Optimization of UAV–UGV Last-Mile Collaborative Delivery under Dynamic Carbon Footprint Constraints: Visual Simulation and Scenario Analysis of Coordinated Air–Ground Unmanned Delivery System
DOI:
https://doi.org/10.61360/BoniGHSS26202052040801Keywords:
air–ground collaborative delivery, UAV–UGV, carbon footprint tracking, visual simulation, talent cultivationAbstract
To address the challenges of operational cost control, safety assurance, and dynamic carbon footprint management in urban last-mile logistics, this study proposes a UAV–UGV air–ground collaborative delivery framework and develops a multi-objective optimization model considering efficiency, safety, and low-carbon performance. A visual simulation platform is constructed using HTML5 Canvas and Three.js, integrating obstacle avoidance, low-altitude UAV delivery, random scenario generation, and dynamic route re-planning modules. Simulation results show that, under an order density of 20 orders/100 km², the proposed strategy reduces total operational time by 33.9% compared with traditional truck delivery and decreases travel distance by 24.3% compared with UAV-only delivery. The unit carbon emission intensity is reduced by 96.4% and 24.3% compared with truck-based and UAV-only delivery modes, respectively. The obstacle avoidance algorithm achieves a 100% success rate in 50 random scenarios, with a path length variation coefficient of 0.132, demonstrating strong robustness. Sensitivity analysis confirms that the model remains stable under ±20% variations in carbon emission factors and carbon trading prices. This study provides a quantitative decision-support framework for unmanned delivery optimization and low-carbon logistics equipment selection. At the same time, it provides a reference for nurturing potential talent for the future.
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Copyright (c) 2026 Yu Qiao*, Xinwen Deng, Xiaoying Huang, Wanting Chen, Yimeng Lin, Zuqiang Luo, Yuchen Du, Luyao Li, Meijun Shen, Xuanxuan Chen
This work is licensed under a Creative Commons Attribution 4.0 International License.
