Robust Energy-Aware Routing for Air-Ground Cooperative Multi-UAV Delivery in Wind-Uncertain Environments

2026-04-14

AI Summary

This study addresses the challenge of ensuring safety and reliability in drone delivery under uncertain wind conditions, where conventional approaches relying on static energy consumption models fall short. To overcome this limitation, the authors propose the Battery-Efficient Routing (BER) framework, which introduces, for the first time, a risk-sensitive, real-time energy budgeting mechanism into multi-drone collaborative delivery. BER constructs a time-varying energy consumption graph incorporating real-time wind field data to dynamically assess return-to-home feasibility and jointly optimizes task allocation, path planning, and trajectory execution within a hierarchical air-ground cooperative architecture. High-fidelity wind field simulations based on Unreal Engine demonstrate that BER significantly improves mission success rates in both synthetic and near-realistic wind environments, effectively mitigating flight failures caused by wind disturbances and outperforming static and greedy baseline methods.

Technology Category

• Planning, Routing, and Scheduling: Planning under Uncertainty
• Intelligent Robots: Multi-Robot Systems
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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 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.

Problem

• UAV delivery
• wind uncertainty
• energy feasibility
• air-ground cooperation
• dynamic environment

Innovation

• energy-aware routing
• wind uncertainty
• risk-sensitive planning
• time-dependent energy graph
• air-ground cooperative delivery