Jianhua Zhang, S. Dou, Yang Li, Xueli Wu, Ran Zhen, Kai Gao, Dongwen Zhang
{"title":"Research on UAV Conflict Resolution Algorithm Based on Improved Particle Swam Algorithm","authors":"Jianhua Zhang, S. Dou, Yang Li, Xueli Wu, Ran Zhen, Kai Gao, Dongwen Zhang","doi":"10.1109/ICMIC.2018.8529975","DOIUrl":null,"url":null,"abstract":"This paper is based on the safety method of particle swarm, and integrates the classical particle swarm algorithm and simulated annealing particle swarm algorithm. This article combines the classic particle swarm algorithm and simulated annealing particle swarm optimization algorithm, the advantages of can improve the UAV mission in a complex spatial environment safety, reduce the unmanned aerial vehicle unable to effectively avoid the ground fixed obstacles in the process of flying and air other aircraft due to the risk of collision, made it possible to unmanned aerial vehicles and man-machine Shared airspace, able to perform various tasks for unmanned aerial vehicle safely and successfully provide effective protection.","PeriodicalId":262938,"journal":{"name":"2018 10th International Conference on Modelling, Identification and Control (ICMIC)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 10th International Conference on Modelling, Identification and Control (ICMIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMIC.2018.8529975","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper is based on the safety method of particle swarm, and integrates the classical particle swarm algorithm and simulated annealing particle swarm algorithm. This article combines the classic particle swarm algorithm and simulated annealing particle swarm optimization algorithm, the advantages of can improve the UAV mission in a complex spatial environment safety, reduce the unmanned aerial vehicle unable to effectively avoid the ground fixed obstacles in the process of flying and air other aircraft due to the risk of collision, made it possible to unmanned aerial vehicles and man-machine Shared airspace, able to perform various tasks for unmanned aerial vehicle safely and successfully provide effective protection.