Large-scale disaster Search And Rescue (SAR) operations are persistently challenged by complex terrain and disrupted communications. While Unmanned Aerial Vehicle (UAV) swarms offer a promising solution for tasks like wide-area search and supply delivery, yet their effective coordination places a significant cognitive burden on human operators. The core human-machine collaboration bottleneck lies in the ``intention-to-action gap'', which is an error-prone process of translating a high-level rescue objective into a low-level swarm command under high intensity and pressure. To bridge this gap, this study proposes a novel LLM-CRF system that leverages Large Language Models (LLMs) to model and augment human-swarm teaming cognition. The proposed framework initially captures the operator's intention through natural and multi-modal interactions with the device via voice or graphical annotations. It then employs the LLM as a cognitive engine to perform intention comprehension, hierarchical task decomposition, and mission planning for the UAV swarm. This closed-loop framework enables the swarm to act as a proactive partner, providing active feedback in real-time while reducing the need for manual monitoring and control, which considerably advances the efficacy of the SAR task. We evaluate the proposed framework in a simulated SAR scenario. Experimental results demonstrate that, compared to traditional order and command-based interfaces, the proposed LLM-driven approach reduced task completion time by approximately $64.2\%$ and improved task success rate by $7\%$. It also leads to a considerable reduction in subjective cognitive workload, with NASA-TLX scores dropping by $42.9\%$. This work establishes the potential of LLMs to create more intuitive and effective human-swarm collaborations in high-stakes scenarios.

翻译：大规模灾害搜救行动持续面临复杂地形与通信中断的挑战。尽管无人机群为广域搜索与物资投送等任务提供了前景广阔的解决方案，但其有效协调对人类操作员构成了显著的认知负担。人机协作的核心瓶颈在于“意图-行动鸿沟”，即在高压环境下将高层救援目标转化为低层级群指令这一易出错的过程。为弥合此鸿沟，本研究提出一种新颖的LLM-CRF系统，利用大语言模型建模并增强人-群协同认知。该框架首先通过语音或图形标注等自然多模态交互方式捕捉操作员意图，随后将LLM作为认知引擎执行意图理解、分层任务分解及无人机群任务规划。该闭环框架使无人机群能够作为主动协作伙伴实时提供反馈，同时减少人工监控与控制需求，从而显著提升搜救任务效能。我们在模拟搜救场景中评估了该框架，实验结果表明：相较于传统指令式交互界面，所提出的LLM驱动方法将任务完成时间缩短约$64.2\\%$，任务成功率提升$7\\%$，同时显著降低主观认知负荷——NASA-TLX评分下降$42.9\\%$。本工作证实了大语言模型在高风险场景中构建更直观有效的人-群协作系统的潜力。