一个基于共识的双层集群捆绑算法,用于动态异质的多UAV多任务分配
Yichao Wang1, Chunjiang Wang2, Shuangyin Ren3
1Department of Systems Engineering, Academy of Military Sciences, Beijing 100000, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了基于共识的两级集群捆绑算法 (TLC-CBBA),以改善多个无人机 (UAV) 的分布式任务分配. 在动态,异质的环境中,TLC-CBBA提高了通信效率和任务匹配.
科学领域:
- 机器人技术和自主系统
- 分布式人工智能 分布式人工智能
- 网络化系统 网络化系统
背景情况:
- 多个无人机 (UAV) 的合作任务面临着动态通信和资源异质性的挑战,影响分布式任务分配.
- 现有的方法,如基于共识的捆绑算法 (CBBA),在大规模,异构的场景中难以扩展和适应,导致高通信开销和低于最佳的任务与资源匹配.
- 计算成本的增加是由于分散的多无人机系统中通信效率低下和任务与资源对齐不佳造成的.
研究的目的:
- 引入一种新的基于共识的双层集群捆绑算法 (TLC-CBBA),旨在解决动态和异质的多UAV任务分配中现有算法的局限性.
- 在复杂的多无人机合作任务中提高通信效率,任务与资源的匹配,以及整体性能.
- 为大规模,异质无人机团队提高去中心化任务分配框架的可扩展性和稳定性.
主要方法:
- 实施了双层集群方法:第一层使用图形理论的中心性和最短路径距离进行基于拓的分组,第二层使用资源平衡,距离意识的K-medoids算法进行子组形成.
- 将无人机资源异质性和空间接近性集成到集群过程中,以确保子组的紧性和资源的均衡分配.
- 在每个子组内执行基于共识的捆绑算法 (CBBA),用于本地任务分配,集群中心协调集群间的通信,以实现全球一致性.
主要成果:
- 在关键指标中,TLC-CBBA显著优于标准CBBA及其变体 (DMCHBA,G-CBBA,集群CBBA).
- 在通信效率,总任务得分和运行时间方面取得了实质性的改进.
- 显著性分析证实了TLC-CBBA在各种任务场景和不同的UAV团队规模中的有效性.
结论:
- 拟议的TLC-CBBA有效地解决了动态通信拓和多无人机任务分配中的资源异质性的挑战.
- TLC-CBBA表现出强大的稳定性和可扩展性,使其适合在动态环境中进行复杂,大规模和异质的多无人机操作.
- 这种新的方法为合作无人机系统中分散的任务分配提供了更高效和有效的解决方案.
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