pp. 2063-2079
S&M3307 Research Paper of Special Issue https://doi.org/10.18494/SAM4278 Published: June 30, 2023 Cooperative Formation Control Method for Unmanned Aerial Vehicle Cluster Based on Less Sensor Data [PDF] Qingfeng Xia, Xianzhong Zhou, Dalei Song, and Yuxiang Sun (Received December 6, 2022; Accepted April 20, 2023) Keywords: UAV, formation, consensus, sensor data
The practical cross-regional, rapid, and large-scale deployment of unmanned aerial vehicle (UAV) clusters can effectively be improved by using large UAVs to carry several micro-UAVs for launching operations. In contrast to a UAV taking off from the ground, when multiple micro-UAVs are launched into motion, the UAV body has a nonzero velocity and nonzero attitude, that is, a nonstationary initial state. Combining existing methods for UAV clusters cannot solve the dynamic optimization problem of the group trajectory in a nonstationary initial state. A consensus artificial potential field (APF)-based cooperative method is proposed. This method requires only the position and attitude data of adjacent UAVs, which can reduce sensor data as much as possible. First, a geometric control method and dynamic constraints are used to realize the single-body pose stabilization control in a nonstationary initial state. Second, the consensus control and APF method are combined on the basis of the single-body stabilization control to realize UAV group formation and dynamic collision avoidance with minimum time and position errors. Finally, in the nonstationary initial state, on the basis of the proposed dynamic model and control method, a whole-process simulation of a large UAV carrying 40 micro-UAVs is designed to verify the effectiveness of the proposed method.
Corresponding author: Qingfeng XiaThis work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Qingfeng Xia, Xianzhong Zhou, Dalei Song, and Yuxiang Sun, Cooperative Formation Control Method for Unmanned Aerial Vehicle Cluster Based on Less Sensor Data, Sens. Mater., Vol. 35, No. 6, 2023, p. 2063-2079. |