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 use 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 hydrodynamic models, which consist of a family of microscopic social force models that describe how human movements are locally affected by other humans, the environment, and 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 macrostates of the crowd to guide the crowd to a safe location. We prove the stability of the proposed evacuation algorithm and conduct extensive simulations to investigate the performance of the algorithm with different combinations of human numbers, robot numbers, and obstacle settings.

翻译：摘要：紧急疏散描述了涉及疏散人员即时决策的复杂情境。移动机器人正被积极研究作为提供及时引导的潜在解决方案。本研究探讨机器人引导的人群疏散问题，其中使用少量机器人引导大量人群前往安全地点。挑战在于如何通过微观层面的人机交互间接影响数量远超机器人的人群，从而实现集体疏散目标。为应对这一挑战，我们采用双尺度建模策略，探索流体动力学模型。该模型包含一系列微观社会力模型（描述人类运动如何受其他个体、环境及机器人的局部影响）以及关联的宏观方程（描述人群密度与流动速度的时空演化）。我们为机器人设计控制器，使其不仅能自动探索环境（包含未知动态障碍物）以尽可能广域覆盖，还能基于实时人群宏观状态动态调整局部导航力场方向，引导人群前往安全区域。我们证明了该疏散算法的稳定性，并通过大量仿真研究了算法在不同人数、机器人数量及障碍物设置组合下的性能表现。