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Drones
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Advanced Cross-Domain Unmanned Platform Command and Security Technology
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Advanced Cross-Domain Unmanned Platform Command and Security Technology

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

Deadline for manuscript submissions: 10 May 2025 | Viewed by 4943

Special Issue Editors

Prof. Dr. Haibin Duan

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: unmanned aerial vehicles swarms; autonomous navigation; autonomous control
Special Issues, Collections and Topics in MDPI journals
Dr. Jialong Zhang

E-Mail Website
Guest Editor
Test Center, National University of Defense Technology, Xi’an 710100, China
Interests: UAV swarm cooperative control; tracking control; operational mission planning
Dr. Shuangxi Liu

E-Mail Website
Guest Editor
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
Interests: UAV; swarm intelligence; cooperative guidance and control; game theory

Special Issue Information

Dear Colleagues,

In the era of rapid technological advancement, the concept of cross-domain unmanned platforms has emerged, encompassing various unmanned vehicles such as UAVs, UGVs, and UUVs operating across different domains. This development is rooted in the growing demand for efficient and versatile systems, underscoring its significant scientific background. The need for coordinated and secure operations is paramount, for instance, in civilian uses such as disaster response and infrastructure inspection. To achieve seamless cooperation, effective command and control are essential, relying on advanced communication networks, intelligent algorithms, and real-time data processing. In particular, security technology plays a crucial role in protecting data and systems from cyber threats, such as in the aerospace field. Furthermore, as these technologies become more widespread, ethical and regulatory considerations, including privacy and liability, gain prominence. Overall, research in this area is vital for the advancement and safe application of these technologies. This Special Issue aims to contribute to the expansion of knowledge and innovation in this field.

The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights into the challenges associated with cross-domain operations. Specifically, the focus is on algorithms, architectures, and applications that facilitate seamless interaction among unmanned platforms while ensuring their resilience against emerging security threats. This topic is well-aligned with the journal's scope, as it includes cutting-edge research in drone technology, automation, and cooperative systems. Such contributions are expected to advance both theoretical knowledge and practical applications in the field. We invite submissions that offer insights into theoretical frameworks, empirical studies, and case analyses to foster a comprehensive understanding of this critical area of research.

This Special Issue will welcome manuscripts that link the following themes:

  • Cooperative guidance and control;
  • Intelligent mission planning;
  • Multi-domain operations;
  • Aircraft overall technology;
  • Multi-target allocation technology;
  • Structural design and analysis;
  • Cooperative perception and decision;
  • Heterogeneous cross-domain system;
  • Artificial intelligence and machine learning;
  • Intelligent game theory;
  • Network attacks and security;
  • Security protocols and risk management;
  • Case studies and real-world applications;
  • Ethical and regulatory considerations.

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

Prof. Dr. Haibin Duan
Dr. Jialong Zhang
Dr. Shuangxi Liu
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

  • intelligent control
  • cross-domain
  • command
  • cooperation technologies
  • security
  • heterogeneous UAV swarms
  • unmanned platforms

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

Published Papers (7 papers)

