Stochastic Optimal Intervention for Robot-Assisted Crowd Evacuation

Robot-assisted interventions are often provided for crowd evacuation to prevent accidents and keep crowds safety, which is currently a hot topic of research. However, the existing work has ignored the impact of crowd motion chaos which is a fundamental factor determining the effective evacuation. To assist the crowd evacuation more efficiently and reduce the intervention costs, robots need to identify destructive individuals based on the randomly evolved chaos and intervene in them specifically. To this end, we propose a stochastic optimal intervention method to provide the optimal strategies for robot-assisted crowd evacuation. First, the definition of chaos is introduced to quantify the chaotic degree of crowd movement. Then, we study the stochastic evolution of chaos by exploring the chaos state transition with marked temporal point process (MTPP) and stochastic differential equations (SDEs). Next, the robot-assisted intervention is formulated as a stochastic optimal intervention problem that seeks to maximize the intervention utility. Fortunately, a closed-form solution is obtained by using the Bellman's principle of optimality and solving the derived Hamilton-Jacobi-Bellman (HJB) equation. At last, we develop an event-driven crowd motion system to simulate crowd evacuation process. The simulation results show that our method can prevent the occurrence of chaos effectively, and improve the efficiency of crowd evacuation significantly. The proposed method is expected to provide guidance for emergency management.