Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Prediction

Small unmanned aircraft can help firefighters combat wildfires by providing real-time surveillance of fire evolution. However, guiding the aircraft autonomously given only wildfire images is challenging. We propose two approaches to state estimation from wildfire images obtained from noisy on-board cameras. The first approach uses a simple Kalman filter to reduce noise and update a belief map in observed areas. The second approach uses a particle filter to predict wildfire growth and uses observations to estimate uncertainties relating to wildfire expansion. The belief maps are used to train a deep reinforcement learning controller, which learns a policy to navigate the aircraft to survey the wildfire while avoiding flight directly over the fire. Simulation results show that the proposed controllers precisely guide the aircraft and accurately estimate wildfire growth. A study of observation noise demonstrates the robustness of the particle filter approach.