{"title":"基于遗传模拟退火算法的无人机航路规划","authors":"Hao Meng, Guizhou Xin","doi":"10.1109/ICMA.2010.5589035","DOIUrl":null,"url":null,"abstract":"For the local minimum problem of genetic algorithm in unmanned aerial vehicle route planning, the Metropolis acceptance criteria of simulated annealing algorithm is incorporated into the genetic algorithm in this paper. In the algorithm, the original DEM (Digital Elevation Map) is processed into the smallest threat surface. In order to obtain a more smooth surface of flight, the original digital elevation map are processed in four directions, and then the genetic simulated annealing algorithm is used for three-dimensional route planning in this minimal threat surface. In addition, the distance between the track segment and threats are converted into elevation values and the value is added to the fitness function, a smaller code space was proposed at the same time. The simulation results show that the Genetic Simulated Annealing Algorithm proposed is good.","PeriodicalId":145608,"journal":{"name":"2010 IEEE International Conference on Mechatronics and Automation","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"UAV route planning based on the genetic simulated annealing algorithm\",\"authors\":\"Hao Meng, Guizhou Xin\",\"doi\":\"10.1109/ICMA.2010.5589035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For the local minimum problem of genetic algorithm in unmanned aerial vehicle route planning, the Metropolis acceptance criteria of simulated annealing algorithm is incorporated into the genetic algorithm in this paper. In the algorithm, the original DEM (Digital Elevation Map) is processed into the smallest threat surface. In order to obtain a more smooth surface of flight, the original digital elevation map are processed in four directions, and then the genetic simulated annealing algorithm is used for three-dimensional route planning in this minimal threat surface. In addition, the distance between the track segment and threats are converted into elevation values and the value is added to the fitness function, a smaller code space was proposed at the same time. The simulation results show that the Genetic Simulated Annealing Algorithm proposed is good.\",\"PeriodicalId\":145608,\"journal\":{\"name\":\"2010 IEEE International Conference on Mechatronics and Automation\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Conference on Mechatronics and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMA.2010.5589035\",\"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 IEEE International Conference on Mechatronics and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMA.2010.5589035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
UAV route planning based on the genetic simulated annealing algorithm
For the local minimum problem of genetic algorithm in unmanned aerial vehicle route planning, the Metropolis acceptance criteria of simulated annealing algorithm is incorporated into the genetic algorithm in this paper. In the algorithm, the original DEM (Digital Elevation Map) is processed into the smallest threat surface. In order to obtain a more smooth surface of flight, the original digital elevation map are processed in four directions, and then the genetic simulated annealing algorithm is used for three-dimensional route planning in this minimal threat surface. In addition, the distance between the track segment and threats are converted into elevation values and the value is added to the fitness function, a smaller code space was proposed at the same time. The simulation results show that the Genetic Simulated Annealing Algorithm proposed is good.