针对低能见度环境下人群疏散引导失效和效率低下等挑战，构建了一个考虑无人机-行人微观交互机制的疏散模型。该模型融合社会力模型与互惠速度障碍算法，精细刻画了行人与无人机的感知-决策-运动的动态反馈闭环，并形式化定义了听觉、视觉与通信三种引导交互模式，以此为基础制定引导策略。系统仿真结果表明，无人机多模态引导能普遍且显著提升疏散性能。在所有测试场景中，无人机引导下的整体疏散效率（平均行程时间）均优于随机探索下的。即便是性能最低的视觉模式，在最不利场景下仍能节省约9.99%的平均行程时间；而表现最优的通信模式，最高可节省26.36%的时间。敏感性分析结果表明，疏散性能对信息传递半径和接受率高度敏感，而对信息粒度不敏感。此外，研究揭示了交互模式与飞行策略间的交互效应，发现通信模式和固定路线策略的组合表现最优。一个关键发现是，听觉模式虽能提升整体疏散效率，却可能损害个体安全（最大行程时间），揭示了群体效率与个体安全之间的内在权衡。研究为低能见度下智能疏散系统的设计提供了定量分析框架与优化原则。

To address challenges such as the failure and inefficiency of crowd evacuation guidance in low-visibility environments, this study constructs an evacuation model that incorporates the micro-interaction mechanism between Unmanned Aerial Vehicles (UAVs) and pedestrians. Integrating the social force model with the optimal reciprocal collision avoidance algorithm, the model meticulously characterizes the dynamic “perception-decision-motion” feedback loop for both pedestrians and UAVs. Furthermore, it formally defines three guidance interaction modes: auditory, visual, and communication, and formulates guidance strategies based on these. System simulation results demonstrate that multi-modal UAV guidance universally and significantly enhances evacuation performance. In all test scenarios, overall evacuation efficiency (measured by average travel time) is superior under UAV guidance compared to random exploration. Even the visual mode, which exhibited the lowest performance, achieved a reduction in average travel time of approximately 9.99% under the most unfavorable conditions. Conversely, the communication mode demonstrated optimal performance, reducing average travel time by up to 26.36%. Sensitivity analysis reveals that evacuation performance is highly sensitive to the information transmission radius and information acceptance rate, but relatively insensitive to information granularity. Additionally, the study uncovers interaction effects between interaction modes and flight strategies, identifying the combination of the communication mode with a fixed-route strategy as the most effective. A critical finding indicates that while the auditory mode improves overall evacuation efficiency, it may compromise individual safety (measured by maximum travel time), highlighting an intrinsic trade-off between collective efficiency and individual safety. This research provides a quantitative analytical framework and optimization principles for the design of intelligent evacuation systems in low-visibility conditions.