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Drones
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The Conceptual Design Methodology for UAV: New Research and New...
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The Conceptual Design Methodology for UAV: New Research and New Development

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 25 January 2025 | Viewed by 4315

Special Issue Editors

Dr. Jie Li

E-Mail Website
Guest Editor
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: swarm; mission planning; multi-robot systems; UAV
Dr. Jinli Suo

E-Mail Website
Guest Editor
Department of Automation, Tsinghua University, Beijing 100084, China
Interests: computational photography; computer vision; visual navigation
Dr. Meng Guo

E-Mail Website
Guest Editor
Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing 100091, China
Interests: task and motion planning; formal methods; multi-robot systems
Dr. Min Chang

E-Mail Website
Guest Editor
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China
Interests: overall design of unmanned systems; flight dynamics and control of UAV; multi-domain unmanned system design
Special Issues, Collections and Topics in MDPI journals
Dr. Zhaowei Ma

E-Mail Website
Guest Editor
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: autonomous control; environment perception; UAV
Dr. Changyun Wei

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Interests: multi-robot systems; autonomous unmanned systems; applied intelligence

Special Issue Information

Dear Colleagues,

With the advancement of technology and its increasingly widespread application, UAVs have become a key technology in various fields, including aviation, agriculture, mapping, environmental monitoring, and logistics. Flight dynamics and control systems, UAV sensors and perception technologies, energy and power systems, advanced algorithms, and artificial intelligence together comprise the scientific background that new research and development in the field of UAV concept design primarily focus on. The importance of research in the field of the UAV concept design methodology is reflected in the technological and application aspects in a number of ways. (1) Increased efficiency and risk reduction: by designing more efficient and intelligent UAV systems, the efficiency and accuracy of mission execution can be improved while reducing human labor and risks. (2) Exploration of new application areas: the rapid development of UAVs provides opportunities for new application areas. (3) Driving technological innovation and industrial development. Therefore, research into the concept design methodology of UAVs has a significant scientific background and practical significance in advancing the development and application of UAV technology.

The goal of this Special Issue is to collect papers (original research papers and review papers) on innovative directions for drone perception, decision making, and control, new sensors, energy and propulsion systems, and the use of emerging AI technologies such as LLM in drones. This publication welcomes all kinds of papers that adhere to academic norms and do not involve legal conflicts. This includes, but is not limited to, review papers, theoretical research papers, and applied research papers.

This Special Issue will welcome manuscripts that connect to the following themes:

  • Design and implementation of the hybrid aquatic aerial vehicle;
  • New discoveries in UAV energy and power systems;
  • Autonomous localization of UAVs under GPS-denied environment;
  • Application of novel sensing technologies in UAVs;
  • High-precision robust and fast maneuver control of UAVs;
  • Autonomous/cooperative decision and planning for UAVs/swarms;
  • New exploration of large models or new artificial intelligence technologies in the field of drones.

We look forward to receiving your original research articles and reviews.

Dr. Jie Li
Dr. Jinli Suo
Dr. Meng Guo
Dr. Min Chang
Dr. Zhaowei Ma
Dr. Changyun Wei
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. 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.

Keywords

  • unmanned aerial vehicles (UAVs)
  • hybrid aquatic aerial vehicle (HAAV)
  • smart materials and aircraft structures design for drones
  • new energy and power systems for drones
  • GPS-denied autonomous localization of UAVs
  • robust and fast maneuver control for drones
  • emergent behaviors in swarm
  • large language model for swarm interaction

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

Published Papers (3 papers)

