With the rapid advancement of UAV technology, the problem of UAV coalition formation has become a hotspot. Therefore, designing task-driven multi-UAV coalition formation mechanism has become a challenging problem. However, existing coalition formation mechanisms suffer from low relevance between UAVs and task requirements, resulting in overall low coalition utility and unstable coalition structures. To address these problems, this paper proposed a novel multi-UAV coalition network collaborative task completion model, considering both coalition work capacity and task-requirement relationships. This model stimulated the formation of coalitions that match task requirements by using a revenue function based on the coalition's revenue threshold. Subsequently, an algorithm for coalition formation based on marginal utility was proposed. Specifically, the algorithm utilized Shapley value to achieve fair utility distribution within the coalition, evaluated coalition values based on marginal utility preference order, and achieved stable coalition partition through a limited number of iterations. Additionally, we theoretically proved that this algorithm has Nash equilibrium solution. Finally, experimental results demonstrated that the proposed algorithm, compared to currently classical algorithms, not only forms more stable coalitions but also further enhances the overall utility of coalitions effectively.

翻译：随着无人机技术的快速发展，无人机联盟形成问题已成为研究热点。因此，设计面向任务驱动的多无人机联盟形成机制成为具有挑战性的问题。然而，现有联盟形成机制普遍存在无人机与任务需求关联度低的问题，导致联盟总体效用偏低且联盟结构不稳定。针对这些问题，本文提出了一种新型多无人机联盟网络协同任务完成模型，综合考虑了联盟工作能力与任务需求关系。该模型通过基于联盟收益阈值的收益函数，激励形成与任务需求匹配的联盟。随后，提出一种基于边际效用的联盟形成算法。具体而言，该算法利用沙普利值实现联盟内部公平效用分配，基于边际效用偏好顺序评估联盟价值，并通过有限次迭代实现稳定联盟划分。此外，我们从理论上证明了该算法具有纳什均衡解。实验结果表明，与当前经典算法相比，所提算法不仅能形成更稳定的联盟，还能有效提升联盟的整体效用。