Obstacle avoidance control for cluster-based unmanned aerial vehicle formation with multiple constraints - Wenjie Zhou; Shuo Chen; Jiacheng Li; Chenjun Liu; Wenjun Luo; Jason J.R. Liu (Franklin Open, 2025)
In this paper, we conduct research on coordinated obstacle avoidance for multiple Unmanned Aerial Vehicles (UAVs). In this scenario, it is known that the existing communication and computing capabilities and resources severely limit the scalability of the formation. To solve this problem, a cluster-based strategy is proposed to divide numerous UAVs into multiple sub-clusters for coordinated control, which reduces the communication and computing burden of the system. Then, the fixed-wing UAV formation is selected as the control target, emphasizing the practical constraints in their flight characteristics, such as the inability to hover and control restrictions. Furthermore, the artificial potential field (APF) method is introduced into the formation control under the cluster-based strategy, ensuring that the UAVs fly safely in a complex and changeable obstacle environment. Subsequently, numerical simulations are used to verify the collision-free flight of the formation in obstacle-free, static and dynamic obstacle environments. Our simulation results show that, with the proposed control strategy, the formation successfully avoids collisions with obstacles and internal collisions within the formation, ensuring the safety of all UAVs. Overall, our obstacle avoidance control strategy offers a theoretical algorithmic foundation for multi-UAV systems, showcasing promising practical applications.