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 diverse applications. Task allocation, however, remains a critical challenge. For heterogeneous multi-aircraft coalition 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, we propose a distributed task allocation strategy based on a hierarchical architecture. First, a hedonic game-based self-organizing clustering algorithm is developed. Through distributed interactions, it enables cluster self-organization and assigns heterogeneous aircraft 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. It 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 compromising solution quality. Finally, a gliding aircraft case study validates the feasibility of the proposed algorithm with coupled range estimation.

Formation control of fixed-wing aerial vehicles is an important yet rarely addressed problem because of their complex dynamics and various motion constraints, such as nonholonomic and velocity constraints. The guidance-route-based strategy has been demonstrated to be applicable to fixed-wing aircraft. However, it requires a global coordinator and there exists control lag, due to its own natures. For this reason, this paper presents a fully distributed guidance-route-based formation approach to address the aforementioned issues. First, a hop-count scheme is introduced to achieve distributed implementation, in which each aircraft chooses a neighbor with the minimum hop-count as a reference to generate its guidance route using only local information. Next, the model predictive control algorithm is employed to eliminate the control lag and achieve precise formation shape control. In addition, the stall protection and collision avoidance are also considered. Finally, three numerical simulations demonstrate that our proposed approach can implement precise formation shape control of fixed-wing aircraft in a fully distributed manner.