Search papers, labs, and topics across Lattice.
This paper introduces Battery-Efficient Routing (BER), an online risk-sensitive planning framework for truck-assisted UAV delivery that explicitly models wind-induced aerodynamic effects on energy consumption. BER formulates the problem as routing on a time-dependent energy graph, continuously evaluating return feasibility while balancing energy expenditure and uncertainty-aware risk. Simulations in Unreal Engine and with real wind logs demonstrate that BER significantly improves mission success rates and reduces wind-induced failures compared to static and greedy baselines.
Drones can navigate uncertain winds and complete deliveries more reliably by dynamically re-routing based on real-time energy expenditure and risk assessment.
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.
