Hierarchical Distributed Strategy for Autonomous UAV Swarm Formation Aggregation

This study proposes a Hierarchical Distributed framework for Decision-making and collaborative Control (HDDC) to enable self-organized formation in Unmanned Aerial Vehicle (UAV) swarms. Although hierarchical theory-based methods for distributed construction and formation maintenance are well-documented, they typically require frequent sharing of global information, which limits scalability and efficiency in decentralized environments. To address these challenges, we introduce a fully distributed hierarchical framework by decomposing the behavioral logic of self-organized swarm actions. The framework operates in two layers: the decision-making layer, where a dual transformation allows each UAV to compute optimal aggregation positions using only local information; and the control strategy layer, which ensures predefined-time convergence to assigned positions. This layered approach achieves efficient and scalable swarm formation without centralized optimization. Additionally, by incorporating individual velocity directions, collision avoidance is enhanced, leading to a reduced frequency of strategy activation and improved smoothness in the formation process. The control method also avoids singularities, ensuring robust, collision-free operation within user-defined timeframes. Finally, the effectiveness of the proposed approach is validated through multiple simulation examples.