Collaborative Task Allocation for Large-Scale Heterogeneous AAV Swarm: A Hierarchical Coalition Formation Game Method

With the increasing complexity and volume of task demands in high-concurrency IoT applications, UAV swarm systems must scale up to meet these requirements, inevitably introduces challenges related to computational efficiency and performance, as well as a lack of theoretical analysis on solution convergence and optimality. To address these issues, this paper proposes a novel optimization model for coalition formation and a hierarchical task allocation method. The approach combines a semi-centralized clustering with distributed coalition formation scheme, where multi-dimensional contribution clustering decomposes tasks and platforms for complexity reduction. Moreover, by modeling sub-cluster allocation as an Overlapping Coalition Formation (OCF) game, our approach integrates marginal utility criteria with search algorithms featuring adaptive resource matching and random exit mechanisms to accelerate the search and avoid suboptimal solutions. Theoretical proof confirms the Nash equilibrium attainment through iterative coalition adjustments while ensuring low complexity. Simulation results show that the method significantly reduces decision-making complexity while ensuring task utility and overall coalition efficiency, demonstrating its effectiveness in UAV swarm-based civilian disaster relief systems.