航空学报 > 2023, Vol. 44 Issue (5): 326539-326539   doi: 10.7827/S1000-6893.2021.26539

固定翼集群无人机空中模拟对接技术

许勇1, 颜鸿涛1(), 贾涛1, 马跃1, 邓泽华1, 刘多能2   

  1. 1.中国空气动力研究与发展中心 空天技术研究所,绵阳 621000
    2.北京流体动力科学研究中心,北京 100011
  • 收稿日期:2021-10-19 修回日期:2021-11-05 接受日期:2022-01-02 出版日期:2023-02-01 发布日期:2023-02-01
  • 通讯作者: 颜鸿涛 E-mail:yanhongtao@cardc.cn
  • 基金资助:
    科技部科技创新2030-重大项目(2020AAA0104801);国家自然科学基金(61903364)

Aerial simulation docking technology of fixed-wing clustering UAVs

Yong XU1, Hongtao YAN1(), Tao JIA1, Yue MA1, Zehua DENG1, Duoneng LIU2   

  1. 1.Aerospace Technology Institute,China Aevodynamics Research and Development Center,Mianyang 621000,China
    2.Beijing Aerohydrodynamic Frontier Research Center,Beijing 100011,China
  • Received:2021-10-19 Revised:2021-11-05 Accepted:2022-01-02 Online:2023-02-01 Published:2023-02-01
  • Contact: Hongtao YAN E-mail:yanhongtao@cardc.cn
  • Supported by:
    Science and Technology Innovation 2030 Major Project(2020AAA0104801);National Natural Science Foundation of China(61903364)

摘要:

自主空中加油技术对于提高无人机(UAV)续航能力,扩大作战半径,增加载荷重量以及提升战略部署等方面有着重要的意义,是未来智能集群无人机系统的必备技术。本文以“无人争锋(UI-STRIVE)”空中握手比赛为背景,对固定翼集群无人机空中模拟对接相关技术及策略进行了研究。首先,针对固定翼集群无人机飞行速度快、编队密集程度高等特点,设计了集群无人机空中对接流程以及基于Dubins路径规划的时间最短的追机方案,并采用非线性制导律进行航路跟踪;其次,基于模拟锥套的先验信息设计了一组弱分类器,并通过级联的方式实现对模拟锥套的快速检测;然后,设计了沿加油机航线方向进行精确对接的策略,并结合无人机姿态先验以及模拟锥套尺寸信息推导了精确对接阶段制导参数解算方法;最后,设计了相应的固定翼集群无人机系统,并以4机编队参加了第2021届“无人争锋”空中握手比赛,参赛结果验证了本文所提出固定翼集群无人机系统以及其技术和策略的可行性。

关键词: 空中加油, 固定翼无人机, 集群无人机, 路径规划, 无人争锋

Abstract:

Autonomous aerial refueling technology is of great significance for improving the endurance of Unmanned Aerial Vehicle (UAV), expanding the combat radius, increasing the weight of the payload, and enhancing strategic deployment. It is an essential technology for future intelligent clustering UAV systems. In this paper, we study the related aerial simulation docking technologies and strategies of the fixed-wing clustering UAVs for the simulation docking race of UI⁃STRIVE. First, considering the characteristics of fast speed and high formation density of the clustering UAVs, we design the aerial simulation docking pipeline of the clustering UAV. The shortest time pursuit scheme is also designed based on the Dubins path, and the nonlinear guidance law is used for route tracking. Second, we design a set of weak classifiers to classify the targets based on the prior information of the simulation drogue, and the detection process is accelerated by cascading these classifiers. Third, for the precise docking stage, we design a strategy for precise docking along the course angle, and compute the visual guidance parameters by integrating the attitudes prior of UAV and the size of the simulation drogue. We also develop a corresponding fixed-wing clustering UAVs system, and participate in the 2021 simulation docking race of UI⁃STRIVE in a formation of 4 UAVs. Results demonstrate the feasibility and reliability of the proposed fixed-wing clustering UAVs system and simulation docking technologies.

Key words: aerial refueling, fixed-wing UAV, clustering UAVs, path planning, UI-STRIVE

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