Emergency evacuation describes a complex situation involving time-critical decision-making by evacuees. Mobile robots are being actively explored as a potential solution to provide timely guidance. In this work, we study a robot-guided crowd evacuation problem where a small group of robots is used to guide a large human crowd to safe locations. The challenge lies in how to utilize micro-level human-robot interactions to indirectly influence a population that significantly outnumbers the robots to achieve the collective evacuation objective. To address the challenge, we follow a two-scale modeling strategy and explore mean-field hydrodynamic models which consist of a family of microscopic social-force models that explicitly describe how human movements are locally affected by other humans, the environment, and the robots, and associated macroscopic equations for the temporal and spatial evolution of the crowd density and flow velocity. We design controllers for the robots such that they not only automatically explore the environment (with unknown dynamic obstacles) to cover it as much as possible but also dynamically adjust the directions of their local navigation force fields based on the real-time macro-states of the crowd to guide the crowd to a safe location. We prove the stability of the proposed evacuation algorithm and conduct a series of simulations (involving unknown dynamic obstacles) to validate the performance of the algorithm.

翻译：紧急疏散描述了一种涉及疏散人员实时决策的复杂情境。移动机器人作为一种提供及时引导的潜在方案正在被积极探索。本文研究了机器人引导的人群疏散问题，其中使用一小群机器人引导大量人群到达安全位置。挑战在于如何利用微观层面的人机交互间接影响数量远超机器人的群体，以实现集体疏散目标。为应对这一挑战，我们采用双尺度建模策略，探索了均值场流体动力学模型，该模型包含一族微观社会力模型——明确描述人类运动如何受其他行人、环境及机器人的局部影响——以及描述人群密度和流动速度时空演化的相关宏观方程。我们为机器人设计了控制器，使其不仅能自动探索环境（包含未知动态障碍物）以尽可能覆盖区域，还能根据人群的实时宏观状态动态调整局部导航力场方向，从而引导人群到达安全位置。我们证明了所提疏散算法的稳定性，并通过一系列涉及未知动态障碍物的仿真验证了算法的性能。