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Research

23 pages, 7999 KiB  
Article
Adaptive Impact-Time-Control Cooperative Guidance Law for UAVs Under Time-Varying Velocity Based on Reinforcement Learning
by Zhenyu Liu, Gang Lei, Yong Xian, Leliang Ren, Shaopeng Li and Daqiao Zhang
Drones 2025, 9(4), 262; https://doi.org/10.3390/drones9040262 - 29 Mar 2025
Viewed by 246
Abstract
In this study, an adaptive impact-time-control cooperative guidance law based on deep reinforcement learning considering field-of-view (FOV) constraints is proposed for high-speed UAVs with time-varying velocity. Firstly, a reinforcement learning framework for the high-speed UAVs’ guidance problem is established. The optimization objective is [...] Read more.
In this study, an adaptive impact-time-control cooperative guidance law based on deep reinforcement learning considering field-of-view (FOV) constraints is proposed for high-speed UAVs with time-varying velocity. Firstly, a reinforcement learning framework for the high-speed UAVs’ guidance problem is established. The optimization objective is to maximize the impact velocity; and the constraints for impact time, dive attacking, and FOV are considered simultaneously. The time-to-go estimation method is improved so that it can be applied to high-speed UAVs with time-varying velocity. Then, in order to improve the applicability and robustness of the agent, environmental uncertainties, including aerodynamic parameter errors, observation noise, and target random maneuvers, are incorporated into the training process. Furthermore, inspired by the RL2 algorithm, the recurrent layer is introduced into both the policy and value network. In this way, the agent can automatically adapt to different mission scenarios by updating the hidden states of the recurrent layer. In addition, a compound reward function is designed to train the agent to satisfy the requirements of impact-time control and dive attack simultaneously. Finally, the effectiveness and robustness of the proposed guidance law are validated through numerical simulations conducted across a wide range of scenarios. Full article
(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology)
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17 pages, 8686 KiB  
Article
Modeling Non-Equilibrium Rarefied Gas Flows Past a Cross-Domain Reentry Unmanned Flight Vehicle Using a Hybrid Macro-/Mesoscopic Scheme
by Weiqi Yang, Jing Men, Bowen Xu, Haixia Ding and Jie Li
Drones 2025, 9(4), 239; https://doi.org/10.3390/drones9040239 - 24 Mar 2025
Viewed by 192
Abstract
The cross-domain reentry unmanned flight vehicle passes through thin atmospheres and dense atmospheres when it comes across atmospheres in the near-space area. For the early transition regime, the classical macroscopic and mesoscopic approaches are either not accurate or computational too expensive. The hybrid [...] Read more.
The cross-domain reentry unmanned flight vehicle passes through thin atmospheres and dense atmospheres when it comes across atmospheres in the near-space area. For the early transition regime, the classical macroscopic and mesoscopic approaches are either not accurate or computational too expensive. The hybrid macro-/mesoscopic method is proposed for simulating rarefied gas flows past a cross-domain reentry spheroid–cone unmanned flight vehicle in the present study. The R26 moment scheme is applied in the main flow from a macroscopic point of view, and the discrete velocity method (DVM) is used for solving the Boltzmann equation from a mesoscopic point of view. The simulation results show that the hybrid macro-/mesoscopic scheme is well-suited for non-equilibrium rarefied gas flows past a cross-domain reentry unmanned flight vehicle. The results obtained in this study are consistent with benchmark results, with a maximum density error of 9%. The maximum errors of the heat transfer coefficient and pressure coefficient are 2% and 4.6%, respectively. In addition, as the Knudsen number (Kn) becomes larger, the thickness of the shock layer at the head of the flight vehicle becomes thicker, and non-equilibrium effects become more critical for the aircraft. Since the Boltzmann–Shakhov equation has only been solved close to the wall of the spacecraft, the computational cost can be considerably saved. Full article
(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology)
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19 pages, 3334 KiB  
Article
A Robust Control Method for the Trajectory Tracking of Hypersonic Unmanned Flight Vehicles Based on Model Predictive Control
by Haixia Ding, Bowen Xu, Weiqi Yang, Yunfan Zhou and Xianyu Wu
Drones 2025, 9(3), 223; https://doi.org/10.3390/drones9030223 - 20 Mar 2025
Viewed by 286
Abstract
Hypersonic unmanned flight vehicles have complex dynamic characteristics, such as nonlinearity, strong coupling, multiple constraints, and uncertainty. Operating in highly complex flight environments, hypersonic unmanned flight vehicles must not only contend with uncertainties and disturbances such as parameter perturbations and noise but also [...] Read more.
Hypersonic unmanned flight vehicles have complex dynamic characteristics, such as nonlinearity, strong coupling, multiple constraints, and uncertainty. Operating in highly complex flight environments, hypersonic unmanned flight vehicles must not only contend with uncertainties and disturbances such as parameter perturbations and noise but also deal with complex task scenarios such as interception and no-fly zone avoidance. These factors collectively pose great challenges on the control performance of the vehicle. To address the challenges of trajectory tracking for the vehicles under complex constraints, this paper proposes a trajectory tracking control method based on model predictive control (MPC). Firstly, a nonlinear dynamic model for hypersonic unmanned flight vehicles is established. Then, a robust model predictive controller is designed and the optimal control law is derived to address the trajectory tracking control problem under complex constraints such as parameter perturbations. Finally, simulation experiments are designed under the conditions of aerodynamic parameter changes in the longitudinal plane and lateral no-fly zone avoidance. The simulation results demonstrate that the vehicle is capable of accurately and rapidly tracking the reference despite aerodynamic parameter perturbations and large-scale lateral maneuvers, thereby validating the effectiveness of the controller. Full article
(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology)
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19 pages, 2858 KiB  
Article
Fast Entry Trajectory Planning Method for Wide-Speed Range UASs
by Weihao Feng, Dongzhu Feng, Pei Dai, Shaopeng Li, Chenkai Zhang and Jiadi Ma
Drones 2025, 9(3), 210; https://doi.org/10.3390/drones9030210 - 15 Mar 2025
Viewed by 388
Abstract
Convex optimization has gained increasing popularity in trajectory planning methods for wide-speed range unmanned aerial systems (UASs) with multiple no-fly zones (NFZs) in the entry phase. To address the issues of slow or even infeasible solutions, a modified fast trajectory planning method using [...] Read more.
