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Aerospace
Special Issues
Innovations in Unmanned Aerial Vehicle: Design and Development
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Innovations in Unmanned Aerial Vehicle: Design and Development

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 501

Special Issue Editor

Dr. Marco Ciarcià

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Colorado State University, Fort Collins, CO, USA
Interests: eVTOL; robotics; mechatronics; nonlinear and optimal control; small satellites

Special Issue Information

Dear Colleagues,

The rapid evolution of Unmanned Aerial Vehicles (UAVs) has transformed aerospace engineering, enabling diverse applications in areas such as environmental monitoring, disaster response, agriculture, logistics, and defense. This Special Issue seeks to highlight groundbreaking research, emerging technologies, and innovative solutions shaping the future of UAVs.

We invite researchers, industry professionals, and innovators to contribute original research articles, review papers, and technical notes that advance UAV technology. Submissions can cover a broad range of topics, including but not limited to:

  • Design and Optimization: Novel aerodynamic designs, lightweight materials, and propulsion systems aimed at enhancing UAV performance.
  • Flight Control and Navigation: Innovative algorithms for autonomous navigation, path planning, and multi-UAV coordination.
  • Artificial Intelligence (AI) and Machine Learning: AI-driven capabilities for real-time decision-making, obstacle avoidance, and adaptive mission planning.
  • Payload Integration: Customization and integration of sensors, cameras, and specialized equipment for diverse applications.
  • Energy Efficiency: Advances in battery technology, hybrid propulsion systems, and renewable energy integration.
  • Safety and Reliability: Redundancy systems, fail-safe mechanisms, and robust control systems for enhanced operational reliability.
  • Regulatory and Ethical Considerations: Addressing challenges in UAV certification, airspace integration, and societal impacts.

This Special Issue aims to serve as a platform for knowledge exchange, fostering collaboration across academia, industry, and government institutions. Contributions are expected to emphasize innovative solutions, theoretical advancements, and experimental validation.

Join us in shaping the future of UAV technology by sharing your expertise and insights. Let us drive innovation and explore the limitless potential of UAVs together.

Dr. Marco Ciarcià
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Aerospace 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 2400 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.

Keywords

  • unmanned aerial vehicles (UAVs)
  • drone technology
  • UAV design
  • UAV development
  • aerial robotics
  • flight control systems
  • UAV payload integration
  • artificial intelligence (AI) in UAVs
  • UAV swarming
  • UAV navigation and guidance
  • regulations and standards
  • future trends in UAVs

Benefits of Publishing in a Special Issue

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

32 pages, 5465 KiB  
Article
DETEAMSK: A Model-Based Reinforcement Learning Approach to Intelligent Top-Level Planning and Decisions for Multi-Drone Ad Hoc Teamwork by Decoupling the Identification of Teammate and Task
by Penghui Xu, Yu Zhang, Le Hao and Qilin Yan
Aerospace 2025, 12(7), 635; https://doi.org/10.3390/aerospace12070635 - 16 Jul 2025
Viewed by 243
Abstract
The ability to collaborate with new teammates, adapt to unfamiliar environments, and engage in effective planning is essential for multi-drone agents within unmanned combat systems. This paper introduces DETEAMSK (Model-based Reinforcement Learning by Decoupling the Identification of Teammates and Tasks), a model-based reinforcement [...] Read more.
The ability to collaborate with new teammates, adapt to unfamiliar environments, and engage in effective planning is essential for multi-drone agents within unmanned combat systems. This paper introduces DETEAMSK (Model-based Reinforcement Learning by Decoupling the Identification of Teammates and Tasks), a model-based reinforcement learning method in intelligent top-level planning and decisions designed for ad hoc teamwork among multi-drone agents. It specifically addresses integrated reconnaissance and strike missions in urban combat scenarios under varying conditions. DETEAMSK’s performance is evaluated through comprehensive, multidimensional experiments and compared with other baseline models. The results demonstrate that DETEAMSK exhibits superior effectiveness, robustness, and generalization capabilities across a range of task domains. Moreover, the model-based reinforcement learning approach offers distinct advantages over traditional models, such as the PLASTIC-Model, and model-free approaches, like the PLASTIC-Policy, due to its unique “dynamic decoupling identification” feature. This study provides valuable insights for advancing both theoretical and applied research in model-based reinforcement learning methods for multi-drone systems. Full article
(This article belongs to the Special Issue Innovations in Unmanned Aerial Vehicle: Design and Development)
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