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Drones
Special Issues
Intelligent Cooperative Technologies of UAV Swarm Systems
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Intelligent Cooperative Technologies of UAV Swarm Systems

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Artificial Intelligence in Drones (AID)".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 1158

Special Issue Editors

Prof. Dr. Mou Chen

grade E-Mail Website
Guest Editor
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: control systems engineering; electrical engineering; aerospace engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiwang Dong

E-Mail Website
Guest Editor
1. Institute of Unmanned System, Beihang University, Beijing 100191, China
2. School of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: swarm intelligence; collaborative control; collaborative guidance; collaborative decision-making planning; UAV swarm; UAV flight control and embedded system
Special Issues, Collections and Topics in MDPI journals
Dr. Fei Gao

E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: drones; autonomous navigation; motion planning; environmental perception; robot swarm; SLAM
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the broad applications of UAV swarm systems, e.g., cooperative surveillance, coordination transportation, etc., it has become increasingly important to investigate intelligent coordination theory and technology so as to greatly improve the mission execution efficiency of UAV swarm systems. However, with the increasing complexities associated with operating and task requirements, an unmanned swarm system requires a series of advanced methods with higher efficiency, greater generalization ability and better adaptability than traditional algorithms. The artificial intelligence (AI)-based intelligent cooperative technologies of UAV swarm systems have been becoming a common potential solution to manage the above requirements, recently attracting much attention. Thus, how to develop AI-driven intelligent cooperative technologies to realize the organic collaboration of AI-enabled perception and cognition, navigation and positioning, decision-making and planning, control and optimization, evaluation and verification, etc., is a hot topic of current academia and industry.

The goal of this Special Issue is to collect papers (original research articles and/or review papers) that provide insights about the intelligent cooperative technologies of UAV swarm system. This subject is of significance and relevance to Drones as UAV swarm systems leverage AI-driven intelligent cooperative technologies to enable unmanned aerial vehicles to complete complex tasks together autonomously and efficiently. These technologies are crucial for enhancing mission performance in complex and dynamic environments.

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

  • AI-enabled intelligent cooperative perception and cognition;
  • AI-enabled autonomous navigation and distributed localization in GNSS-denied environments;
  • Intelligent cooperative decision-making and planning in complex and adversarial environments;
  • Distributed learning, control, and optimization for UAV swarm systems;
  • Cooperative/noncooperative/evolutionary games on UAV networks;
  • UAV Swarm countermeasures and confrontation;
  • Simulation and experiment verification for UAV swarm systems.

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

Prof. Dr. Mou Chen
Prof. Dr. Xiwang Dong
Dr. Fei Gao
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.

Keywords

  • AI-enabled drones
  • intelligent perception and cognition
  • autonomous navigation and cooperative localization
  • AI-enabled decision-making and planning
  • countermeasures and confrontation
  • learning-based control and optimization
  • game-theoretical analysis
  • security and resilience

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

Published Papers (3 papers)

