关键词: 三维路径规划, Q-Learning算法, 复杂风场, 分层复合机制, 多目标优化

Abstract:

This study proposes an enhanced Q-Learning algorithm for UAV path planning in complex 3D wind fields. By designing a hierarchical composite reward function that integrates wind vectors, path smoothness, and spatial constraints, the algorithm optimizes the trade-off between flight distance and environmental costs. Validated across six diverse wind field scenarios, the results indicate that the proposed method significantly minimizes cumulative wind speed consumption and headwind exposure compared to conventional algorithms. While a slight increase in geometric length is observed, the trajectories feature significantly improved smoothness and reduced inflection points, aligning better with UAV kinematic constraints. The algorithm demonstrates stable convergence within 2,000 iterations. The results confirm the robustness and adaptability of this method for safe and efficient UAV autonomous navigation in dynamic wind environments.

Key words: 3D path planning, Q-Learning algorithm, complex wind fields, hierarchical composite mechanism, multi-objective optimization