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Drones
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Swarm Intelligence-Inspired Planning and Control for Drones
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Swarm Intelligence-Inspired Planning and Control for Drones

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

Deadline for manuscript submissions: 28 August 2025 | Viewed by 2629

Special Issue Editors

Prof. Dr. Kaiyu Qin

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Interests: self-organizing mobile internet communication network technology; aircraft measurement and control communication technology; navigation technology; guidance technology; control technology; aircraft cluster intelligent perception technology; aircraft cluster intelligent control technology, microwave and communication measurement technology; microwave and communication measurement instruments, high-speed signal real-time processing technology; microwave module and component technology; new energy automation technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haitao Nie

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: aircraft overall design; intelligent UAV system overall design
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinliang Shao

E-Mail Website
Guest Editor
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: multiagent system; robust control; matrix analysis with applications in control theory
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lei Shi

E-Mail Website
Guest Editor
School of Artificial Intelligence, Henan University, Zhengzhou, China
Interests: Multi-agent collaborative control; opinion dynamics of social networks; distributed localization of sensor networks
Special Issues, Collections and Topics in MDPI journals
Dr. Mengji Shi

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: design and evaluation of cooperative control algorithm for agent system and its application in aircraft cooperation
Special Issues, Collections and Topics in MDPI journals
Dr. Boxian Lin

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: cooperative control of multi-agent systems; multi-agent systems application in aircraft cooperation
Dr. Yang Zhu

E-Mail Website
Guest Editor
School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China
Interests: disturbance noise rejection control, measurement noise rejection control; synchronization control of multivehicle systems; nonminimum phase systems

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit manuscripts to the MDPI Drones Special Issue on “Swarm Intelligence-Inspired Planning and Control for Drones”.

In recent years, drone swarms have attracted significant attention due to their transformative potential across a diverse range of applications, including search and rescue operations, environmental monitoring, agricultural management, and military missions. Unlike a single drone, swarms operate in a highly coordinated manner, achieving complex objectives with enhanced efficiency and flexibility. The inherent advantages of swarm behavior, such as redundancy, scalability, and robustness, make them ideal for tasks requiring adaptability and precision. However, effectively deploying drone swarms presents unique challenges, necessitating swarm intelligence-inspired planning and control strategies to ensure efficient and reliable operations.

This Special Issue is inspired by the applications of networked drones in complex and variable missions.

Within this context, we invite manuscripts for this Special Issue on “Swarm Intelligence-Inspired Planning and Control for Drones”. Papers are solicited in areas directly related to topics including, but not limited to, those listed below:

(1) Swarm intelligence-based task and path planning for drone swarms;
(2) Cooperative control scheme for drone swarms;
(3) Optimization techniques for swarm behavior and performance;
(4) Collision avoidance and conflict resolution in drone swarms;
(5) Real-time communication and coordination among networked drones;
(6) Applications of swarm intelligence for drones in dynamic and complex environments.

Prof. Dr. Kaiyu Qin
Prof. Dr. Haitao Nie
Prof. Dr. Jinliang Shao
Prof. Dr. Lei Shi
Dr. Mengji Shi
Dr. Boxian Lin
Dr. Yang Zhu
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

  • drone task and path planning
  • cooperative control
  • collision avoidance
  • swarm intelligence-based optimization
  • communication and coordination

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

Published Papers (3 papers)

