In recent years, drones have been widely used. The method of autonomous cooperative positioning of multiple drones to complete tasks is also widely used in various fields, such as search and rescue, surveillance, military missions, and so on. Based on this, this paper constructs a two-dimensional planar cone formation composed of a group of UAVs from the point of view of a circular geometric positioning model. This paper breaks this formation into a combination of  regular hexagon drone formation and analyzes the regular hexagon UAV formation. Firstly, the signals emitted by FY00, FY01, and a known numbered drone are used to predict the position of the remaining drones. Then, the model is extended and expanded to predict the position of the remaining drones by constructing the electromagnetic signals emitted by FY00, FY01, and three unknown numbered drones. On this basis, considering the problem of poor electromagnetic signal reception caused by attitude deviation caused by body motion of UAV in real life, the PID algorithm is used in this paper to correct relative position and relative speed, and further improve the geometric autonomous cooperative positioning model of UAV in daily life. The results show that the study of UAV formation based on a geometric model has the advantages of intuitiveness, mathematical controllability, and simplification, which are helpful in understanding, analyzing, and solving formation-related problems.

Sisi Zheng. Research on the autonomous cooperative positioning scheme of UAV formation. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 2: 102-108. https://doi.org/10.25236/AJCIS.2024.070214.

[1] Huang Yi, Ma Wenhui, Li Jiacheng et al. Intelligent obstacle avoidance control method for UAV formation in unknown environment [J/OL]. Journal of Tsinghua University (Natural Science Edition):1-12[2023-10-08].

[2] Li Haisi. Research on radiation source location and path planning technology based on multiple drones [D]. Strategic Support Force Information Engineering University, 2022.

[3] Ma Dejin, Chen Yang, Zhu Zhenhua. Multi-UAV Collaborative path planning for River inspection [J/OL]. Control Engineering:1-8 [2023-10-08].

[4] Zhu Xiumin. Research on high energy efficiency 3D Position deployment and path planning of UAV array [D]. Shandong Normal University, 2023.

[5] Xie Tao, Guo Jiansheng, Zhang Xiaofeng, et al. Real-time task assignment of multi-UAV based on the CNP method [J/OL]. Electro-optics and Control:1-8 [2023-10-08].

[6] Niu Yuan, Cui Zhihua, Bai Hui. Task Assignment Optimization Problem in heterogeneous multi-UAV [J]. Small and Microcomputer Systems,2023,44(08):1720-1727. (in Chinese)

[7] ZHAO Junmin, He Haozhe, Wang Shaoqi, et al. Joint track planning for multi-UAV target tracking and obstacle avoidance in complex environments [J]. Journal of Military Engineering, 2023, 44(09): 2685-2696. (in Chinese)

[8] Cheng Jie, Zheng Yuan, Li Chenglong, et al. Multi-aircraft Collaborative track planning Algorithm for ultra-low altitude logistics scenario [J/OL]. Journal of System Simulation:1-16 [2023-10-08].

[9] Zhou Hailong, Zhou Shuixing. Derivation of transformation Matrix in three-dimensional space solid-beam Element Method [J]. Highway Engineering,2014,39(06):147-151.

[10] Ma Shuai, Wang Yuyou, Yang Li et al. Control Strategy of Mine Electromechanical Equipment based on Improved Particle Swarm Optimization PID [J]. Industrial Instrumentation & Automation Equipment,2023(05):115-119. (in Chinese)