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Drones
Special Issues
Distributed Control, Optimization, and Game of UAV Swarm Systems
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Distributed Control, Optimization, and Game of UAV Swarm Systems

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

Deadline for manuscript submissions: 25 July 2024 | Viewed by 3182

Special Issue Editors

Dr. Yongzhao Hua

E-Mail Website
Guest Editor
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: distributed control; formation control; Intelligent control
Dr. Jianglong Yu

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: cooperative guidance; flight control
Dr. Chao Sun

E-Mail Website
Guest Editor
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: distributed optimization; networked game; cooperative control

Special Issue Information

Dear Colleagues,

UAV swarm systems can also be named as 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 has been a hot research topic in many scientific communities, especially the control and robotics communities. How to design the controller or protocol using only neighboring relative information is the main challenge. Distributed control, optimization, and game of UAV swarm systems is promising due to that the emerging behavior has the features of 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 obtain sound results on this topic.

The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights about distributed control, optimization, and game of UAV swarm systems. The journal and the special issue does not consider the publication of manuscripts related to military operations/applications or that have any explicit reference to military organization.

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. Yongzhao Hua
Dr. Jianglong Yu
Dr. Chao Sun
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

Published Papers (3 papers)

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Research

33 pages, 4285 KiB  
Article
A Path-Planning Method for UAV Swarm under Multiple Environmental Threats
by Xiangyu Fan, Hao Li, You Chen and Danna Dong
Drones 2024, 8(5), 171; https://doi.org/10.3390/drones8050171 - 26 Apr 2024
Viewed by 309
Abstract
To weaken or avoid the impact of dynamic threats such as wind and extreme weather on the real-time path of a UAV swarm, a path-planning method based on improved long short-term memory (LSTM) network prediction parameters was constructed. First, models were constructed for [...] Read more.
To weaken or avoid the impact of dynamic threats such as wind and extreme weather on the real-time path of a UAV swarm, a path-planning method based on improved long short-term memory (LSTM) network prediction parameters was constructed. First, models were constructed for wind, static threats, and dynamic threats during the flight of the drone. Then, it was found that atmospheric parameters are typical time series data with spatial correlation. The LSTM network was optimized and used to process time series parameters to construct a network for predicting atmospheric parameters. The state of the drone was adjusted in real time based on the prediction results to mitigate the impact of wind or avoid the threat of extreme weather. Finally, a path optimization method based on an improved LSTM network was constructed. Through simulation, it can be seen that compared to the path that does not consider atmospheric effects, the optimized path has significantly improved flightability and safety. Full article
(This article belongs to the Special Issue Distributed Control, Optimization, and Game of UAV Swarm Systems)
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18 pages, 1851 KiB  
Article
Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue
by Dan Han, Hao Jiang, Lifang Wang, Xinyu Zhu, Yaqing Chen and Qizhou Yu
Drones 2024, 8(4), 138; https://doi.org/10.3390/drones8040138 - 03 Apr 2024
Viewed by 646
Abstract
Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique [...] Read more.
Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations. Full article
(This article belongs to the Special Issue Distributed Control, Optimization, and Game of UAV Swarm Systems)
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16 pages, 3864 KiB  
Article
Reinforcement Learning-Based Formation Pinning and Shape Transformation for Swarms
by Zhaoqi Dong, Qizhen Wu and Lei Chen
Drones 2023, 7(11), 673; https://doi.org/10.3390/drones7110673 - 13 Nov 2023
Cited by 2 | Viewed by 1585
Abstract
Swarm models hold significant importance as they provide the collective behavior of self-organized systems. Boids model is a fundamental framework for studying emergent behavior in swarms systems. It addresses problems related to simulating the emergent behavior of autonomous agents, such as alignment, cohesion, [...] Read more.
Swarm models hold significant importance as they provide the collective behavior of self-organized systems. Boids model is a fundamental framework for studying emergent behavior in swarms systems. It addresses problems related to simulating the emergent behavior of autonomous agents, such as alignment, cohesion, and repulsion, to imitate natural flocking movements. However, traditional models of Boids often lack pinning and the adaptability to quickly adapt to the dynamic environment. To address this limitation, we introduce reinforcement learning into the framework of Boids to solve the problem of disorder and the lack of pinning. The aim of this approach is to enable drone swarms to quickly and effectively adapt to dynamic external environments. We propose a method based on the Q-learning network to improve the cohesion and repulsion parameters in the Boids model to achieve continuous obstacle avoidance and maximize spatial coverage in the simulation scenario. Additionally, we introduce a virtual leader to provide pinning and coordination stability, reflecting the leadership and coordination seen in drone swarms. To validate the effectiveness of this method, we demonstrate the model’s capabilities through empirical experiments with drone swarms, and show the practicality of the RL-Boids framework. Full article
(This article belongs to the Special Issue Distributed Control, Optimization, and Game of UAV Swarm Systems)
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