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Swarm Intelligence in Multi-UAVs

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

Deadline for manuscript submissions: closed (17 January 2025) | Viewed by 21464

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Special Issue Editor

Prof. Dr. Haibin Duan

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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

Special Issue Information

Dear Colleagues,

Research on swarm intelligence in multi-UAVs performing 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 swarm intelligence in multi-UAV systems. To date, efforts in the study of cooperative controller design of swarm intelligence in multi-UAVs 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 “Swarm Intelligence in Multi-UAVs”, 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 swarm intelligence in multi-UAVs, and encourages readers to participate in this promising and challenging research area. For this purpose, papers focusing on new methods and applications of swarm intelligence in multi-UAVs are welcomed including, but not limited to, some of the following topics:

Multi-UAVs’ swarm modeling;
Cooperative strategy and optimization of multi-UAVs;
Multi-UAV swarm control theories and technologies;
Multi-UAVs’ cooperative swarm control;
Swarm intelligence optimization (such as pigeon-inspired optimization) for multi-UAVs;
Distributed consensus for multi-UAVs’ swarm and applications;
Heterogeneous teams’ (combining different vehicles or manned/unmanned systems, end-effectors, and sensors) swarm intelligence.

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
Guest Editor

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

multi-UAVs
swarm intelligence
cooperative control
pigeon-inspired optimization

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Published Papers (10 papers)

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Research

15 pages, 2447 KiB

Open AccessArticle

Autonomous Task Planning of Intelligent Unmanned Aerial Vehicle Swarm Based on Deep Deterministic Policy Gradient

by Qiang Jiang, Yongzhao Yan, Yinxing Dai, Zequan Yang, Huazhen Cao, Bo Wang and Xiaoping Ma

Drones 2025, 9(4), 272; https://doi.org/10.3390/drones9040272 - 3 Apr 2025

Viewed by 368

Abstract

Intelligent swarm is a powerful tool for targeting high-value objectives. Within the Anti-Access/Area Denial (A2/AD) context, an unmanned aerial vehicle (UAV) swarm must leverage its autonomous decision-making capability to execute tasks with independence. This paper focuses on the Suppression of Enemy Air Defenses (SEAD) mission for intelligent stealth UAV swarms. The current research field mainly faces challenges in fully simulating the complexity of real-world scenarios and in insufficient autonomous task planning capabilities. To address these issues, this paper develops a representative problem model, establishes a six-tier standardized simulation environment, and selects the Deep Deterministic Policy Gradient (DDPG) algorithm as the core intelligent algorithm to enhance the autonomous task planning capabilities of UAV swarms. At the algorithm level, this paper designs reward functions corresponding to UAV swarm behaviors, aiming to motivate UAV swarms to adopt more effective action strategies, thereby achieving autonomous task planning. Simulation results demonstrate that the scenario and architectural design are feasible and that artificial intelligence algorithms can enable the UAV swarm to show a higher level of intelligence. Full article

(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)

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23 pages, 9402 KiB

Open AccessArticle

Cooperative Path Planning for Multiple UAVs Based on APF B-RRT* Algorithm

by Cailong Wu, Zhengyu Guo, Jian Zhang, Kai Mao and Delin Luo

Drones 2025, 9(3), 177; https://doi.org/10.3390/drones9030177 - 27 Feb 2025

Cited by 1 | Viewed by 753

Abstract

Aiming at the path planning problem of an unmanned aerial vehicle (UAV) in a complex unknown environment, this paper proposes a cooperative path planning algorithm for multiple UAVs. Using the local environment information, several rolling path plannings are carried out by the Artificial Potential Field Bidirectional-Rapidly exploring Random Trees (APF B-RRT*) algorithm. The APF B-RRT* algorithm optimizes the search space by pre-sampling and adapts with an adaptive step while fusing with the APF algorithm for guiding sampling. Then, the generated path is trimmed and smoothed to obtain the optimized path. Then, through the sampling constraint, several paths can be planned at the same time, which are guaranteed not to collide. The model predictive control (MPC) is used to realize the cooperative control of the UAVs, that is, the UAVs reached the destination simultaneously along the planned path. This algorithm achieves some progress in solving the problems of slow convergence speed, an unstable result and an unsmooth path in UAV path planning. Simulation and comparison show that the APF B-RRT* algorithm has certain advantages in algorithm performance. Full article

