Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.6
4.4
Journals
Drones
Special Issues
Biological UAV Swarm Control
share announcement

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 2173

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 5324 KiB  
Article
Distributed Model Predictive Formation Control for UAVs and Cooperative Capability Evaluation of Swarm
by Ming Yang, Xiaoyi Guan, Mingming Shi, Bin Li, Chen Wei and Ka-Fai Cedric Yiu
Drones 2025, 9(5), 366; https://doi.org/10.3390/drones9050366 - 13 May 2025
Viewed by 183
Abstract
This paper utilizes the distributed model predictive control (DMPC) method to investigate the formation control problem of unmanned aerial vehicles (UAVs) in the obstacle environment and establishes cooperative capability evaluation metrics of the swarm. Based on the DMPC approach, the formation cost function [...] Read more.
This paper utilizes the distributed model predictive control (DMPC) method to investigate the formation control problem of unmanned aerial vehicles (UAVs) in the obstacle environment and establishes cooperative capability evaluation metrics of the swarm. Based on the DMPC approach, the formation cost function is constructed to adjust the relative positions and velocities of UAVs, ensuring the desired formation. Additionally, to address the obstacle avoidance problem in the formation, the obstacle avoidance function is designed to provide safe formation control in the obstacle environment. To evaluate the cooperative capability of UAVs, we design evaluation metrics from multiple dimensions to reflect the swarm’s cooperative capability. Finally, the simulation results show the effectiveness of the formation control method with obstacle avoidance and the applicability of the swarm’s cooperative capability evaluation metrics. Full article
(This article belongs to the Special Issue Biological UAV Swarm Control)
►▼ Show Figures

Figure 1

30 pages, 3473 KiB  
Article
Advancing Large Language Models with Enhanced Retrieval-Augmented Generation: Evidence from Biological UAV Swarm Control
by Jin-Xing Hao, Lei Chen and Luyao Meng
Drones 2025, 9(5), 361; https://doi.org/10.3390/drones9050361 - 10 May 2025
Viewed by 191
Abstract
As research on biological unmanned aerial vehicle (UAV) swarm control has blossomed, professionals face increasing time and cognitive pressure in mastering the rapidly growing domain knowledge. Although recent general large language models (LLMs) may augment human cognitive capabilities, they still face significant hallucination [...] Read more.
As research on biological unmanned aerial vehicle (UAV) swarm control has blossomed, professionals face increasing time and cognitive pressure in mastering the rapidly growing domain knowledge. Although recent general large language models (LLMs) may augment human cognitive capabilities, they still face significant hallucination and interpretability issues in domain-specific applications. To address these challenges, this study designs and evaluates a domain-specific LLM for the biological UAV swarm control using an enhanced Retrieval-Augmented Generation (RAG) framework. In particular, this study proposes an element-based chunking strategy to build the domain-specific knowledge base and develops novel hybrid retrieval and reranking modules to improve the classical RAG framework. This study also carefully conducts automatic and expert evaluations of our domain-specific LLM, demonstrating the advantages of our model regarding accuracy, relevance, and human alignment. Full article
(This article belongs to the Special Issue Biological UAV Swarm Control)
►▼ Show Figures

Figure 1

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 307
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)
►▼ Show Figures

Figure 1

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 338
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)
►▼ Show Figures

Figure 1

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 312
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop