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| 基于时序约束联盟博弈的异构无人机集群任务分配方法 |
| Task Allocation Method for Heterogeneous UAV Swarms Based on Coalitional Game with Temporal Constraints |
| 投稿时间：2026-08-04 修订日期：2026-08-24 |
| DOI： |
| 中文关键词: 异构无人机集群 任务分配 时序约束 联盟形成博弈 |
| English Keywords:heterogeneous UAV swarm task allocation temporal constraints coalition formation game |
| 基金项目:国家自然科学基金资助项目(62373053) |
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| 中文摘要: |
| 针对异构无人机集群对防御能力未知的地面目标执行“侦察—打击—评估”时序任务时的协同分配问题，提出一种基于时序约束联盟博弈的任务分配算法（Time-Sequenced Coalition Formation Game, TS-CFG）。该方法设计了双层优化框架：外层为基于时序约束的多阶段联盟形成机制，实现时序约束下的自适应联盟形成；内层为引入价值约束、交换及联盟重组操作的联盟优化算法。为验证所提算法的有效性，设计了基础场景、大规模场景和资源稀缺场景三类共150组测试算例，并将所提算法与PS-CBBA算法、IM-GA算法和IM-CFG算法进行性能对比。实验结果表明，TS-CFG在任务完成率、系统总效用、价值约束满足率、收敛迭代次数四项核心指标上的表现均优于对比算法，能够有效解决不同资源条件下的异构无人机集群时序耦合任务分配问题。 |
| English Summary: |
| To address the cooperative allocation problem for heterogeneous UAV swarms executing “Reconnaissance-Strike-Assessment” sequential tasks against ground targets with unknown defense capabilities, we propose a Time-Sequenced Coalition Formation Game (TS-CFG) algorithm. A two-layer optimization framework was constructed: the outer layer employed a multi-stage coalition formation mechanism based on temporal constraints to achieve adaptive coalition formation; the inner layer utilized a coalition optimization algorithm incorporating value constraints, swap operations, and coalition reorganization. To validate the effectiveness of the proposed algorithm, a total of 150 test instances were designed across three categories: basic scenarios, large-scale scenarios, and resource-scarce scenarios. Comparative experiments were conducted with PS-CBBA, IM-GA, and IM-CFG algorithms. Experimental results demonstrate that TS-CFG outperforms the comparison algorithms across four core metrics: task completion rate, total system utility, value constraint satisfaction rate, and number of convergence iterations. The algorithm effectively resolves the time-coupled task allocation problem for heterogeneous UAV swarms under varying resource conditions. |
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