(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)

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21 pages, 3322 KiB

Open AccessArticle

Consensus-Based Formation Control for Heterogeneous Multi-Agent Systems in Complex Environments

by Xiaofei Chang, Yiming Yang, Zhuo Zhang, Jiayue Jiao, Haoyu Cheng and Wenxing Fu

Drones 2025, 9(3), 175; https://doi.org/10.3390/drones9030175 - 26 Feb 2025

Viewed by 525

Abstract

The purpose of this paper is to develop formation control strategies for heterogeneous multi-intelligent-agent systems in complex environments, with the goal of enhancing their performance, reliability, and stability. Complex flight conditions, such as navigating narrow gaps in urban high-rise buildings, pose considerable challenges for agent control. To address these challenges, this paper proposes a consensus-based formation strategy that integrates graph theory and multi-consensus algorithms. This approach incorporates time-varying group consistency to strengthen fault tolerance and reduce interference while ensuring obstacle avoidance and formation maintenance in dynamic environments. Through a Lyapunov stability analysis, combined with minimum dwell time constraints and the LaSalle invariance principle, this work proves the convergence of the proposed control scheme under changing network topologies. Simulation results confirm that the proposed strategy significantly improves system performance, mission execution capability, autonomy, synergy, and robustness, thereby enabling agents to successfully maintain formation and avoid obstacles in both homogeneous and heterogeneous clusters in complex environments. Full article

(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)

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33 pages, 12646 KiB

Open AccessArticle

A Binocular Vision-Assisted Method for the Accurate Positioning and Landing of Quadrotor UAVs

by Jie Yang, Kunling He, Jie Zhang, Jiacheng Li, Qian Chen, Xiaohui Wei and Hanlin Sheng

Drones 2025, 9(1), 35; https://doi.org/10.3390/drones9010035 - 6 Jan 2025

Viewed by 792

Abstract

This paper introduces a vision-based target recognition and positioning system for UAV mobile landing scenarios, addressing challenges such as target occlusion due to shadows and the loss of the field of view. A novel image preprocessing technique is proposed, utilizing finite adaptive histogram equalization in the HSV color space, to enhance UAV recognition and the detection of markers under shadow conditions. The system incorporates a Kalman filter-based target motion state estimation method and a binocular vision-based depth camera target height estimation method to achieve precise positioning. To tackle the problem of poor controller performance affecting UAV tracking and landing accuracy, a feedforward model predictive control (MPC) algorithm is integrated into a mobile landing control method. This enables the reliable tracking of both stationary and moving targets via the UAV. Additionally, with a consideration of the complexities of real-world flight environments, a mobile tracking and landing control strategy based on airspace division is proposed, significantly enhancing the success rate and safety of UAV mobile landings. The experimental results demonstrate a 100% target recognition success rate and high positioning accuracy, with x and y-axis errors not exceeding 0.01 m in close range, the x-axis relative error not exceeding 0.05 m, and the y-axis error not exceeding 0.03 m in the medium range. In long-range situations, the relative errors for both axes do not exceed 0.05 m. Regarding tracking accuracy, both KF and EKF exhibit good following performance with small steady-state errors when the target is stationary. Under dynamic conditions, EKF outperforms KF with better estimation results and a faster tracking speed. The landing accuracy is within 0.1 m, and the proposed method successfully accomplishes the mobile energy supply mission for the vehicle-mounted UAV system. Full article

(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)

