{"title":"四旋翼尾翼无人机的三维轨迹优化:穿越给定航路点","authors":"Mingyue Fan;Fangfang Xie;Tingwei Ji;Yao Zheng","doi":"10.1109/TAES.2025.3531845","DOIUrl":null,"url":null,"abstract":"Given the evolving application scenarios of current fixed-wing autonomous aerial vehicles (AAVs), it is necessary for AAVs to possess agile and rapid 3-D flight capabilities. Typically, the trajectory of a tail-sitter is generated separately for vertical and level flights. This limits the tail-sitter's ability to move in a 3-D airspace and makes it difficult to establish a smooth transition between vertical and level flights. In this article, a 3-D trajectory optimization method is proposed for quadrotor tail-sitters. Especially, the differential dynamics constraints are eliminated when generating the trajectory of the tail-sitter by utilizing the differential flatness method. In addition, the temporal parameters of the trajectory are generated using the State-of-the-Art trajectory generation method called MINCO (minimum control). Subsequently, we convert the speed constraint on the vehicle into a soft constraint by discretizing the trajectory in time. This increases the likelihood that the control input limits are satisfied, and the trajectory is feasible. Then, we utilize a kind of model-predictive control method to track trajectories. Even if restricting the tail-sitter's motion to a 2-D horizontal plane, the solutions still outperform those of the L1 Guidance Law and Dubins path.","PeriodicalId":13157,"journal":{"name":"IEEE Transactions on Aerospace and Electronic Systems","volume":"61 3","pages":"6987-7005"},"PeriodicalIF":5.7000,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Three-Dimensional Trajectory Optimization for Quadrotor Tail-Sitter AAVs: Traversing Through Given Waypoints\",\"authors\":\"Mingyue Fan;Fangfang Xie;Tingwei Ji;Yao Zheng\",\"doi\":\"10.1109/TAES.2025.3531845\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Given the evolving application scenarios of current fixed-wing autonomous aerial vehicles (AAVs), it is necessary for AAVs to possess agile and rapid 3-D flight capabilities. Typically, the trajectory of a tail-sitter is generated separately for vertical and level flights. This limits the tail-sitter's ability to move in a 3-D airspace and makes it difficult to establish a smooth transition between vertical and level flights. In this article, a 3-D trajectory optimization method is proposed for quadrotor tail-sitters. Especially, the differential dynamics constraints are eliminated when generating the trajectory of the tail-sitter by utilizing the differential flatness method. In addition, the temporal parameters of the trajectory are generated using the State-of-the-Art trajectory generation method called MINCO (minimum control). Subsequently, we convert the speed constraint on the vehicle into a soft constraint by discretizing the trajectory in time. This increases the likelihood that the control input limits are satisfied, and the trajectory is feasible. Then, we utilize a kind of model-predictive control method to track trajectories. Even if restricting the tail-sitter's motion to a 2-D horizontal plane, the solutions still outperform those of the L1 Guidance Law and Dubins path.\",\"PeriodicalId\":13157,\"journal\":{\"name\":\"IEEE Transactions on Aerospace and Electronic Systems\",\"volume\":\"61 3\",\"pages\":\"6987-7005\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2025-01-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Aerospace and Electronic Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10847903/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, AEROSPACE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Aerospace and Electronic Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10847903/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
Three-Dimensional Trajectory Optimization for Quadrotor Tail-Sitter AAVs: Traversing Through Given Waypoints
Given the evolving application scenarios of current fixed-wing autonomous aerial vehicles (AAVs), it is necessary for AAVs to possess agile and rapid 3-D flight capabilities. Typically, the trajectory of a tail-sitter is generated separately for vertical and level flights. This limits the tail-sitter's ability to move in a 3-D airspace and makes it difficult to establish a smooth transition between vertical and level flights. In this article, a 3-D trajectory optimization method is proposed for quadrotor tail-sitters. Especially, the differential dynamics constraints are eliminated when generating the trajectory of the tail-sitter by utilizing the differential flatness method. In addition, the temporal parameters of the trajectory are generated using the State-of-the-Art trajectory generation method called MINCO (minimum control). Subsequently, we convert the speed constraint on the vehicle into a soft constraint by discretizing the trajectory in time. This increases the likelihood that the control input limits are satisfied, and the trajectory is feasible. Then, we utilize a kind of model-predictive control method to track trajectories. Even if restricting the tail-sitter's motion to a 2-D horizontal plane, the solutions still outperform those of the L1 Guidance Law and Dubins path.
期刊介绍:
IEEE Transactions on Aerospace and Electronic Systems focuses on the organization, design, development, integration, and operation of complex systems for space, air, ocean, or ground environment. These systems include, but are not limited to, navigation, avionics, spacecraft, aerospace power, radar, sonar, telemetry, defense, transportation, automated testing, and command and control.