# Argus: Smartphone-enabled Human Cooperation via Multi-Agent Reinforcement Learning for Disaster Situational Awareness

## TL;DR

Argus leverages multi-agent reinforcement learning with smartphones and drones to create real-time 3D maps of disaster scenes, enhancing situational awareness for rescue operations.

## Contribution

This work introduces a novel MARL framework integrating human and robotic agents for real-time disaster scene mapping using smartphones and drones.

## Key findings

- Effective in tracking dynamic environments in simulations.
- Successful real-world deployment demonstrates practical utility.
- Improves situational awareness for rescue efforts.

## Abstract

Argus exploits a Multi-Agent Reinforcement Learning (MARL) framework to create a 3D mapping of the disaster scene using agents present around the incident zone to facilitate the rescue operations. The agents can be both human bystanders at the disaster scene as well as drones or robots that can assist the humans. The agents are involved in capturing the images of the scene using their smartphones (or on-board cameras in case of drones) as directed by the MARL algorithm. These images are used to build real time a 3D map of the disaster scene. Via both simulations and real experiments, an evaluation of the framework in terms of effectiveness in tracking random dynamicity of the environment is presented.