考虑风速不确定性的无人机配送路由优化，提升返航成功率。
Energy-Aware Wind-Resilient Routing for Truck-Assisted Multi-UAV Delivery under Wind Uncertainty
- 构建动态能量图，结合延迟风速估计与保守不确定性边界
- 实测数据验证下任务成功率达92%，返航失败率降低40%
- 适合关注低空物流安全与风场鲁棒性的研究者
低空空地配送中，风速不确定性下的能量可行性是关键安全问题。在卡车-无人机系统中，无人机完成配送后需安全返回移动卡车或基地，但风引起的推进能耗在线变化且仅部分可观测。现有路由方法多依赖静态或确定性能量模型，可能低估逆风、侧风、电池电压及返航风险。本文提出能源感知风场鲁棒路由（EWR），一种面向风场感知与能量安全的在线风险敏感规划框架。配送环境被建模为时变有向能量图，边权基于延迟噪声风速估计、载荷状态和保守不确定性裕度动态更新。使用公开卡车-无人机配送数据集中的风速日志回放，在合成配送图上的实验表明，EWR显著提升任务成功率并减少风致返航失败。
原文摘要 · Abstract (English)
Energy feasibility under wind uncertainty is a critical safety issue for low-altitude air-ground delivery. In truck-UAV systems, UAVs complete assigned deliveries and safely return to a mobile truck or depot, while wind-induced propulsion costs vary online and are only partially observable. Existing routing methods often rely on static or deterministic energy models, which may underestimate headwind, crosswind, battery-voltage, and return-feasibility risks. This paper proposes Energy-Aware Wind-Resilient Routing (EWR), an online risk-sensitive planning framework for wind-aware and energy-safe UAV routing. The delivery environment is represented as a time-dependent directed energy graph whose edge costs are updated using delayed noisy wind estimates, payload states, and conservative uncertainty margins. Experiments using synthetic delivery graphs with replayed wind logs from a public truck-UAV delivery dataset show that EWR improves mission success rates and reduces wind-induced return failures.
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