低炭素航空知能に向けて：進化・応用・将来動向に関する包括的レビュー
Toward Low-Carbon Aerial Intelligence: A Comprehensive Review of Evolution, Applications, and Future Trends (原題)
Zenglin Liu, Yubo Wang, Pascal Yim
🤖 gxceed AI 要約
日本語
AI搭載無人航空システム（UAV）の進化・分類・技術階層をPRISMA準拠の系統的レビューで整理。軍事・民生の応用に加え、知的エネルギー管理や軌道最適化、環境センシングが低炭素航空に寄与する経路を検討する。複雑環境での頑健性、エッジ知能、説明可能性、サイバーセキュリティ、規制順守などの障壁と、国際標準化を含む将来方向を示す。
English
A PRISMA-based systematic review of AI-enabled aerial unmanned systems, covering platform evolution, a layered technical architecture (perception, decision, control, collaboration, support), and military/civilian applications. It examines AI-driven decarbonization pathways—intelligent energy management, trajectory optimization, and UAV environmental sensing—and identifies barriers such as robustness, edge intelligence, explainability, cybersecurity, and regulatory compliance, outlining future directions toward embodied intelligence and harmonized standards.
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
航空脱炭素は日本のGX政策（航空分野のカーボンニュートラル、SAF普及）と連動するが、本稿はUAV技術レビューであり、SSBJ・有報・投資家開示への直接的示唆は限定的。ドローン物流やインフラ点検の省エネ運用に関心を持つ読者には参考になる。
In the global GX context
Aviation decarbonization is a recognized global transition pathway (ICAO net-zero 2050, SAF mandates), and this review adds an AI/UAV technology lens on energy management and environmental monitoring. However, it does not engage TCFD/ISSB/CSRD disclosure frameworks, so its value to global disclosure scholarship is indirect—useful mainly for understanding operational decarbonization levers in aviation and drone-based emissions monitoring.
👥 読者別の含意
🔬研究者:AI×UAVの技術階層と脱炭素応用の全体像を整理する出発点として有用。
🏢実務担当者:ドローン物流・インフラ点検・環境モニタリングの省エネ運用や規制順守の論点把握に使える。
🏛政策担当者:UAVの安全・セキュリティ・国際標準化と低炭素航空政策の接点を検討する際の参考になる。
📄 Abstract（原文）
Achieving carbon neutrality in the aviation sector demands transformative technologies, and Artificial Intelligence (AI)-enabled aerial unmanned systems are emerging as a critical enabler of low-carbon flight operations, environmental monitoring, and sustainable airspace governance, yet a comprehensive synthesis that jointly addresses their technical architecture, climate-oriented applications, and regulatory pathways remains limited. This review provides a systematic analysis of AI-enabled aerial unmanned systems using a structured literature review framework aligned with PRISMA principles. We first summarize the evolution and classification of aerial unmanned platforms, then propose a layered technical perspective covering perception, decision-making, control, collaboration, and support capabilities. On this basis, we map representative advances in military and civilian scenarios, including autonomous navigation, multi-UAV cooperation, infrastructure inspection, emergency response, and logistics operations. A dedicated discussion further examines AI-driven decarbonization pathways, showing how intelligent energy management and trajectory optimization, together with UAV-based environmental sensing, can contribute to low-carbon aviation and climate-oriented operations. We also identify major barriers to large-scale deployment, including robustness in complex environments, edge intelligence constraints, explainability and trust, cybersecurity, regulatory compliance, and interdisciplinary talent gaps. Finally, we outline future directions centered on embodied intelligence, stronger onboard computing, swarm-level autonomy, and internationally harmonized standards. This review offers an integrated roadmap for researchers, engineers, and policymakers to advance safe, scalable, and sustainable AI-enabled unmanned aviation.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.62762/jcn.2026.328644first seen 2026-10-19 04:59:51
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