TY - JOUR
T1 - Hierarchical Distributed Strategy for Autonomous UAV Swarm Formation Aggregation
AU - Zhang, Tao
AU - Yu, Dengxiu
AU - Cheong, Kang Hao
AU - Liu, Yanjun
AU - Wang, Zhen
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - bilevel optimization
KW - collision-avoidance
KW - Hierarchical distributed strategy
KW - predefined time control
UR - http://www.scopus.com/inward/record.url?scp=105006741835&partnerID=8YFLogxK
U2 - 10.1109/TVT.2025.3572915
DO - 10.1109/TVT.2025.3572915
M3 - 文章
AN - SCOPUS:105006741835
SN - 0018-9545
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
ER -