Robust Energy-Aware Routing for Air-Ground Cooperative Multi-UAV Delivery in Wind-Uncertain Environments
Tianshun Li, Hongliang Lu, Yanggang Sheng, Zhongzhen Wang, Haoang Li, Xinhu Zheng

TL;DR
This paper introduces BER, an online risk-sensitive routing framework for UAV delivery that accounts for wind uncertainty, improving safety and success rates in dynamic environments.
Contribution
The paper presents a novel energy-aware routing method that dynamically adapts to wind conditions, unlike prior static or deterministic models.
Findings
BER significantly increases mission success rates.
BER reduces wind-induced failures in UAV delivery.
Simulations show improved reliability over static baselines.
Abstract
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…
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