大规模的多无人机任务分配通过一个中心性驱动的负载意识的自适应共识捆绑算法生物模拟队伍协调
Weifei Gan1, Hongxuan Xu1, Yunwei Bai1
1School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai 201209, China.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
这项研究介绍了CLAC-CBBA,这是一种在大型无人机群中分配任务的新算法. 它通过使用自适应集群和负载平衡来提高可扩展性和稳定性,提高任务效率.
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
- 群集情报 群集情报 群集情报
背景情况:
- 大型多无人机系统在分布式任务分配方面面临挑战,原因是时间变化的通信和异质平台.
- 经典方法往往导致过度的消息传递和低于最佳的任务资源匹配在复杂的群体操作.
研究的目的:
- 提出CLAC-CBBA,一种增强的基于共识的捆绑算法 (CBBA),用于在大型异质无人机群中进行可扩展和强大的任务分配.
- 通过整合群体式的自我组织和负载意识自我调节来改进现有的分布式任务分配方法.
主要方法:
- CLAC-CBBA采用以中心性为基础的自适应集群重构 (CenCluster) 来分割网络并减少通信.
- 负载感知集群自我调节 (LCSR) 机制使用K-medoids集群和实时负载平衡来进行集群动态调整.
- 为了实现全球一致性,CBBA投标在集群内进行本地化,并通过和代表同步获胜者/投标.
主要成果:
- 模拟表明,与CBBA和其他变体相比,CLAC-CBBA显著减少了通信开销和运行时间.
- 该算法在各种网络密度和群体大小的总任务得分中实现了统计学上显著的改进.
- 在大规模异质无人机任务分配场景中,CLAC-CBBA表现出增强的可扩展性和稳定性.
结论:
- 在大型无人机群中,CLAC-CBBA提供了一个仿生,分散的方法来分配任务.
- 拟议的方法有效地平衡了复杂的多无人机任务的可扩展性,稳定性和分配最佳性.
- CLAC-CBBA代表了对异质机器人群的分布式任务分配的重大进步.
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