Abstract: Through local information exchange and collaborative decision-making, distributed unmanned swarms are capable of accomplishing complex tasks with high efficiency and autonomy. It demonstrates considerable potential for di-verse applications. Task allocation, however, remains a critical challenge. For heterogeneous multi-aircraft coali-tion formation, the main difficulties lie in high computational complexity, substantial communication overhead, and the trade-off between solution quality and efficiency. To address these issues, this paper proposes a distributed task allocation strategy based on a hierarchical architecture. First, a hedonic game-based self-organizing cluster-ing algorithm is developed. Through distributed interactions, it enables cluster self-organization and assigns di-verse aircraft in the heterogeneous system to task clusters according to their requirements. Second, the Hungarian method is extended to solve the coalition formation problem within each cluster. An implicit consensus mechanism and a task node splitting mechanism are introduced, allowing effective matching of aircraft to tasks. In this way, the specific requirements of different tasks for each type of aircraft are satisfied. Moreover, a cost estimation method is designed by integrating the aircraft dynamics model. This achieves a tight coupling between task allocation and trajectory planning, which ensures the dynamic feasibility of the allocation results. Simulation results show that the hierarchical strategy significantly improves scalability while reducing communication overhead without compromis-ing solution quality. Finally, a gliding aircraft case study validates the feasibility of the proposed algorithm with cou-pled range estimation.

Key words: swarm collaboration, distributed task allocation, hedonic game clustering, Hungarian algorithm, trajectory planning