面对无人机高损耗，新算法保障通信网络持续稳定运行。
Robust Geospatial Coordination of Multi-Agent Communications Networks Under Attrition
- 用物理启发的拓扑算法动态调整无人机位置以维持连接
- 在100任务500架无人机场景下保持99.9%以上任务可用性
- 适合应急救援等极端环境下需要高可靠通信的多机系统
在野火等极端环境下的应急响应中，需要具备鲁棒性和高带宽的通信骨干网络。尽管自主飞行集群可建立自组网提供连通性，但此类场景中单个节点损失风险极高，常导致网络分裂和任务关键性中断。为此，我们提出并形式化了抗损鲁棒任务组网问题（RTNUA），将多机器人系统的连通性维护扩展至主动冗余与损毁恢复。随后提出物理信息驱动的多智能体网络鲁棒部署方法（ΦIREMAN），一种利用物理启发势场的拓扑算法。评估表明，ΦIREMAN持续优于基线，在最多100项任务、500架无人机的仿真中仍能保持超过99.9%的任务可用性，验证了其有效性和可扩展性。
原文摘要 · Abstract (English)
Coordinating emergency responses in extreme environments, such as wildfires, requires resilient and high-bandwidth communication backbones. While autonomous aerial swarms can establish ad-hoc networks to provide this connectivity, the high risk of individual node attrition in these settings often leads to network fragmentation and mission-critical downtime. To overcome this challenge, we introduce and formalize the problem of Robust Task Networking Under Attrition (RTNUA), which extends connectivity maintenance in multi-robot systems to explicitly address proactive redundancy and attrition recovery. We then introduce Physics-Informed Robust Employment of Multi-Agent Networks ($Φ$IREMAN), a topological algorithm leveraging physics-inspired potential fields to solve this problem. In our evaluations, $Φ$IREMAN consistently outperforms baselines, and is able to maintain greater than $99.9\%$ task uptime despite substantial attrition in simulations with up to 100 tasks and 500 drones, demonstrating both effectiveness and scalability.
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