Abstract

To address the limitations of static-obstacle-based route planning in forest fire missions, this study develops a three-dimensional route-planning method for fixed-wing unmanned aerial vehicles (UAVs) that accounts for time-varying fire and smoke threats, complex terrain, and flight-dynamics constraints. A cellular automaton models fire spread with wind, slope, fuel, and moisture effects, while a Gaussian plume model estimates smoke concentration. The resulting burning and high-concentration smoke cells are encoded as dynamic three-dimensional threat envelopes and local grid masks. A hierarchical proximal policy optimization (H-PPO) architecture then combines a high-level stateful long short-term memory (LSTM) policy for route-subgoal generation with a pretrained low-level flight controller that produces continuous throttle and control-surface commands in JSBSim. In 100 independent simulation tests, the complete H-PPO model achieved a 100% task success rate, a mean terrain clearance of 771.92 m, and an average online decision time of 1.290 ms. Compared with A* and RRT*, H-PPO provided higher task reliability, greater mean terrain clearance, and lower online computational cost. The results show that hierarchical temporal decision making improves safety-prioritized planning in evolving fire and smoke environments, although conservative avoidance increases route length and mission duration. Further real-world and flight-test validation is required.

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