鲁棒能耗感知路由：风不确定环境中的空地协同多无人机配送
Robust Energy-Aware Routing for Air-Ground Cooperative Multi-UAV Delivery in Wind-Uncertain Environments
AI总结：
本文提出BER框架，通过动态能耗图和风险感知路由提升无人机在风扰环境中的配送可靠性。
摘要：
在风不确定环境下确保无人机配送任务的能耗可行性至关重要。在现实的卡车-无人机物流系统中，无人机必须在时间变化的风条件下安全配送包裹并返回。然而，现有路由方法大多假设静态或确定性能耗模型，难以应对动态风环境。本文提出电池高效路由（BER），一种在线风险敏感规划框架，用于风敏感的卡车辅助无人机配送。该问题被建模为时间依赖的能耗图上的路由问题，其中边成本根据风引起的空气动力学效应变化。BER在平衡即时能耗和风险感知不确定性的同时，持续评估返回可行性。该方法嵌入了分层空地配送架构，结合任务分配、路由和去中心化轨迹执行。在Unreal Engine环境中生成的合成ER图和准真实风日志上的广泛仿真表明，BER相比静态和贪心基线显著提高了任务成功率并减少了风引起的失败。这些结果突显了在动态风条件下整合实时能耗预算和环境意识对于无人机配送规划的重要性。
英文摘要：
Ensuring energy feasibility under wind uncertainty is critical for the safety and reliability of UAV delivery missions. In realistic truck-drone logistics systems, UAVs must deliver parcels and safely return under time-varying wind conditions that are only partially observable during flight. However, most existing routing approaches assume static or deterministic energy models, making them unreliable in dynamic wind environments. We propose Battery-Efficient Routing (BER), an online risk-sensitive planning framework for wind-sensitive truck-assisted UAV delivery. The problem is formulated as routing on a time dependent energy graph whose edge costs evolve according to wind-induced aerodynamic effects. BER continuously evaluates return feasibility while balancing instantaneous energy expenditure and uncertainty-aware risk. The approach is embedded in a hierarchical aerial-ground delivery architecture that combines task allocation, routing, and decentralized trajectory execution. Extensive simulations on synthetic ER graphs generated in Unreal Engine environments and quasi-real wind logs demonstrate that BER significantly improves mission success rates and reduces wind-induced failures compared with static and greedy baselines. These results highlight the importance of integrating real-time energy budgeting and environmental awareness for UAV delivery planning under dynamic wind conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
