发表机构
The Hong Kong University of Science and Technology (Guangzhou); Southern University of Science and Technology（香港科技大学（广州）; 南方科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对卡车辅助多无人机配送的风况不确定性与能量安全问题，提出EWR在线规划框架，通过时变能量图优化路由，提升任务成功率并减少风致返回失败。
AI 中文摘要
风况不确定性下的能量可行性是低空空地配送的关键安全问题。在卡车-无人机系统中，无人机完成指定配送后需安全返回移动卡车或基地，而风导致的推进成本随在线变化且仅部分可观测。现有路由方法常依赖静态或确定性能量模型，可能低估逆风、侧风、电池电压及返回可行性风险。本文提出能量感知抗风路由（EWR），一种面向风感知与能量安全的无人机路由在线风险敏感规划框架。配送环境表示为随时间变化的有向能量图，其边成本通过延迟带噪风估计、载荷状态及保守不确定性裕度更新。使用来自公开卡车-无人机配送数据集的重放风日志的合成配送图开展实验，结果显示EWR提升了任务成功率并降低了风导致的返回失败率。
英文摘要
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.
