Aerial Vehicle Path Planning for Monitoring Wildfire Frontiers

Abstract

This paper explores the use of unmanned aerial vehicles (UAVs) in wildfire monitoring. To begin establishing effective methods for autonomous monitoring, a simulation (FLAME) is developed for algorithm testing. To simulate a wildfire, the well established FARSITE fire simulator is used to generate realistic fire behavior models. FARSITE is a wildfire simulator that is used in the field by Incident Commanders (IC’s) to predict the spread of the fire using topography, weather, wind, moisture, and fuel data. The data obtained from FARSITE is imported into FLAME and parsed into a dynamic frontier used for testing hotspot monitoring algorithms. In this paper, points of interest along the frontier are established as points with a fireline intensity (British-Thermal-Unit/feet/second) above a set threshold. These interest points are refined into hotspots using the Mini-Batch K-means Clustering technique. A distance threshold differentiates moving hotspot centers and newly developed hotspots. The proposed algorithm is compared to a baseline for minimizing the sum of the max time untracked J(t). The results show that simply circling the fire performs poorly (baseline), while a weighted-greedy metric (proposed) performs significantly better. The algorithm was then run on a UAV to demonstrate the feasibility of real world implementation.