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Drones
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Biological UAV Swarm Control
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Biological UAV Swarm Control

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1457

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
Prof. Dr. Youmin Zhang

E-Mail Website
Guest Editor
Department of Mechanical, Industrial and Aerospace Engineering, Concordia Institute of Aerospace Design and Innovation, Concordia University, Montreal, QC H3G 1M8, Canada
Interests: guidance, navigation, and control; fault detection and diagnosis; fault-tolerant control; remote sensing with applications to unmanned aerial/space/ground/marine vehicles; smart grids; smart cities; cyber-physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research on biological UAV swarm control to conduct cooperative complex tasks dates back to the late 1990s. Over the last decade, this research area has blossomed, with many significant efforts devoted to the research and development of biological UAV swarm control systems. To date, efforts in the study of biological UAV swarm controller design for various tasks have been continuously increasing. However, there are still many issues yet to be explored, discovered, and understood. Therefore, we propose this Special Issue on “Biological UAV Swarm Control”, which provides a platform to exhibit the state of the art in this area.

This Special Issue is devoted to collecting the latest developments and achievements in the study of biological UAV swarm control and encourages readers to participate in this promising and challenging research area. For this purpose, papers focusing on new methods and applications of biological UAV swarm control are welcome. Topics of interest include, but are not limited to, the following:

  • Biological mechanism modeling;
  • Biological UAV swarm strategy and optimization;
  • Biological UAV swarm control theories and technologies;
  • Biological UAV swarm control;
  • Bionic intelligence optimization (such as pigeon-inspired optimization) for multi-UAVs;
  • Distributed consensus for biological UAV swarm applications;
  • Heterogeneous teams’ (combining manned/unmanned systems or different vehicles, etc.) biological swarm control.

All submissions will be reviewed following the standard procedures of the journal, and acceptance will be limited to papers requiring only moderate revisions.

Prof. Dr. Haibin Duan
Prof. Dr. Youmin Zhang
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

  • biological swarm intelligence
  • swarm autonomous control
  • pigeon-inspired optimization

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Published Papers (3 papers)

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Research

16 pages, 3094 KiB  
Article
Pigeon-Inspired UAV Swarm Control and Planning Within a Virtual Tube
by Yangqi Lei, Zhikun She and Quan Quan
Drones 2025, 9(5), 333; https://doi.org/10.3390/drones9050333 - 25 Apr 2025
Viewed by 172
Abstract
To guide the movement of a UAV swarm in an obstacle-dense environment, a curved regular virtual tube based on pigeon-inspired optimization (PIO) is planned in this paper. There is no obstacle within the virtual tube, which serves as a safe corridor for UAVs. [...] Read more.
To guide the movement of a UAV swarm in an obstacle-dense environment, a curved regular virtual tube based on pigeon-inspired optimization (PIO) is planned in this paper. There is no obstacle within the virtual tube, which serves as a safe corridor for UAVs. Then, a distributed swarm controller based on a pigeon flocking hierarchical model is proposed, enabling all UAVs to pass through a virtual tube, guaranteeing safety between UAVs and keeping within the virtual tube. Numerical simulations demonstrate the effectiveness of the proposed virtual tube planning and UAV swarm passing-through methods. Full article
(This article belongs to the Special Issue Biological UAV Swarm Control)
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24 pages, 5207 KiB  
Article
Finite-Time Formation Control for Clustered UAVs with Obstacle Avoidance Inspired by Pigeon Hierarchical Behavior
by Zhaoyu Zhang, Yang Yuan and Haibin Duan
Drones 2025, 9(4), 276; https://doi.org/10.3390/drones9040276 - 4 Apr 2025
Viewed by 276
Abstract
To address the formation control issue of multiple unmanned aerial vehicles (UAVs), a finite-time control scheme based on terminal sliding mode (TSM) is investigated in this paper. A quadcopter UAV with the vertical takeoff property is considered, with cascaded kinematics composed of rotational [...] Read more.
To address the formation control issue of multiple unmanned aerial vehicles (UAVs), a finite-time control scheme based on terminal sliding mode (TSM) is investigated in this paper. A quadcopter UAV with the vertical takeoff property is considered, with cascaded kinematics composed of rotational and translational loops. To strengthen the application in the low-cost UAV system, the applied torque is synthesized with an auxiliary rotational system, which can avoid utilizing direct attitude measurement. Furthermore, a terminal sliding mode surface is established and employed in the finite-time formation control protocol (FTFCP) as the driven thrust of multiple UAVs over an undirected topology in the translational system. To maintain the safe flight of the UAV clusters in an environment to avoid collision with obstacles or with other UAV neighbors, a pigeon-hierarchy-inspired obstacle avoidance protocol (PHOAP) is proposed. By imitating the interactive hierarchy that exists among the homing pigeon flocks, the collision avoidance scheme is separately enhanced to generate the repulsive potential field for the leader maneuver target and the follower UAV cluster. Subsequently, the collision avoidance laws based on pigeon homing behavior are combined with the finite-time sliding mode formation protocol, and the applied torque is attached as a cascaded structure in the attitude loop to synthesize an obstacle avoidance cooperative control framework. Finally, simulation scenarios of multiple UAVs to reach a desired formation among obstacles is investigated, and the effectiveness of the proposed approach is validated. Full article
(This article belongs to the Special Issue Biological UAV Swarm Control)
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25 pages, 13143 KiB  
Article
Swarm Maneuver Decision Method Based on Learning-Aided Evolutionary Pigeon-Inspired Optimization for UAV Swarm Air Combat
by Yongbin Sun, Yu Chen, Chen Wei, Bin Li and Yanming Fan
Drones 2025, 9(3), 218; https://doi.org/10.3390/drones9030218 - 18 Mar 2025
Viewed by 251
Abstract
Unmanned aerial vehicle (UAV) swarm dynamic combat poses significant challenges due to its complexity and dynamism. This study introduces a novel approach that addresses these challenges through the development of a swarm maneuver decision method based on the Learning-Aided Evolutionary Pigeon-Inspired Optimization (LAEPIO) [...] Read more.
Unmanned aerial vehicle (UAV) swarm dynamic combat poses significant challenges due to its complexity and dynamism. This study introduces a novel approach that addresses these challenges through the development of a swarm maneuver decision method based on the Learning-Aided Evolutionary Pigeon-Inspired Optimization (LAEPIO) algorithm. This research proceeds systematically as follows: First, a nonlinear model of fixed-wing UAVs and a decision-making system for swarm air combat are established. Next, a situation function is applied to characterize the battlefield environment and quantify the strategic advantages of each side during the engagement. The LAEPIO algorithm is then advanced to tackle sub-tasks in swarm air combat by incorporating a learning-aided evolutionary mechanism. Building upon this foundation, a swarm maneuver decision method is designed, enabling UAV swarms to select optimal strategies from a library of maneuvers after thoroughly assessing the battlefield scenario. Finally, the efficacy and superiority of the proposed method are demonstrated through comprehensive simulations across diverse air combat scenarios. The results show that the average win rate of the proposed algorithm is 36.7% higher than that of similar algorithms. Full article
(This article belongs to the Special Issue Biological UAV Swarm Control)
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