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15 pages, 3900 KiB

Open AccessArticle

Coverage Path Planning of UAV Based on Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization

by Yan Jiang, Tingting Bai, Daobo Wang and Yin Wang

Drones 2024, 8(2), 50; https://doi.org/10.3390/drones8020050 - 4 Feb 2024

Cited by 4 | Viewed by 2101

Abstract

In contrast to rotorcraft, fixed-wing unmanned aerial vehicles (UAVs) encounter a unique challenge in path planning due to the necessity of accounting for the turning radius constraint. This research focuses on coverage path planning, aiming to determine optimal trajectories for fixed-wing UAVs to thoroughly explore designated areas of interest. To address this challenge, the Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization algorithm (LP-FCMPIO) is proposed. Initially considering the turning radius constraint, a linear-programming-based model for fixed-wing UAV coverage path planning is established. Subsequently, to partition multiple areas effectively, an improved fuzzy clustering algorithm is introduced. Employing the pigeon-inspired optimization algorithm as the final step, an approximately optimal solution is sought. Simulation experiments demonstrate that the LP-FCMPIO, when compared to traditional FCM, achieves a more balanced clustering effect. Additionally, in contrast to traditional PIO, the planned flight paths display improved coverage of task areas, with an approximately 27.5% reduction in the number of large maneuvers. The experimental results provide validation for the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)

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24 pages, 3292 KiB

Open AccessArticle

Fault-Tolerant Event-Triggrred Control for Multiple UAVs with Predefined Tracking Performance

by Ziyuan Ma, Huajun Gong and Xinhua Wang

Drones 2024, 8(1), 25; https://doi.org/10.3390/drones8010025 - 19 Jan 2024

Cited by 2 | Viewed by 1903

Abstract

This paper proposes an event-triggered fault-tolerant time-varying formation control method dedicated to multiple unmanned aerial vehicles (UAVs). We meticulously design a formation-tracking controller with a predefined tracking performance to accommodate the presence of actuator faults and external disturbances. Firstly, the formation-tracking controller acquires the desired heading using the line-of-sight algorithm. Secondly, in the presence of actuator faults and external disturbances, we introduce the radial basis function neural network (RBFNN) and adaptive law tracking control to effectively compensate for their effects. Additionally, we design adaptive tracking controllers and event-triggering conditions to increase the computational frequency. The predefined tracking performance, implemented via a Lyapunov function, ensures the convergence of the tracking error over time. Finally, we conduct a thorough analysis of the system’s stability, successfully eliminating the possibility of Zeno behavior. The simulation results thoroughly validate the effectiveness of the theoretical analysis. Full article

(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)

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26 pages, 8511 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Robust Control for UAV Close Formation Using LADRC via Sine-Powered Pigeon-Inspired Optimization

by Guangsong Yuan and Haibin Duan

Drones 2023, 7(4), 238; https://doi.org/10.3390/drones7040238 - 29 Mar 2023

Cited by 6 | Viewed by 2414

Abstract

This paper designs a robust close-formation control system with dynamic estimation and compensation to advance unmanned aerial vehicle (UAV) close-formation flights to an engineer-implementation level. To characterize the wake vortex effect and analyze the sweet spot, a continuous horseshoe vortex method with high estimation accuracy is employed to model the wake vortex. The close-formation control system will be implemented in the trailing UAV to steer it to the sweet spot and hold its position. Considering the dynamic characteristics of the trailing UAV, the designed control system is divided into three control subsystems for the longitudinal, altitude, and lateral channels. Using linear active-disturbance rejection control (LADRC), the control subsystem of each channel is composed of two cascaded first-order LADRC controllers. One is responsible for the outer-loop position control and the other is used to stabilize the inner-loop attitude. This control system scheme can significantly reduce the coupling effects between channels and effectively suppress the transmission of disturbances caused by the wake vortex effect. Due to the cascade structure of the control subsystem, the correlation among the control parameters is very high. Therefore, sine-powered pigeon-inspired optimization is proposed to optimize the control parameters for the control subsystem of each channel. The simulation results for two UAV close formations show that the designed control system can achieve stable and robust dynamic performance within the expected error range to maximize the aerodynamic benefits for a trailing UAV. Full article

(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)

