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.

翻译：极端环境（如野火）中的应急响应协调需要具有韧性的高带宽通信骨干。尽管自主空中集群可构建自组网络提供此类连接，但在这种环境下单个节点的高损耗风险常导致网络碎片化及任务关键性中断。为攻克这一难题，我们提出并形式化定义了"损耗条件下的鲁棒任务联网问题"（RTNUA），该问题将多机器人系统中的连通性维护扩展至显式处理主动冗余与损耗恢复。随后我们提出"物理信息驱动的多智能体网络鲁棒部署方法"（ΦIREMAN），这是一种利用物理启发势场解决该问题的拓扑算法。在含最多100项任务与500架无人机的仿真评估中，ΦIREMAN始终优于基线方法，即使在显著损耗下仍能维持超过99.9%的任务不间断运行，展现了其有效性与可扩展性。