Electric vertical-takeoff and landing (eVTOL) aircraft, recognized for their maneuverability and flexibility, offer a promising alternative to our transportation system. However, the operational effectiveness of these aircraft faces many challenges, such as the delicate balance between energy and time efficiency, stemming from unpredictable environmental factors, including wind fields. Mathematical modeling-based approaches have been adopted to plan aircraft flight path in urban wind fields with the goal to save energy and time costs. While effective, they are limited in adapting to dynamic and complex environments. To optimize energy and time efficiency in eVTOL's flight through dynamic wind fields, we introduce a novel path planning method leveraging deep reinforcement learning. We assess our method with extensive experiments, comparing it to Dijkstra's algorithm -- the theoretically optimal approach for determining shortest paths in a weighted graph, where weights represent either energy or time cost. The results show that our method achieves a graceful balance between energy and time efficiency, closely resembling the theoretically optimal values for both objectives.

翻译：电动垂直起降（eVTOL）飞行器以其高机动性和灵活性，为交通运输系统提供了极具前景的替代方案。然而，受风场等不可预测环境因素影响，此类飞行器的运行效能面临诸多挑战，其中能耗与时间效率的精细平衡尤为关键。现有基于数学建模的方法虽能规划城市风场中的飞行路径以节省能耗与时间成本，但其在动态复杂环境中的适应性存在局限。为优化动态风场中eVTOL飞行的能耗与时间效率，我们提出了一种基于深度强化学习的新型路径规划方法。通过大规模实验，我们将该方法与迪杰斯特拉算法——以能耗或时间成本为权重的加权图中求取最短路径的理论最优方法——进行了对比。结果表明，我们的方法在能耗与时间效率之间实现了优雅平衡，与两项指标的理论最优值高度吻合。