Embodied Intelligence for Drones: Perception, Control, and Decision-Making

Dear Colleagues,

Recent advances in embodied intelligence, foundation models, multi-modal perception, and autonomous robotics are rapidly transforming drones from remotely operated platforms into intelligent embodied agents capable of perceiving, understanding, interacting with, and adapting to complex physical environments. Unlike conventional autonomous drones that separately address perception, control, or navigation, embodied intelligence emphasizes the tight integration of sensing, reasoning, planning, decision-making, and action through continuous interaction with the environment.

With the rapid development of vision–language–action models, world models, reinforcement learning, digital twins, and large-scale multi-modal models, drones are becoming increasingly capable of performing long-horizon autonomous missions in dynamic and uncertain environments. These advances have opened new opportunities for applications such as infrastructure inspection, emergency response, precision agriculture, environmental monitoring, logistics, public safety, and intelligent transportation. Meanwhile, numerous challenges remain in robust perception, adaptive control, autonomous decision-making, collaborative intelligence, and real-world deployment.

This Special Issue aims to provide an international forum for researchers and practitioners working on embodied intelligence for drones. It seeks to present recent theoretical advances, algorithmic innovations, system architectures, and practical applications that enable intelligent drones to perceive, reason, control, and make autonomous decisions in complex environments.

The Special Issue welcomes interdisciplinary contributions spanning artificial intelligence, aerial robotics, control, computer vision, machine learning, and autonomous aerial systems. It aligns closely with the scope of Drones by promoting cutting-edge research on intelligent unmanned aerial systems and their real-world applications.

Topics of interest include, but are not limited to, the following:

1. Embodied intelligence for autonomous drones.
2. Multimodal onboard perception and aerial environment understanding for UAVs.
3. Autonomous navigation, planning, and decision-making for drones in complex and GPS-denied environments.
4. Intelligent unmanned flight control and adaptive control.
5. Vision–language–action and foundation models for drones.
6. Reinforcement learning, imitation learning, and learning-based autonomy for unmanned aerial systems.
7. Multi-drone cooperation and swarm intelligence.
8. Human–drone interaction, teleoperation, and shared autonomy in aerial robotic systems.
9. Digital twins, high-fidelity aerial simulation, and sim-to-real transfer for drone systems.
10. Real-world applications of embodied drone systems, including infrastructure inspection, precision agriculture, logistics, environmental monitoring, and disaster response.

Article Types: Original research articles, review papers, communications, and application-oriented case studies.

Prof. Dr. Yuezu Lv
Prof. Dr. Peihu Duan
Dr. Xiaoxu Lyu
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• embodied intelligence
• drones
• unmanned aerial systems
• intelligent perception
• autonomous control
• decision-making
• autonomous navigation
• vision–language–action models
• reinforcement learning
• multi-agent systems