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Research

39 pages, 5668 KiB  
Article
A Self-Adaptive Improved Slime Mold Algorithm for Multi-UAV Path Planning
by Yuelin Ma, Zeren Zhang, Meng Yao and Guoliang Fan
Drones 2025, 9(3), 219; https://doi.org/10.3390/drones9030219 - 18 Mar 2025
Viewed by 355
Abstract
Multi-UAV path planning presents a critical challenge in Unmanned Aerial Vehicle (UAV) applications, particularly in environments with various obstacles and restrictions. These conditions transform multi-UAV path planning into a complex optimization problem with multiple constraints, significantly reducing the number of feasible solutions and [...] Read more.
Multi-UAV path planning presents a critical challenge in Unmanned Aerial Vehicle (UAV) applications, particularly in environments with various obstacles and restrictions. These conditions transform multi-UAV path planning into a complex optimization problem with multiple constraints, significantly reducing the number of feasible solutions and complicating the generation of optimal flight trajectories. Although the slime mold algorithm (SMA) has proven effective in optimization missions, it still suffers from limitations such as inadequate exploration capacity, premature convergence, and a propensity to become stuck in local optima. These drawbacks degrade its performance in intricate multi-UAV scenarios. This study proposes a self-adaptive improved slime mold algorithm called AI-SMA to address these issues. Firstly, AI-SMA incorporates a novel search mechanism to balance exploration and exploitation by integrating ranking-based differential evolution (rank-DE). Then, a self-adaptive switch operator is introduced to increase population diversity in later iterations and avoid premature convergence. Finally, a self-adaptive perturbation strategy is implemented to provide an effective escape mechanism, facilitating faster convergence. Extensive experiments were conducted on the CEC 2017 benchmark test suite and multi-UAV path-planning scenarios. The results show that AI-SMA improves the quality of optimal fitness by approximately 7.83% over the original SMA while demonstrating superior robustness and effectiveness in generating collision-free trajectories. Full article
(This article belongs to the Special Issue Swarm Intelligence-Inspired Planning and Control for Drones)
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23 pages, 4847 KiB  
Article
Robust Consensus Tracking Control for Multi-Unmanned-Aerial-Vehicle (UAV) System Subjected to Measurement Noise and External Disturbance
by Zhiyuan Zheng, Shiji Tong, Erquan Wang, Yang Zhu and Jinliang Shao
Drones 2025, 9(1), 61; https://doi.org/10.3390/drones9010061 - 16 Jan 2025
Viewed by 743
Abstract
In practice, the consensus performance of a multi-UAV system can degrade significantly due to the presence of measurement noise and disturbances. However, simultaneously rejecting the noise and disturbances to achieve high-precision consensus tracking control is rather challenging. In this paper, to address this [...] Read more.
In practice, the consensus performance of a multi-UAV system can degrade significantly due to the presence of measurement noise and disturbances. However, simultaneously rejecting the noise and disturbances to achieve high-precision consensus tracking control is rather challenging. In this paper, to address this issue, we propose a novel distributed consensus tracking control framework consisting of a distributed observer and a local dual-estimator-based tracking controller. Each UAV’s distributed observer estimates the leader’s states and generates the local reference, functioning even under a switching communication topology. In the local tracking controller design, we reveal that classic uncertainty and disturbance estimator (UDE)-based control can magnify the noise. By combining the measurement error estimator (MEE) with UDE, a local robust tracking controller is designed to reject noise and disturbances simultaneously. The parameter tuning of MEE and UDE is unified into a single parameter, and the monotonic relationship between this parameter and system performance is revealed by the singular perturbation theorem. Finally, the validity of the proposed control framework is verified by both simulation and comparative real-world experiments. Full article
(This article belongs to the Special Issue Swarm Intelligence-Inspired Planning and Control for Drones)
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28 pages, 8679 KiB  
Article
Adaptive Sliding Mode Control of Quadrotor System with Elastic Load Connection of Unknown Mass
by Longchao Ru, Jiale Liu, Binqi Chen, Dengnuo Chen and Zeyin Fan
Drones 2024, 8(12), 708; https://doi.org/10.3390/drones8120708 - 27 Nov 2024
Viewed by 845
Abstract
During quadrotor load transport, the cable’s elasticity exacerbates load fluctuations, which may result in platform instability or a potential crash. This paper introduced a model of the connecting cable as a spring-damper system and established the dynamic model of the suspension system based [...] Read more.
During quadrotor load transport, the cable’s elasticity exacerbates load fluctuations, which may result in platform instability or a potential crash. This paper introduced a model of the connecting cable as a spring-damper system and established the dynamic model of the suspension system based on Newton’s law. Nonsingular fast terminal sliding mode control (NFTSMC) was employed for attitude, position, and anti-swing controller design. Adaptive controllers were integrated into altitude control to address uncertainties related to load mass and cable length. The inclusion of an anti-swing controller into the position control loop effectively dampens load oscillations while ensuring accurate position tracking. Numerical simulations demonstrated that the proposed controller outperforms both the energy-based controller and the conventional linear sliding mode controller. Full article
(This article belongs to the Special Issue Swarm Intelligence-Inspired Planning and Control for Drones)
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