用强化学习预生成近优航线，加速飞机应急路径重规划。
Hybrid Reinforcement Learning and Search for Flight Trajectory Planning
- 用强化学习预计算近优路径，约束搜索空间缩小
- 相比传统方法提速最高50%，油耗差异小于1%
- 适合需要快速响应的民航应急导航场景
本文探索将强化学习（RL）与基于搜索的路径规划相结合，以加速客机飞行路径优化，尤其在紧急情况下快速重算航路至关重要。核心思想是训练一个RL智能体，根据位置和气象数据预先计算出近似最优路径，并在运行时作为初始解约束底层路径规划求解器，使其在一定距离内快速找到可行解。该方法显著缩小了求解器的搜索空间，从而大幅提升优化速度。尽管无法保证全局最优，但基于空客飞机性能模型的实验表明，燃油消耗与无约束求解器几乎相同，偏差通常低于1%；同时计算速度相较传统求解器提升高达50%。论文还讨论了理论框架、实现策略、测试流程、结果分析及未来发展方向。
原文摘要 · Abstract (English)
This paper explores the combination of Reinforcement Learning (RL) and search-based path planners to speed up the optimization of flight paths for airliners, where in case of emergency a fast route re-calculation can be crucial. The fundamental idea is to train an RL Agent to pre-compute near-optimal paths based on location and atmospheric data and use those at runtime to constrain the underlying path planning solver and find a solution within a certain distance from the initial guess. The approach effectively reduces the size of the solver's search space, significantly speeding up route optimization. Although global optimality is not guaranteed, empirical results conducted with Airbus aircraft's performance models show that fuel consumption remains nearly identical to that of an unconstrained solver, with deviations typically within 1%. At the same time, computation speed can be improved by up to 50% as compared to using a conventional solver alone. This paper discusses the theoretical framework, the different implementation strategies, the adopted testing procedures, the obtained results and finally further possible developments and future perspectives.can be improved by up to 50% as compared to using a conventional solver alone.
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