This paper introduces a collaborative allocation model designed for multiple UAVs and diverse targets in maritime combat situations. The model incorporates factors such as distance, angle, interception rate, and recognition rate to comprehensively represent the UAVs' overall damage advantage against targets. Given the complexity of real-world environments and real-time demands, large-scale UAV swarm missions necessitate swift and effective responses. To address this, the paper proposes a Two-Stage Greedy Auction Algorithm, enabling the rapid and efficient completion of cooperative strike tasks within large-scale UAV swarms while preventing deadlock occurrences. In the initial allocation stage, the entropy weight method is utilized to assess task advantages, ensuring a rational allocation criterion for various metrics during the strike process. Subsequently, to enhance the overall effective strike rate within all constraints, a reassignment algorithm is designed based on effective strike benefit indices and the initial assignment result. Simulation results demonstrate the algorithm's quick and stable running time in small-scale and large-scale scenarios.

中文翻译：

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基于两阶段贪婪拍卖算法的大规模无人机群协同目标分配问题


本文介绍了一种针对海上作战情况下多无人机和不同目标设计的协同分配模型。该模型综合了距离、角度、拦截率、识别率等因素，综合表征无人机对目标的整体毁伤优势。鉴于现实环境的复杂性和实时需求，大规模无人机群任务需要快速有效的响应。针对这一问题，本文提出了一种两阶段贪婪拍卖算法，能够快速高效地完成大规模无人机群内的协同打击任务，同时防止死锁的发生。在初始分配阶段，利用熵权法评估任务优势，保证打击过程中各项指标的合理分配标准。随后，为了提高所有约束下的整体有效罢工率，基于有效罢工效益指数和初始分配结果设计了重新分配算法。仿真结果表明该算法在小规模和大规模场景下均具有快速稳定的运行时间。