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動的炭素フットプリント制約下におけるUAV-UGVラストマイル協調配送の最適化：可視化シミュレーションとシナリオ分析

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 (原題)

Yu Qiao, Xinwen Deng, Xiaoying Huang, Wanting Chen, Yimeng Lin, Zuqiang Luo, Yuchen Du, Luyao Li, Meijun Shen, Xuanxuan Chen

Global Humanities and Social Sciences📚 査読済 / ジャーナル2026-08-18#AI×ESGOrigin: CN経営インパクト: コスト削減対象セクター: transport
DOI: 10.61360/bonighss26202052040801
原典: https://doi.org/10.61360/bonighss26202052040801

🤖 gxceed AI 要約

日本語

本研究は、都市部のラストマイル物流におけるUAVとUGVの協調配送フレームワークを提案し、効率・安全性・低炭素性を考慮した多目的最適化モデルを構築した。HTML5 CanvasとThree.jsを用いた可視化シミュレーションプラットフォームを開発し、動的経路再計画と障害物回避を統合。シミュレーションでは、トラック配送と比較して総運用時間を33.9%削減し、UAV単独と比較して走行距離を24.3%削減、単位炭素排出強度を96.4%削減することを示した。感度分析により、炭素排出係数と炭素取引価格の±20%変動に対してモデルが安定していることを確認した。

English

This study proposes a UAV-UGV collaborative delivery framework for urban last-mile logistics, developing a multi-objective optimization model considering efficiency, safety, and low-carbon performance. A visual simulation platform using HTML5 Canvas and Three.js integrates obstacle avoidance, dynamic route re-planning, and random scenario generation. Results show a 33.9% reduction in total operational time compared to truck delivery, a 24.3% reduction in travel distance compared to UAV-only, and a 96.4% reduction in unit carbon emission intensity. Sensitivity analysis confirms model stability under ±20% variations in carbon factors and prices.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本の物流業界は2024年問題によるドライバー不足とカーボンニュートラル対応が喫緊の課題であり、無人配送の低炭素化はSSBJ開示やScope 3排出削減にも寄与する。本研究成果は、配送効率と炭素排出削減を両立する技術選択の定量的根拠を提供し、日本の物流企業や自治体の実装検討に有用である。

In the global GX context

Globally, last-mile logistics is a significant source of urban emissions, and this study offers a quantitative framework for integrating UAV-UGV systems with carbon footprint constraints. It aligns with ISSB and CSRD disclosure requirements by providing data on emission reductions and operational efficiency. The sensitivity analysis on carbon pricing supports transition finance decisions and low-carbon infrastructure investments.

👥 読者別の含意

🔬研究者:Provides a multi-objective optimization model and simulation platform for UAV-UGV delivery that can be extended to other urban logistics contexts.

🏢実務担当者:Offers quantitative evidence for adopting UAV-UGV systems to reduce operational costs and carbon emissions, useful for logistics companies and fleet managers.

🏛政策担当者:Highlights the potential of unmanned delivery systems to meet low-carbon targets, informing urban logistics regulations and incentives.

📄 Abstract（原文）

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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