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Drones
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UAV Swarm Intelligent Control and Decision-Making
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UAV Swarm Intelligent Control and Decision-Making

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 726

Special Issue Editors

Prof. Dr. Youmin Zhang

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Guest Editor
Department of Mechanical, Industrial and Aerospace Engineering, Concordia Institute of Aerospace Design and Innovation, Concordia University, 1455 de Maisonneuve Blvd. W., 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
Prof. Dr. Haibin Duan

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: swarm intelligence; intelligent perception; bionic algorithms
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bin Li

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China
Interests: aircraft guidance and control; aircraft swarm control; spacecraft attitude and orbit control; low-orbit satellite constellation mission management; communication-sensing-control integrated design

Special Issue Information

Dear Colleagues,

Swarm intelligent control and decision-making for unmanned aerial vehicle (UAV) swarms represent a significant frontier in the field of aerospace applications and artificial intelligence. This area focuses on the intelligent decision-making and coordinated control of swarms composed of UAVs, unmanned ground vehicles, unmanned surface vessels, and robots operating in complex and dynamic environments. This area holds significant potential to propel progress in fields such as autonomous systems, aerospace applications, intelligent transportation, and defense and security.

The goal of this Special Issue is to collect original research articles and review papers that provide insights into swarm intelligent control and decision-making for unmanned aerial vehicle (UAV) swarms. This Special Issue aims to bring together experts, scholars, and engineers to present and exchange the latest theoretical advances, key technological breakthroughs, and innovative applications in this domain.

This Special Issue welcomes submissions that include the following themes:

  • Distributed intelligent control and decision-making approaches;
  • Swarm self-organization and embodied intelligent behaviors;
  • Innovative applications of unmanned swarms in complex scenarios.

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

Prof. Dr. Youmin Zhang
Prof. Dr. Haibin Duan
Prof. Dr. Bin Li
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

  • intelligent decision-making
  • intelligent control
  • UAV swarms

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

Published Papers (2 papers)

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Research

19 pages, 3028 KB  
Article
Adaptive Prescribed-Performance Guidance Law for UAVs with Predefined-Time Convergence
by Lihan Sun, Shiyao Li, Ze Yang, Baoqing Yang and Jie Ma
Drones 2026, 10(3), 219; https://doi.org/10.3390/drones10030219 - 20 Mar 2026
Viewed by 127
Abstract
In order to evade interception, advanced aircraft often adopt jump-gliding trajectories to efficiently utilize aerodynamics and achieve complex maneuvers. Precise guidance of UAVs for intercepting such targets is critically challenged due to their high speed and uncertain maneuvers. For terminal guidance scenarios, the [...] Read more.
In order to evade interception, advanced aircraft often adopt jump-gliding trajectories to efficiently utilize aerodynamics and achieve complex maneuvers. Precise guidance of UAVs for intercepting such targets is critically challenged due to their high speed and uncertain maneuvers. For terminal guidance scenarios, the extremely short engagement window necessitates strict convergence within the predefined finite time. While PPC offers a promising framework to ensure such convergence with guaranteed transient performance, it suffers from singularity when target uncertainties drive tracking errors beyond performance bounds. To address these challenges, this paper proposes an adaptive prescribed-performance guidance law with predefined-time convergence for UAVs. Built upon the analysis that jump-gliding targets exhibit predominantly longitudinal oscillatory maneuvers, we first establish a velocity model to characterize their motion uncertainties. Using the derived uncertainty bounds and estimated parameters, a predefined-time performance function (PPF) is then developed and robustly modified to eliminate the singularity risk. By integrating this modified PPC with an adaptive law, the proposed framework achieves robust predefined-time convergence of the line-of-sight angle while simultaneously compensating for unknown target maneuvers. Theoretical analysis verifies the framework’s stability, and simulation results demonstrate its effectiveness in intercepting highly maneuverable targets. Full article
(This article belongs to the Special Issue UAV Swarm Intelligent Control and Decision-Making)
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23 pages, 7885 KB  
Article
UAV Cooperative Search Method Based on Asynchronous Collaborative Hybrid Architecture Under Urban Communication Constraints
by Xiefang Lin, Tingting Bai, Xiqiang Liu and Yong Liu
Drones 2026, 10(3), 179; https://doi.org/10.3390/drones10030179 - 5 Mar 2026
Viewed by 264
Abstract
Cooperative searches by unmanned aerial vehicles (UAVs) have wide applications in urban environments. However, the dense obstacles and limited communication networks in urban settings often lead to repeated searches and inefficient information sharing among UAVs. To address these challenges, this article proposes a [...] Read more.
Cooperative searches by unmanned aerial vehicles (UAVs) have wide applications in urban environments. However, the dense obstacles and limited communication networks in urban settings often lead to repeated searches and inefficient information sharing among UAVs. To address these challenges, this article proposes a novel cooperative strategy named the Asynchronous Collaborative Hybrid Architecture (ACHA), which is tailored for urban flight. Specifically, a digital pheromone mechanism is devised to create artificial potential field to guide UAVs’ search efficiently within local communication constraints. Moreover, UAVs switch between the dual decision mode, namely Chain-Following Mode (CFM) and Tree Expansion Mode (TEM) based on the urban environmental topology. When UAVs arrive at a bifurcation node, the TEM is activated, asynchronously triggering the Collaborative-aware Pruning Search Tree (CPT) algorithm to generate subsequent paths, after which they switch back to CFM. Theoretically, it is demonstrated that the collaborative-aware pruning scheme can avoid the “cooperative benefit trap”, where there is a significant divergence between the actual and predicted cooperative benefits. The simulation results confirm that the proposed method outperforms existing approaches in terms of cooperative search accuracy, collision risk and convergence speed in complex urban search scenarios. Full article
(This article belongs to the Special Issue UAV Swarm Intelligent Control and Decision-Making)
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