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Distributed Control, Optimization, and Game of UAV Swarm Systems (2nd...
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Distributed Control, Optimization, and Game of UAV Swarm Systems (2nd Edition)

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

Deadline for manuscript submissions: 10 November 2025 | Viewed by 4079

Special Issue Editors

Dr. Zhi Feng

E-Mail Website
Guest Editor
School of Automation Science and Engineering, Beihang University, Beijing 100191, China
Interests: UAV; cooperative control; distributed optimization; game theory and NE seeking; security and resilience
Prof. Dr. Rui Yan

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: reach-avoid differential games; neuro-symbolic stochastic games; multi-agent reinforcement learning
Dr. Yishi Liu

E-Mail Website
Guest Editor
School of Cyber Science and Technology, Beihang University, Beijing 100191, China
Interests: resilient control of swarm system; security for cyber–physical systems; fault-tolerant control
Dr. Xiaoduo Li

E-Mail Website
Guest Editor
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: cooperative control; intelligent decision-making; task allocation and trajectory planning
Dr. Qing Wang

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: cooperative control of multiagent systems; multiagent reinforcement learning

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of Drones on “Distributed Control, Optimization, and Game of UAV Swarm Systems (2nd Edition)”.

UAV swarm systems can also be named multi-UAV systems consisting of multiple UAVs with neighboring interactions and have broad potential applications in various areas, such as intelligent transportation, disaster rescue, and cooperative detection. Distributed control, optimization, and game of UAV swarm systems have been a hot research topic in many scientific communities, especially the control and robotics communities. The main challenge is designing the controller or protocol using only neighboring relative information. Distributed control, optimization, and game of UAV swarm systems are promising because the emerging behavior features low cost, high scalability and flexibility, great robustness, and easy maintenance. Motivated by the facts stated above, more and more researchers are devoting themselves to obtaining sound results on this topic.

This Special Issue aims to collect papers (original research articles and review papers) to give insights about distributed control, optimization, and game of UAV swarm systems.

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

  • Distributed control;
  • Formation control;
  • Distributed optimization;
  • Intelligent motion planning;
  • Game of UAV swarm systems;
  • Distributed Nash equilibrium seeking.

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

Dr. Zhi Feng
Prof. Dr. Rui Yan
Dr. Yishi Liu
Dr. Xiaoduo Li
Dr. Qing Wang
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

  • UAV swarm systems
  • distributed control
  • formation control
  • distributed optimization
  • intelligent motion planning
  • swarm game
  • distributed Nash equilibrium seeking

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

Related Special Issue

Published Papers (4 papers)

