Zhuofan Xu, Ruixuan Wei, Qirui Zhang, Kai Zhou, Renke He
{"title":"Obstacle avoidance algorithm for UAVs in unknown environment based on distributional perception and decision making","authors":"Zhuofan Xu, Ruixuan Wei, Qirui Zhang, Kai Zhou, Renke He","doi":"10.1109/CGNCC.2016.7828936","DOIUrl":null,"url":null,"abstract":"Guaranteeing the flight with safety and efficiency for UAVs in unknown environment is one of the most important problems for researchers. In this paper, a novel obstacle avoidance algorithm for UAVs in unknown environment based on distributional perception and decision making is proposed, where the obstacle avoidance problem is divided into two parts: distributional decision making and global decision making. The global optimal obstacle avoidance path is get with mission completed safely at last. The simulation shows the algorithm proposed is capable of solving the obstacle avoidance problem for UAVs in the unknown environment and is valuable to improve the perception and decision making ability of UAVs.","PeriodicalId":426650,"journal":{"name":"2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CGNCC.2016.7828936","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Guaranteeing the flight with safety and efficiency for UAVs in unknown environment is one of the most important problems for researchers. In this paper, a novel obstacle avoidance algorithm for UAVs in unknown environment based on distributional perception and decision making is proposed, where the obstacle avoidance problem is divided into two parts: distributional decision making and global decision making. The global optimal obstacle avoidance path is get with mission completed safely at last. The simulation shows the algorithm proposed is capable of solving the obstacle avoidance problem for UAVs in the unknown environment and is valuable to improve the perception and decision making ability of UAVs.