Using Ant Colony Optimization to Route a Team of UAVs to Fires Based on Known Fire Intensities

Abstract

1 ABSTRACT Increasing in severity due to Climate Change, wildfires will increasingly damage human populations. Autonomous solutions to fighting fires using Unmanned Aerial Vehicles (UAVs) show increasing promise in recent years. In this paper, we apply Ant Colony Optimization to allocate a team of UAVs with known fire extinguisher payloads to fires of known intensities. We evaluate the effectiveness of the algorithm on a dataset based on Washington Department of Natural Resources fire data from 2011-2020.