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This paper introduces Energy-Aware Wind-Resilient Routing (EWR), an innovative online planning framework designed to enhance the safety and efficiency of truck-assisted UAV delivery systems under uncertain wind conditions. By modeling the delivery environment as a time-dependent directed energy graph and dynamically updating edge costs based on real-time wind estimates and payload states, EWR significantly mitigates risks associated with wind-induced propulsion costs. Experimental results demonstrate that EWR not only improves mission success rates but also reduces the likelihood of return failures caused by adverse wind conditions.
EWR boosts UAV delivery success rates by effectively navigating the unpredictable challenges of wind, transforming how we approach energy management in aerial logistics.
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.
