Artificial Intelligence in Aerial Unmanned Systems: A Comprehensive Review of Evolution, Applications, and Future Trends

Zenglin Liu; Yubo Wang; Pascal Yim

doi:10.62762/JCN.2026.328644

Article Information

Published in Journal of Carbon Neutrality

Volume/Issue Volume 1, Issue 2, 2026

Pages 127-154

Abstract

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.

Graphical Abstract

Artificial Intelligence in Aerial Unmanned Systems: A Comprehensive Review of Evolution, Applications, and Future Trends

Keywords

aerial unmanned systems unmanned aerial vehicle (UAV) artificial intelligence (AI) carbon neutrality low-carbon aviation green aviation sustainable development development trends swarm intelligence

Data Availability Statement

Not applicable.

Funding

This work was supported without any funding.

Conflicts of Interest

Zenglin Liu is affiliated with the POLY Aviation Technology Co., Ltd., Beijing 100027, China. The authors declare that this affiliation had no influence on the study design, data collection, analysis, interpretation, or the decision to publish, and that no other competing interests exist.

AI Use Statement

The authors declare that ChatGPT-5 was used for language editing, including improvements to the language, grammar, and readability of the manuscript. The authors have carefully reviewed, revised, and verified the AI-assisted output and take full responsibility for the content of the manuscript.

Ethical Approval and Consent to Participate

Not applicable.

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Liu, Z., Wang, Y., & Yim, P. (2026). Artificial Intelligence in Aerial Unmanned Systems: A Comprehensive Review of Evolution, Applications, and Future Trends. Journal of Carbon Neutrality, 1(2), 127-154. https://doi.org/10.62762/JCN.2026.328644

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TY  - JOUR
AU  - Liu, Zenglin
AU  - Wang, Yubo
AU  - Yim, Pascal
PY  - 2026
DA  - 2026/09/22
TI  - Artificial Intelligence in Aerial Unmanned Systems: A Comprehensive Review of Evolution, Applications, and Future Trends
JO  - Journal of Carbon Neutrality
T2  - Journal of Carbon Neutrality
JF  - Journal of Carbon Neutrality
VL  - 1
IS  - 2
SP  - 127
EP  - 154
DO  - 10.62762/JCN.2026.328644
UR  - https://www.icck.org/article/abs/JCN.2026.328644
KW  - aerial unmanned systems
KW  - unmanned aerial vehicle (UAV)
KW  - artificial intelligence (AI)
KW  - carbon neutrality
KW  - low-carbon aviation
KW  - green aviation
KW  - sustainable development
KW  - development trends
KW  - swarm intelligence
AB  - 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.
SN  - 3144-2668
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Liu2026Artificial,
  author = {Zenglin Liu and Yubo Wang and Pascal Yim},
  title = {Artificial Intelligence in Aerial Unmanned Systems: A Comprehensive Review of Evolution, Applications, and Future Trends},
  journal = {Journal of Carbon Neutrality},
  year = {2026},
  volume = {1},
  number = {2},
  pages = {127-154},
  doi = {10.62762/JCN.2026.328644},
  url = {https://www.icck.org/article/abs/JCN.2026.328644},
  abstract = {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.},
  keywords = {aerial unmanned systems, unmanned aerial vehicle (UAV), artificial intelligence (AI), carbon neutrality, low-carbon aviation, green aviation, sustainable development, development trends, swarm intelligence},
  issn = {3144-2668},
  publisher = {Institute of Central Computation and Knowledge}
}

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Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

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