Learning-based Wildfire Tracking with Unmanned Aerial Vehicles

This paper designs a path planning algorithm for a group of unmanned aerial vehicles (UAVs) to track multiple spreading wildfire zones. Due to limited observable information, the fire evolution is hard to model. A regression neural network is online trained with real-time UAV observation data and applied for fire front prediction. To track fire fronts effectively, a UAV path planning algorithm is proposed by Q-learning. Various practical factors are taken into account by cost function designs such as moving target tracking, field of view of UAVs, spreading speed of fire zones, collision/obstacle avoidance, and maximum information collection. Simulation results validate the fire prediction accuracy and UAV tracking performance.