAVs, the position relationship between each UAV and the boundary of the target area and the motion performance of each UAV are taken as inputs of the neural network, and the output is rough path of each UAV. Then, the weights and thresholds of neural network are optimized by using genetic algorithm, and the optimal paths of multi-UAVs cooperative regional reconnaissance is solved. The simulation results show that the method can not only enable UAVs to learn reconnaissance rules autonomously, but also plan the cooperative reconnaissance paths of each UAV, achieve effective coverage of the target area, and have good reconnaissance efficiency.