基于大语言模型的灾难搜索救援中人-群智能协同认知框架
An LLM-based Framework for Human-Swarm Teaming Cognition in Disaster Search and Rescue
AI总结：
本文提出基于大语言模型的LLM-CRF系统，通过自然交互捕捉意图并实现任务规划，显著提升灾难救援任务效率与成功率。
摘要：
大规模灾难搜索与救援（SAR）操作始终受到复杂地形和通信中断的挑战。尽管无人机群（UAV群）在广域搜索和物资配送等任务中提供了有前景的解决方案，但其有效协调却给人类操作员带来了显著的认知负担。核心的人机协作瓶颈在于'意图-行动间隙'，即在高强度和压力下将高层救援目标转化为低层群命令的过程易出错。为此，本研究提出了一种新颖的LLM-CRF系统，利用大语言模型（LLMs）来建模和增强人-群智能协同认知。该框架首先通过语音或图形注释等自然多模态交互捕捉操作员的意图。然后利用LLM作为认知引擎，对UAV群进行意图理解、分层任务分解和任务规划。这种闭环框架使群能够作为主动合作伙伴，实时提供反馈，减少对人工监控和控制的需求，从而显著提升SAR任务的效果。我们在模拟SAR场景中评估了所提框架。实验结果表明，与传统命令和控制接口相比，所提基于LLM的方法将任务完成时间减少了约64.2%，任务成功率提高了7%。它还导致主观认知负荷显著降低，NASA-TLX分数下降了42.9%。本工作确立了LLM在高风险场景中创建更直观和有效的人-群智能协作的潜力。
英文摘要：
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.
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