Autonomous Wildland Fire Monitoring through Integrated UAS Path Planning and Sensor Fusion

Real-time wildfire prediction and estimation is paramount in the practice of wildland fire management. In this paper we propose a method of autonomous wildland fire monitoring via integrated path planning and sensor fusion. Accurate localized measurements made by a Unmanned Aerial System (UAS) equipped with a radiometric infrared camera are fused with a low fidelity Reaction Diffusion fire model using an ensemble Kalman filter (EnKF). We develop a reward function defined by the variance of the EnKF. We pose a fixed-time horizon reward maximization path planning problem to determine what sequence of measurements the UAS is to take, and solve it using a dynamic programming algorithm. We demonstrate the closed loop performance of the path planning and sensor fusion framework using real infrared video of a prescribed prairie burn to simulate a wildland fire.