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Research

41 pages, 18104 KB  
Article
Cooperative Online 3D Path Planning for Fixed-Wing UAVs
by Yonggang Nie, Xinyue Zhang, Chaoyue Li and Dong Zhang
Drones 2026, 10(4), 297; https://doi.org/10.3390/drones10040297 - 17 Apr 2026
Viewed by 127
Abstract
Addressing high dynamics, stringent non-holonomic constraints, and limited onboard computation in cooperative online trajectory planning for multiple fixed-wing UAVs in complex 3D obstacle environments, this paper proposes a Cooperative-3D-Quick-Dubins-RRT*. First, an offline motion-primitive database is engineered to align with RRT* mechanics: an unconstrained [...] Read more.
Addressing high dynamics, stringent non-holonomic constraints, and limited onboard computation in cooperative online trajectory planning for multiple fixed-wing UAVs in complex 3D obstacle environments, this paper proposes a Cooperative-3D-Quick-Dubins-RRT*. First, an offline motion-primitive database is engineered to align with RRT* mechanics: an unconstrained expansion mode facilitates rapid space exploration, while a constrained rewiring mode ensures kinodynamic continuity. This architecture, synergized with four targeted acceleration strategies (dimensionality reduction, elliptical sampling, tree pruning, and pre-discretized collision checking), significantly accelerates convergence. Second, a Dubins-detour-based time-coordination mechanism is designed to map cooperative timing constraints into controllable path-length adjustments, and the feasible adjustment range is analyzed to ensure realizability. Finally, simulations and hardware-in-the-loop experiments across a variety of representative scenarios are conducted for validation. The results show that, compared with the classical Dubins-RRT*, the proposed method achieves clear advantages in planning time and path length, demonstrating its suitability for online cooperative obstacle-avoidance planning of multiple UAVs. Full article
(This article belongs to the Special Issue Intelligent Cooperative Technologies of UAV Swarm Systems)
29 pages, 10011 KB  
Article
Method for Controlling the Movement of an AUV Follower Based on Visual Information About the Position of the AUV Leader Using Reinforcement Learning Methods
by Evgenii Norenko, Vadim Kramar and Aleksey Kabanov
Drones 2026, 10(4), 282; https://doi.org/10.3390/drones10040282 - 14 Apr 2026
Viewed by 303
Abstract
This paper considers the problem of controlling the motion of an autonomous underwater vehicle (AUV) following a leader in a leader–follower scheme based on visual information about the leader’s position. It is assumed that the leader is equipped with a system of light [...] Read more.
This paper considers the problem of controlling the motion of an autonomous underwater vehicle (AUV) following a leader in a leader–follower scheme based on visual information about the leader’s position. It is assumed that the leader is equipped with a system of light markers with known geometry, and the follower determines its relative position based on data from an onboard camera without using a hydroacoustic communication channel or direct exchange of navigation information. To synthesize the control law, a reinforcement learning method based on the Proximal Policy Optimization algorithm is used. Policy learning is performed in a simulation environment, taking into account the dynamic model of the agent in the horizontal plane and observation noise. A structure of state space, actions, and reward function is proposed, aimed at minimizing the error in relative position and orientation. Additionally, Bayesian optimization of the weight coefficients of the reward function is performed. Bayesian optimization of the reward function weights reduces the RMS tracking error from 0.24 m to 0.09 m and demonstrates that heading regulation has a significantly stronger impact on stability than position penalties. The results of modeling, testing in the Webots environment, and experiments on MiddleAUV class devices confirm the feasibility and scalability of the approach. It is shown that a single trained policy ensures stable formation maintenance when the number of follower agents and initial conditions change without additional retraining. Full article
(This article belongs to the Special Issue Intelligent Cooperative Technologies of UAV Swarm Systems)
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26 pages, 8867 KB  
Article
A Physics-Guided Aeromagnetic Interference Compensation Method for Geomagnetic Sensing in GNSS-Denied UAV Swarm Systems
by Shiyao Wang, Liran Ma, Yue Wang, Dongguang Li and Jianbin Luo
Drones 2026, 10(4), 252; https://doi.org/10.3390/drones10040252 - 31 Mar 2026
Viewed by 413
Abstract
Geomagnetic navigation is a promising alternative for positioning and localization of UAV swarm systems in GNSS-denied environments. However, strong and heterogeneous electromagnetic interference generated by onboard power, propulsion, and electronic subsystems severely degrades magnetic measurement fidelity, limiting the achievable accuracy of cooperative UAV [...] Read more.
Geomagnetic navigation is a promising alternative for positioning and localization of UAV swarm systems in GNSS-denied environments. However, strong and heterogeneous electromagnetic interference generated by onboard power, propulsion, and electronic subsystems severely degrades magnetic measurement fidelity, limiting the achievable accuracy of cooperative UAV swarm navigation. To address this challenge, this paper proposes PG-TLNet, a physics-guided aeromagnetic interference compensation framework based on the extended Tolles–Lawson (T–L) model. By integrating onboard state information (current, voltage, and attitude) with magnetic measurements through physics-consistency constraints and a lightweight multi-branch convolutional neural network, the framework enables robust real-time compensation under strong and time-varying interference while remaining suitable for resource-constrained UAV nodes. Experimental validation using multiple scalar magnetometers under heterogeneous interference conditions, with amplitudes up to 1000 nT, shows that PG-TLNet consistently outperforms the conventional T–L model across all sensing nodes, maintaining residual magnetic interference at approximately 0–30 nT under long-duration and highly dynamic operations. The proposed method achieves an improvement ratio (IR) of up to 15 with an end-to-end inference latency below 94 μs. These results indicate that PG-TLNet meets the practical measurement fidelity requirements for geomagnetic navigation in GNSS-denied environments. By ensuring reliable and consistent magnetic measurements at the individual UAV node level, the proposed framework establishes a practical sensing foundation for geomagnetic navigation and distributed magnetic sensing in UAV swarm systems operating in GNSS-denied environments. Full article
(This article belongs to the Special Issue Intelligent Cooperative Technologies of UAV Swarm Systems)
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