Wildfire Tracking by Fixed-Wing Unmanned Aerial Vehicles Using Receding-Horizon Guidance

Abstract Unmanned aerial vehicles (UAVs) can be used to track growing and moving boundaries such as those of wildfires where the boundary cannot be pre-specified. Toward this, we first present a model predictive control (MPC) formulation for this task, which systematically incorporates vehicle dynamics, evolving boundary models, and input constraints to enable precise tracking. While effective, solving the nonlinear optimization online incurs high computational cost, limiting real-time deployment. To address this, we propose a novel receding-horizon guidance law that replaces the optimization step with a closed-form solution based on steady-turn motion primitives embedded in a receding-horizon framework. This approach generates circular-arc trajectories in lieu of the computationally expensive optimization routine, while preserving the predictive nature of the formulation and enabling real-time onboard implementation. Simulation studies validate the method across varying UAV initial conditions, prediction horizons, and fire model parameters, demonstrating that it achieves tracking performance comparable to MPC while reducing computation time by several orders of magnitude.