TEeVTOL：都市規模の風場を横断するeVTOL航空機の経路計画におけるエネルギーと時間効率のバランス
TEeVTOL: Balancing Energy and Time Efficiency in eVTOL Aircraft Path Planning Across City-Scale Wind Fields
深層強化学習を用いて、動的な都市風場におけるeVTOLの経路計画を行い、エネルギーと時間の効率を両立させる手法を提案し、ダイクストラ法と比較して理論最適に近い性能を示した。
著者: Songyang Liu, Shuai Li, Haochen Li, Weizi Li, Jindong Tan
分類: cs.RO
原文アブストラクト
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.
