  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.
