Advanced Flight Dynamics and Decision-Making for UAV Operations

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) have rapidly evolved in both civil and military applications, demanding increasingly sophisticated control systems and decision-making frameworks. As UAV missions become more complex—ranging from autonomous surveillance and infrastructure inspection to cooperative multi-agent systems—there is a growing need to enhance their flight dynamics modeling, real-time adaptability, and intelligent autonomy. This Special Issue focuses on cutting-edge advancements in flight dynamics, control algorithms, and decision-making mechanisms that enable UAVs to operate safely, efficiently, and autonomously in dynamic environments.

In particular, the integration of machine learning and data-driven techniques has opened new frontiers in UAV autonomy. From reinforcement learning for adaptive control to neural networks for system identification, and deep learning for perception and decision-making, these approaches are transforming how UAVs learn, respond, and optimize their operations in uncertain and complex scenarios.

Moreover, the development and deployment of UAV platforms increasingly benefit from advancements in digital design and additive manufacturing, allowing rapid prototyping and structural optimization through 3D printing. These technologies facilitate custom-built UAV components designed for specific mission profiles, including lightweight airframes and sensor housings.

The aim of this Special Issue is to bring together state-of-the-art research that pushes the boundaries of UAV flight control and autonomy. Contributions should align with the scope of Drones, emphasizing novel methodologies, rigorous simulations, experimental validations, and practical approaches that contribute to the scientific and technological advancement of UAV systems. We welcome original research articles, comprehensive reviews, and case studies that address theoretical developments or applied solutions.

Potential topics include, but are not limited to:

• Nonlinear and adaptive flight control
• Autonomous navigation and trajectory optimization
• Multi-agent coordination and swarm intelligence
• AI-enhanced decision-making for UAVs
• Learning-based flight dynamics modeling
• Reinforcement learning and imitation learning for UAVs
• Deep learning for perception, localization, and control
• Real-time sensing, planning, and environment mapping
• Design optimization and rapid prototyping of UAVs using 3D printing
• Integration of additive manufacturing in UAV development workflows

We encourage submissions from academia, industry, and research institutions that explore innovative approaches to the future of UAV operations.

Dr. Paraskevi Zacharia
Dr. Antreas Kantaros
Dr. Elias K. Xidias
Prof. Dr. Antoni Grau
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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-blind 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.

• UAV flight dynamics
• autonomous control
• decision-making
• machine learning
• reinforcement learning
• neural networks
• UAV simulation
• swarm UAVs
• 3D Printing
• UAV design
• additive manufacturing