Crowd Congestion and Stampede Management through Multi Robotic Agents

TL;DR

This paper proposes a multi-robotic system for real-time crowd congestion detection and management to prevent stampedes, aiming for minimal environmental disturbance and effective handling of emergencies.

Contribution

It introduces a novel multi-agent robotic approach for real-time crowd monitoring and congestion reduction, enhancing safety without disrupting aesthetics.

Findings

Robotic agents effectively detect congestion and stampede-prone areas.

Simulation shows improved crowd flow and safety during emergencies.

Minimal interference robotic system maintains environment aesthetics.

Abstract

Crowd management is a complex, challenging and crucial task. Lack of appropriate management of crowd has, in past, led to many unfortunate stampedes with significant loss of life. To increase the crowd management efficiency, we deploy automated real time detection of stampede prone areas. Then, we use robotic agents to aid the crowd management police in controlling the crowd in these stampede prone areas. While doing so, we aim for minimum interference by robotic agents in our environment. Thereby not disturbing the ambiance and aesthetics of the place. We evaluate the effectiveness of our model in dealing with difficult scenarios like emergency evacuation and presence of localized congestion. Lastly, we simulate a multi agent system based on our model and use it to illustrate the utility of robotic agents for detecting and reducing congestion.