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Research

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24 pages, 6852 KiB  
Article
Automatic Landing Control for Fixed-Wing UAV in Longitudinal Channel Based on Deep Reinforcement Learning
by Jinghang Li, Shuting Xu, Yu Wu and Zhe Zhang
Drones 2024, 8(10), 568; https://doi.org/10.3390/drones8100568 - 10 Oct 2024
Cited by 1 | Viewed by 991
Abstract
The objective is to address the control problem associated with the landing process of unmanned aerial vehicles (UAVs), with a particular focus on fixed-wing UAVs. The Proportional–Integral–Derivative (PID) controller is a widely used control method, which requires the tuning of its parameters to [...] Read more.
The objective is to address the control problem associated with the landing process of unmanned aerial vehicles (UAVs), with a particular focus on fixed-wing UAVs. The Proportional–Integral–Derivative (PID) controller is a widely used control method, which requires the tuning of its parameters to account for the specific characteristics of the landing environment and the potential for external disturbances. In contrast, neural networks can be modeled to operate under given inputs, allowing for a more precise control strategy. In light of these considerations, a control system based on reinforcement learning is put forth, which is integrated with the conventional PID guidance law to facilitate the autonomous landing of fixed-wing UAVs and the automated tuning of PID parameters through the use of a Deep Q-learning Network (DQN). A traditional PID control system is constructed based on a fixed-wing UAV dynamics model, with the flight state being discretized. The landing problem is transformed into a Markov Decision Process (MDP), and the reward function is designed in accordance with the landing conditions and the UAV’s attitude, respectively. The state vectors are fed into the neural network framework, and the optimized PID parameters are output by the reinforcement learning algorithm. The optimal policy is obtained through the training of the network, which enables the automatic adjustment of parameters and the optimization of the traditional PID control system. Furthermore, the efficacy of the control algorithms in actual scenarios is validated through the simulation of UAV state vector perturbations and ideal gliding curves. The results demonstrate that the controller modified by the DQN network exhibits a markedly superior convergence effect and maneuverability compared to the unmodified traditional controller. Full article
(This article belongs to the Special Issue The Conceptual Design Methodology for UAV: New Research and New Development)
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25 pages, 9006 KiB  
Article
Large-Scale Solar-Powered UAV Attitude Control Using Deep Reinforcement Learning in Hardware-in-Loop Verification
by Yongzhao Yan, Huazhen Cao, Boyang Zhang, Wenjun Ni, Bo Wang and Xiaoping Ma
Drones 2024, 8(9), 428; https://doi.org/10.3390/drones8090428 - 26 Aug 2024
Viewed by 951
Abstract
Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control [...] Read more.
Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. In this work, a low-level attitude control method based on deep reinforcement learning is proposed for solar-powered unmanned aerial vehicles, which is able to interact with high-fidelity nonlinear systems to discover optimal control laws and can receive and track the target attitude input with an arbitrary high-level control module. Considering the risks of field flight experiments, a hardware-in-loop simulation platform is established that connects the on-board avionics stack with the neural network controller trained in a digital environment. Through flight missions under different altitudes and parameter perturbation, the results show that the controller without re-training has comparable performance with the traditional PID controller, even despite physical delays and mechanical backlash. Full article
(This article belongs to the Special Issue The Conceptual Design Methodology for UAV: New Research and New Development)
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Review

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31 pages, 49489 KiB  
Review
Runway-Free Recovery Methods for Fixed-Wing UAVs: A Comprehensive Review
by Yunxiao Liu, Yiming Wang, Han Li and Jianliang Ai
Drones 2024, 8(9), 463; https://doi.org/10.3390/drones8090463 - 5 Sep 2024
Viewed by 1271
Abstract
Fixed-wing unmanned aerial vehicles (UAVs) have the advantages of long endurance and fast flight speed and are widely used in surveying, mapping, monitoring, and defense fields. However, its conventional take-off and landing methods require runway support. Achieving runway-free recovery is necessary for expanding [...] Read more.
Fixed-wing unmanned aerial vehicles (UAVs) have the advantages of long endurance and fast flight speed and are widely used in surveying, mapping, monitoring, and defense fields. However, its conventional take-off and landing methods require runway support. Achieving runway-free recovery is necessary for expanding the application of fixed-wing UAVs. This research comprehensively reviews the various techniques and scenarios of runway-free recovery of fixed-wing UAVs and summarizes the key technologies. The above methods cover parachute recovery, net recovery, rope recovery, SideArm recovery, deep stall recovery, towed drogue docking recovery, and robotic arm recovery methods within runway-free recovery. Finally, this research discusses the future research directions of runway-free recovery. Full article
(This article belongs to the Special Issue The Conceptual Design Methodology for UAV: New Research and New Development)
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