Convex optimization has gained increasing popularity in trajectory planning methods for wide-speed range unmanned aerial systems (UASs) with multiple no-fly zones (NFZs) in the entry phase. To address the issues of slow or even infeasible solutions, a modified fast trajectory planning method using the approaches of variable trust regions and adaptive generated initial values is proposed in this paper. A dimensionless energy-based dynamics model detailing the constraints of the entry phase is utilized to formulate the original entry trajectory planning problem. This problem is then transformed into a finite-dimensional convex programming problem, using techniques such as successive linearization and interval trapezoidal discretization. Finally, a variable trust region strategy and an adaptive initial value generation strategy are adopted to accelerate the solving process in complex flight environments. The experimental results imply that the strategy proposed in this paper can significantly reduce the solution time of trajectory planning for wide-speed range UASs in complex environments. Full article
(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology)
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22 pages, 4238 KiB  
Article
A Rule-Based Agent for Unmanned Systems with TDGG and VGD for Online Air Target Intention Recognition
by Li Chen, Jing Yang, Yuzhen Zhou, Yanxiang Ling and Jialong Zhang
Drones 2024, 8(12), 765; https://doi.org/10.3390/drones8120765 - 18 Dec 2024
Viewed by 736
Abstract
Air target intention recognition (ATIR) is critical for unmanned systems in modern air defense operations. Through the analysis of typical air defense combat scenarios, first, the paper defines the intention space and intention parameters of air units based on military experience and domain [...] Read more.
Air target intention recognition (ATIR) is critical for unmanned systems in modern air defense operations. Through the analysis of typical air defense combat scenarios, first, the paper defines the intention space and intention parameters of air units based on military experience and domain knowledge. Then, a rule-based agent for unmanned systems for online intention recognition is proposed, with no training, no tagging, and no big data support, which is not only for intention recognition and parameter prediction, but also for formation identification of air targets. The most critical point of the agent is the introduction and application of a thermal distribution grid graph (TDGG) and virtual grid dictionary (VGD), where the former is used to identify the formation information of air targets, and the latter is used to optimize the storage space and simplify the access process for the large-scale and real-time combat information. Finally, to have a performance evaluation and application analysis for the algorithm, we carried out a data instance analysis of ATIR for unmanned systems and an air defense warfare simulation experiment based on a Wargame platform; the comparative experiments with the classical k-means, FCNIRM, and the sector-based forward search method verified the effectiveness and feasibility of the proposed agent, which characterizes it as a promising tool or baseline model for the battlefield situational awareness tasks of unmanned systems. Full article
(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology)
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33 pages, 3827 KiB  
Article
Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm
by Songyue Han, Mingyu Wang, Junhong Duan, Jialong Zhang and Dongdong Li
Drones 2024, 8(12), 763; https://doi.org/10.3390/drones8120763 - 17 Dec 2024
Viewed by 957
Abstract
In emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries, heterogeneous communication networks, complex [...] Read more.
In emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries, heterogeneous communication networks, complex data fusion, high task latency, and limited equipment endurance. To address these issues, an unmanned emergency support system tailored for emergency rescue scenarios is designed. This system leverages 5G edge computing technology to provide high-speed and flexible network access along with elastic computing power support, reducing the complexity of data fusion across heterogeneous networks. It supports the control and data transmission of drones through the separation of the control plane and the data plane. Furthermore, by applying the Tammer decomposition method to break down the system optimization problem, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed to jointly optimize the system’s energy consumption and latency. Through simulation experiments, the GLSOAG demonstrates significant advantages over the Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO), and Beetle Antennae Search Algorithm (BAS) in terms of convergence speed, optimization accuracy, and stability. The system optimization approach effectively reduces the system’s energy consumption and latency costs. Overall, our work alleviates the pain points faced in rescue scenarios to some extent. Full article
(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology)
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23 pages, 7072 KiB  
Article
Prescribed-Time Cooperative Guidance Law for Multi-UAV with Intermittent Communication
by Wenhui Ma and Xiaowen Guo
Drones 2024, 8(12), 748; https://doi.org/10.3390/drones8120748 - 11 Dec 2024
Viewed by 896
Abstract
Networked cooperation of multi-unmanned-aerial-vehicle (UAV) is significant, but their cooperative guidance presents challenges due to weak cross-domain communication. Given the difficulties in information transmission security, this paper proposes a prescribed-time cooperative guidance law (PTCGL) for networked multiple UAVs with intermittent communication. Supported by [...] Read more.
Networked cooperation of multi-unmanned-aerial-vehicle (UAV) is significant, but their cooperative guidance presents challenges due to weak cross-domain communication. Given the difficulties in information transmission security, this paper proposes a prescribed-time cooperative guidance law (PTCGL) for networked multiple UAVs with intermittent communication. Supported by the directed internal communication, the proposed PTCGL can ensure the simultaneous arrival in the case of the pinning external communication is time-triggered intermittent. In the first stage, PTCGL is designed to combine with a time-scaling function and second-order intermittent pinning consensus. With the desired convergence performance, the convergence time can be pre-specified independent of initial conditions and parameter tuning in theory. In the second stage, begin with the suitable initial conditions at the prescribed convergence time, the multiple UAVs are organized by proportional navigation guidance, so as to ensure the cooperation regardless of weak communication. Finally, simulations are conducted to verify the effectiveness and robustness of the proposed cooperative guidance law. Full article
(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology)
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