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Aerospace
Special Issues
Artificial Intelligence in Drone Applications (2nd Edition)
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Artificial Intelligence in Drone Applications (2nd Edition)

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 3948

Special Issue Editors

Dr. Chao-Yang Lee

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliou 640301, Taiwan
Interests: artificial intelligence; Internet of Things; wireless communication networks; unmanned aerial vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Ang-Hsun Tsai

E-Mail Website
Guest Editor
Department of Communications Engineering, Feng Chia University, Taichung, Taiwan
Interests: artificial intelligence; aerial communication networks; 6G mobile communication network; radio resource management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, advanced artificial intelligence technologies have provided an opportunity for new drone applications, such as surveillance, search and rescue, remote sensing, air pollution monitoring, precision agriculture, and aerial base stations. Therefore, there is a significant interest in the use of deep learning for various applications. However, artificial-intelligence-based methods are data-hungry and require a certain amount of meaningful available data to generate useful results. Therefore, these methods are challenging to implement in drones due to limited resources. Thus, there is an urgent need to develop more advanced methods.

This Special Issue intends to publish original research and review articles that discuss theoretical and practical results in relation to artificial intelligence in drones, with a particular focus on navigation, perception, wireless communication, decisions, control, and civil applications using artificial intelligence technologies.

This Special Issue addresses a broad list of topics related to artificial intelligence in drones. We welcome papers that focus on, but are not limited to, the following topics:

  • Artificial intelligence in drones;
  • Machine learning in drones;
  • Deep learning in drones;
  • Reinforcement learning in drones;
  • Computer vision for the perception, navigation and control of drones;
  • Artificial-intelligence-based flight and exploration of drones;
  • Artificial-intelligence-based wireless communication for drones;
  • Artificial-intelligence-based control schemes for drones;
  • Artificial-intelligence-based path planning drones;
  • Artificial-intelligence-based obstacle avoidance for drones;
  • Artificial-intelligence-based applications in drones.

Dr. Chao-Yang Lee
Dr. Ang-Hsun Tsai
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 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.

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

Published Papers (2 papers)

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18 pages, 1220 KiB  
Article
Decoys Deployment for Missile Interception: A Multi-Agent Reinforcement Learning Approach
by Enver Bildik, Antonios Tsourdos, Adolfo Perrusquía and Gokhan Inalhan
Aerospace 2024, 11(8), 684; https://doi.org/10.3390/aerospace11080684 - 20 Aug 2024
Viewed by 1243
Abstract
Recent advances in radar seeker technologies have considerably improved missile precision and efficacy during target interception. This is especially concerning in the arenas of protection and safety, where appropriate countermeasures against enemy missiles are required to ensure the protection of naval facilities. In [...] Read more.
Recent advances in radar seeker technologies have considerably improved missile precision and efficacy during target interception. This is especially concerning in the arenas of protection and safety, where appropriate countermeasures against enemy missiles are required to ensure the protection of naval facilities. In this study, we present a reinforcement-learning-based strategy for deploying decoys to enhance the survival probability of a target ship against a missile threat. Our approach involves the coordinated operation of three decoys, trained using the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithms. The decoys operate in a leader–follower dynamic with a circular formation to ensure effective coordination. We evaluate the strategy across various parameters, including decoy deployment regions, missile launch directions, maximum decoy speeds, and missile speeds. The results indicate that, decoys trained with the MATD3 algorithm demonstrate superior performance compared to those trained with the MADDPG algorithm. Insights suggest that our decoy deployment strategy, particularly when utilizing MATD3-trained decoys, significantly enhances defensive measures against missile threats. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone Applications (2nd Edition))
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20 pages, 4247 KiB  
Article
A Deep Learning Approach for Trajectory Control of Tilt-Rotor UAV
by Javensius Sembiring, Rianto Adhy Sasongko, Eduardo I. Bastian, Bayu Aji Raditya and Rayhan Ekananto Limansubroto
Aerospace 2024, 11(1), 96; https://doi.org/10.3390/aerospace11010096 - 19 Jan 2024
Cited by 1 | Viewed by 2079
Abstract
This paper investigates the development of a deep learning-based flight control model for a tilt-rotor unmanned aerial vehicle, focusing on altitude, speed, and roll hold systems. Training data is gathered from the X-Plane flight simulator, employing a proportional–integral–derivative controller to enhance flight dynamics [...] Read more.
This paper investigates the development of a deep learning-based flight control model for a tilt-rotor unmanned aerial vehicle, focusing on altitude, speed, and roll hold systems. Training data is gathered from the X-Plane flight simulator, employing a proportional–integral–derivative controller to enhance flight dynamics and data quality. The model architecture, implemented within the TensorFlow framework, undergoes iterative tuning for optimal performance. Testing involved two scenarios: wind-free conditions and wind disturbances. In wind-free conditions, the model demonstrated excellent tracking performance, closely tracking the desired altitude. The model’s robustness is further evaluated by introducing wind disturbances. Interestingly, these disturbances do not significantly impact the model performance. This research has demonstrated data-driven flight control in a tilt-rotor unmanned aerial vehicle, offering improved adaptability and robustness compared to traditional methods. Future work may explore further flight modes, environmental complexities, and the utilization of real test flight data to enhance the model generalizability. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone Applications (2nd Edition))
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