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Research

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26 pages, 14110 KiB  
Article
Gemini: A Cascaded Dual-Agent DRL Framework for Task Chain Planning in UAV-UGV Collaborative Disaster Rescue
by Mengxuan Wen, Yunxiao Guo, Changhao Qiu, Bangbang Ren, Mengmeng Zhang and Xueshan Luo
Drones 2025, 9(7), 492; https://doi.org/10.3390/drones9070492 - 11 Jul 2025
Viewed by 422
Abstract
In recent years, UAV (unmanned aerial vehicle)-UGV (unmanned ground vehicle) collaborative systems have played a crucial role in emergency disaster rescue. To improve rescue efficiency, heterogeneous network and task chain methods are introduced to cooperatively develop rescue sequences within a short time for [...] Read more.
In recent years, UAV (unmanned aerial vehicle)-UGV (unmanned ground vehicle) collaborative systems have played a crucial role in emergency disaster rescue. To improve rescue efficiency, heterogeneous network and task chain methods are introduced to cooperatively develop rescue sequences within a short time for collaborative systems. However, current methods also overlook resource overload for heterogeneous units and limit planning to a single task chain in cross-platform rescue scenarios, resulting in low robustness and limited flexibility. To this end, this paper proposes Gemini, a cascaded dual-agent deep reinforcement learning (DRL) framework based on the Heterogeneous Service Network (HSN) for multiple task chains planning in UAV-UGV collaboration. Specifically, this framework comprises a chain selection agent and a resource allocation agent: The chain selection agent plans paths for task chains, and the resource allocation agent distributes platform loads along generated paths. For each mission, a well-trained Gemini can not only allocate resources in load balancing but also plan multiple task chains simultaneously, which enhances the robustness in cross-platform rescue. Simulation results show that Gemini can increase rescue effectiveness by approximately 60% and improve load balancing by approximately 80%, compared to the baseline algorithm. Additionally, Gemini’s performance is stable and better than the baseline in various disaster scenarios, which verifies its generalization. Full article
(This article belongs to the Special Issue Distributed Control, Optimization, and Game of UAV Swarm Systems (2nd Edition))
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39 pages, 1775 KiB  
Article
A Survey on UAV Control with Multi-Agent Reinforcement Learning
by Chijioke C. Ekechi, Tarek Elfouly, Ali Alouani and Tamer Khattab
Drones 2025, 9(7), 484; https://doi.org/10.3390/drones9070484 - 9 Jul 2025
Viewed by 819
Abstract
Unmanned Aerial Vehicles (UAVs) have become increasingly prevalent in both governmental and civilian applications, offering significant reductions in operational costs by minimizing human involvement. There is a growing demand for autonomous, scalable, and intelligent coordination strategies in complex aerial missions involving multiple Unmanned [...] Read more.
Unmanned Aerial Vehicles (UAVs) have become increasingly prevalent in both governmental and civilian applications, offering significant reductions in operational costs by minimizing human involvement. There is a growing demand for autonomous, scalable, and intelligent coordination strategies in complex aerial missions involving multiple Unmanned Aerial Vehicles (UAVs). Traditional control techniques often fall short in dynamic, uncertain, or large-scale environments where decentralized decision-making and inter-agent cooperation are crucial. A potentially effective technique used for UAV fleet operation is Multi-Agent Reinforcement Learning (MARL). MARL offers a powerful framework for addressing these challenges by enabling UAVs to learn optimal behaviors through interaction with the environment and each other. Despite significant progress, the field remains fragmented, with a wide variety of algorithms, architectures, and evaluation metrics spread across domains. This survey aims to systematically review and categorize state-of-the-art MARL approaches applied to UAV control, identify prevailing trends and research gaps, and provide a structured foundation for future advancements in cooperative aerial robotics. The advantages and limitations of these techniques are discussed along with suggestions for further research to improve the effectiveness of MARL application to UAV fleet management. Full article
(This article belongs to the Special Issue Distributed Control, Optimization, and Game of UAV Swarm Systems (2nd Edition))
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17 pages, 6404 KiB  
Article
A Cooperative Decision-Making and Control Algorithm for UAV Formation Based on Non-Cooperative Game Theory
by Yongkang Jiao, Wenxing Fu, Xinying Cao, Kunhu Kou, Ji Tang, Rusong Shen, Yiyang Zhang and Haibo Du
Drones 2024, 8(12), 698; https://doi.org/10.3390/drones8120698 - 21 Nov 2024
Viewed by 1304
Abstract
The formation control problem of distributed fixed-wing Unmanned Aerial Vehicles (UAVs) is investigated in this paper. By utilizing the theoretical foundations of non-cooperative game theory, a novel control strategy is introduced, which allows UAVs to autonomously determine the optimal flight trajectory without relying [...] Read more.
The formation control problem of distributed fixed-wing Unmanned Aerial Vehicles (UAVs) is investigated in this paper. By utilizing the theoretical foundations of non-cooperative game theory, a novel control strategy is introduced, which allows UAVs to autonomously determine the optimal flight trajectory without relying on centralized coordination while concurrently mitigating conflicts with other UAVs. By transforming the UAV model into a double integrator form, the control complexity is reduced. Additionally, the incorporation of a homogeneous differential disturbance observer enhances the UAV’s resilience against disturbances during the control process. Through the development and validation of a Nash equilibrium-based algorithm, it is demonstrated that UAVs can sustain a predefined formation flight and autonomously adapt their trajectories in complex environments. Simulations are presented to confirm the efficiency of the proposed method. Full article
(This article belongs to the Special Issue Distributed Control, Optimization, and Game of UAV Swarm Systems (2nd Edition))
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Other

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19 pages, 488 KiB  
Technical Note
Distributed Constrained Optimization Algorithms for Drones
by Hongzhe Liu
Drones 2025, 9(1), 36; https://doi.org/10.3390/drones9010036 - 6 Jan 2025
Viewed by 790
Abstract
The present study addresses a critical issue within the realm of drones: the challenge of distributed constrained optimization. Our research delves into an optimization scenario where the decision variable is confined to a closed convex set. The primary objective is to develop a [...] Read more.
The present study addresses a critical issue within the realm of drones: the challenge of distributed constrained optimization. Our research delves into an optimization scenario where the decision variable is confined to a closed convex set. The primary objective is to develop a distributed algorithm capable of tackling this optimization problem. To achieve this, we have crafted distributed algorithms for both balanced graphs and unbalanced graphs, with the method of feasible direction employed to address the considered constraint, and the method of estimating left eigenvector to address the unbalance, incorporating momentum elements. We have demonstrated that the algorithms exhibit linear convergence when the local objective functions are both smooth and strongly convex, and when the step-sizes are appropriately chosen. Additionally, the simulation outcomes validate the efficacy of our distributed algorithms. Full article
(This article belongs to the Special Issue Distributed Control, Optimization, and Game of UAV Swarm Systems (2nd Edition))
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