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25 pages, 15866 KiB

Open AccessArticle

Multi-Conflict-Based Optimal Algorithm for Multi-UAV Cooperative Path Planning

by Xiaoxiong Liu, Yuzhan Su, Yan Wu and Yicong Guo

Drones 2023, 7(3), 217; https://doi.org/10.3390/drones7030217 - 21 Mar 2023

Cited by 8 | Viewed by 3047

Abstract

Multi-UAV cooperative path planning can improve the efficiency of task completion. To deal with the space and time conflicts of multi-UAVs in complex environments, a multi-collision-based multi-UAV cooperative path planning algorithm, multi-conflict-based search (MCBS), is proposed. First, the flight and cooperative constraints of UAV are analyzed, and a three-dimensional environment model is established that incorporates geographical information. Then, hierarchical optimization is used to design collaborative algorithms. In the low-level path design, UAV flight constraints are combined with a sparse A* algorithm, and by improving the cost function, the search space is reduced, and the search time is shortened. In high-level cooperation, the priorities of different conflicts are set, heuristic information is introduced to guide the constraint tree to grow in the direction of satisfying the constraints, and the optimal path set is searched by the best priority search algorithm to reduce the convergence time. Finally, the planning results of the proposed algorithm, the traditional CBS algorithm, and the sparse A* algorithm for different UAV tasks are compared, and the influence of the optimization parameters on the calculation results is discussed. The simulation results show that the proposed algorithm can solve cooperative conflict between UAVs, improve the efficiency of path searches, and quickly find the optimal safe cooperative path that satisfies flight and cooperative constraints. Full article

(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)

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20 pages, 7780 KiB

Open AccessArticle

Distributed Bearing-Only Formation Control for UAV-UWSV Heterogeneous System

by Shaoshi Li, Xingjian Wang, Shaoping Wang and Yuwei Zhang

Drones 2023, 7(2), 124; https://doi.org/10.3390/drones7020124 - 10 Feb 2023

Cited by 15 | Viewed by 2803

Abstract

This paper investigates the bearing-only formation control problem of a heterogeneous multi-vehicle system, which includes unmanned aerial vehicles (UAVs) and unmanned surface vehicles (UWSVs). The interactions among vehicles are described by a particular class of directed and acyclic graphs, namely heterogeneous leader-first follower (HLFF) graphs. Under the HLFF structure, a UAV is selected as the leader, moving with the reference dynamics, while the followers, including both UAVs and UWSVs, are responsible for controlling the position with regard to the neighbors in the formation. To solve the problem, we propose a velocity-estimation-based control scheme, which consists of a distributed observer for estimating the reference velocity of each vehicle and a distributed formation control law for achieving the desired formation based on the estimations and bearing measurements. Moreover, it is shown that the translation and scale of the formation can be uniquely determined by the leader UAV. The theoretical analysis demonstrated the finite-time convergence of the velocity estimation and the asymptotic convergence of the formation tracking. Comparative simulation results are provided to substantiate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)

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16 pages, 2915 KiB

Open AccessArticle

Formation Control Algorithm of Multi-UAVs Based on Alliance

by Yan Jiang, Tingting Bai and Yin Wang

Drones 2022, 6(12), 431; https://doi.org/10.3390/drones6120431 - 19 Dec 2022

Cited by 4 | Viewed by 3758

Abstract

Among the key technologies of Multi-Unmanned Aerial Vehicle (UAV) leader–follower formation control, formation reconfiguration technology is an important element to ensure that multiple UAVs can successfully complete their missions in a complex operating environment. This paper investigates the problem of formation reconfiguration due to battlefield mission requirements. Firstly, in response to the mission requirements, the article proposes the Ant Colony Pheromone Partitioning Algorithm to subgroup the formation. Secondly, the paper establishes the alliance for the obtained subgroups. For the problem of no leader within the alliance formed after grouping or reconfiguring, the Information Concentration Competition Mechanism is introduced to flexibly select information leaders. For the problem of the stability of alliance structure problem, the control law of the Improved Artificial Potential Field method is designed, which can effectively form a stable formation to avoid collision of UAVs in the alliance. Thirdly, the Lyapunov approach is employed for convergence analysis. Finally, the simulation results of multi-UAV formation control show that the partitioning algorithm and the competition mechanism proposed can form a stable alliance as well as deal with the no-leader in it, and the improved artificial potential field designed can effectively avoid collision of the alliance and also prove the highly efficient performance of the algorithm in this paper. Full article

(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)

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