Multi-Robot-Guided Crowd Evacuation: Two-Scale Modeling and Control

TL;DR

This paper presents a two-scale modeling and control framework for robot-guided crowd evacuation, combining microscopic social force models with macroscopic crowd dynamics to optimize robot guidance strategies.

Contribution

It introduces a novel two-scale hydrodynamic modeling approach and control algorithms for multi-robot guidance of large crowds during evacuations.

Findings

The proposed algorithm stabilizes crowd evacuation dynamics.

Simulations show effective crowd guidance with varying robot and human numbers.

The method adapts to dynamic obstacles and real-time crowd states.

Abstract

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.