Fire front path planning and tracking control of Uncrewed Aerial Vehicles using deep reinforcement learning

This study develops a unified path planning and control framework based on reinforcement learning for Uncrewed Aerial Vehicles (UAVs) operating in dynamic wildfire environments. The Deep Deterministic Policy Gradient (DDPG) algorithm facilitates tracking fire evolution through a structured architecture comprising high-level planning and low-level control components. The path planner computes the linear velocity and refines the heading angle by incorporating the fire’s directional properties to generate the target trajectory. The low-level controller ensures stable trajectory tracking by adaptively tuning the control gains during the learning process. The closed-loop stability of the overall system is analytically validated using Lyapunov-based analysis. The framework is evaluated using the FARSITE fire area simulator, calibrated with real-world wildfire data. The simulation results demonstrate that the framework generates smooth planning variables, provides adaptive tracking, and remains robust against a range of external disturbances. • Uncrewed aerial vehicles (UAVs) enables real-time fire tracking and situational awareness in wildfire management. • The reinforcement learing-based UAV navigation control framework using Environmental properties. The schematic related to the Path planning and controlling of a fixed-wing UAV using DDPG. • Path planning control method for fire front tracking using DDPG, leveraging fire front propagation properties to enhance UAV performance. • Integration of high-level and low-level design structure for stable, precise, and efficient fire front tracking. • Comparative analysis demonstrates that the DDPG-based framework generates smooth and stable trajectories, and contributes to improved safety.