A Reinforcement Learning Approach for Wildfire Tracking with UAV Swarms

TL;DR

This paper introduces a reinforcement learning-based trajectory optimization method for UAV swarms to effectively track wildfires while maintaining resilient cell-free connectivity with ground access points.

Contribution

It presents a novel RL framework using TD3 for dynamic UAV trajectory planning to enhance wildfire monitoring and network resilience.

Findings

RL approach improves wildfire tracking accuracy

UAVs maintain connectivity despite fire-induced AP damage

Method adaptable to other natural disaster monitoring

Abstract

Suitably equipped with cameras and sensors, uncrewed aerial vehicles (UAVs) can be instrumental for wildfire prediction, tracking, and monitoring, provided that uninterrupted connectivity can be guaranteed even if some of the ground access points (APs) are damaged by the fire itself. A cell-free network structure, with UAVs connecting to a multiplicity of APs, is therefore ideal in terms of resilience. This work proposes a trajectory optimization framework for a UAV swarm tracking a wildfire while maintaining cell-free connectivity with ground APs. Such optimization entails a constant repositioning of the multiplicity of UAVs as the fire evolves to ensure that the best possible view is acquired and transmitted reliably, while respecting altitude limits, avoiding collisions, and proceeding to recharge batteries as needed. Given the complexity and time-varying nature of this multi-UAV trajectory optimization, reinforcement learning is leveraged, specifically the twin-delayed deep deterministic policy gradient algorithm. The approach is shown to be highly effective for wildfire tracking and coverage and could be likewise applicable to survey other natural and man-made phenomena, including weather events, earthquakes, or chemical spills.