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Drones
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Intelligent Coordination of UAV Swarm Systems
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Intelligent Coordination of UAV Swarm Systems

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 69222

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Xiwang Dong

E-Mail Website
Guest Editor
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: swarm intelligence; collaborative control; collaborative guidance; collaborative decision-making planning; UAV swarm; UAV flight control and embedded system
Prof. Dr. Mou Chen

grade E-Mail Website
Guest Editor
Department of Automatic Control, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: control systems engineering; electrical engineering; aerospace engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiangke Wang

E-Mail Website
Guest Editor
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410003, China
Interests: swarm intelligence; cooperative control; intelligent decision; flight control; UAV swarm systems
Dr. Fei Gao

E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: drones; autonomous navigation; motion planning; environmental perception; robot swarm; SLAM

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicle (UAV) swarm systems have broad application prospects in many practical fields, such as cooperative surveillance, coordination transportation, communication relay, and so on. UAV swarm systems are promising because the emergent behavior has the features of low cost, high scalability and flexibility, great robustness, and easy maintenance. In order to greatly improve the mission execution efficiency of UAV swarm systems, intelligent coordination theory and technology has become a cutting-edge and difficult research focus in the past several decades. How to design distributed cooperative approaches to realize the organic collaboration of perception and cognition, navigation and positioning, decision and planning, guidance and control, and evaluation and verification is a hot topic of current academia and industry.

This Special Issue originates from "2022 5th IEEE International Conference on Unmanned Systems (IEEE ICUS 2022, https://icus2022.c2.org.cn/index.html), which will be held on 14-16 October 2022, Guangzhou, China. The most exciting and innovative papers related to drones presented at IEEE ICUS 2022 will be selected to be extended and included in this Special Issue.

This Special Issue focuses on the latest research results for the intelligent coordination of UAV swarm systems. Papers are solicited in areas directly related to these topics, including, but not limited to, the following:

  • UAV swarm intelligent perception and cognition;
  • Autonomous navigation and positioning;
  • Intelligent decision making and motion planning;
  • Cooperative guidance;
  • Distributed control;
  • Simulation and experiment verification for UAV swarm systems.

Prof. Dr. Xiwang Dong
Prof. Dr. Mou Chen
Prof. Dr. Xiangke Wang
Dr. Fei Gao
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

  • UAV swarm systems
  • intelligent perception and cognition
  • swarm navigation and positioning
  • autonomous decision and planning
  • cooperative guidance and control
  • UAV simulation and experiment
  • swarm intelligence

Published Papers (25 papers)

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24 pages, 3751 KiB  
Article
A GPS-Adaptive Spoofing Detection Method for the Small UAV Cluster
by Lianxiao Meng, Long Zhang , Lin Yang and Wu Yang
Drones 2023, 7(7), 461; https://doi.org/10.3390/drones7070461 - 11 Jul 2023
Viewed by 2869
Abstract
The small UAV (unmanned aerial vehicle) cluster has become an important trend in the development of UAVs because it has the advantages of being unmanned, having a small size and low cost, and ability to complete many collaborative tasks. Meanwhile, the problem of [...] Read more.
The small UAV (unmanned aerial vehicle) cluster has become an important trend in the development of UAVs because it has the advantages of being unmanned, having a small size and low cost, and ability to complete many collaborative tasks. Meanwhile, the problem of GPS spoofing attacks faced by submachines has become an urgent security problem for the UAV cluster. In this paper, a GPS-adaptive spoofing detection (ASD) method based on UAV cluster cooperative positioning is proposed to solve the above problem. The specific technical scheme mainly includes two detection mechanisms: the GPS spoofing signal detection (SSD) mechanism based on cluster cooperative positioning and the relative security machine optimal marking (RSOM) mechanism. The SSD mechanism starts when the cluster enters the task state, and it can detect all threats to the cluster caused by one GPS signal spoofing source in the task environment; when the function range of the mechanism is exceeded, that is, there is more than one spoofing source and more than one UAV is attacked by different spoofing sources, the RSOM mechanism is triggered. The ASD algorithm proposed in this work can detect spoofing in a variety of complex GPS spoofing threat environments and is able to ensure the cluster formation and task completion. Moreover, it has the advantages of a lightweight calculation level, strong applicability, and high real-time performance. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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24 pages, 5125 KiB  
Article
Minimum-Effort Waypoint-Following Differential Geometric Guidance Law Design for Endo-Atmospheric Flight Vehicles
by Xuesheng Qin, Kebo Li, Yangang Liang and Yuanhe Liu
Drones 2023, 7(6), 369; https://doi.org/10.3390/drones7060369 - 01 Jun 2023
Viewed by 1136
Abstract
To improve the autonomous flight capability of endo-atmospheric flight vehicles, such as cruise missiles, drones, and other small, low-cost unmanned aerial vehicles (UAVs), a novel minimum-effort waypoint-following differential geometric guidance law (MEWFDGGL) is proposed in this paper. Using the classical differential geometry curve [...] Read more.
To improve the autonomous flight capability of endo-atmospheric flight vehicles, such as cruise missiles, drones, and other small, low-cost unmanned aerial vehicles (UAVs), a novel minimum-effort waypoint-following differential geometric guidance law (MEWFDGGL) is proposed in this paper. Using the classical differential geometry curve theory, the optimal guidance problem of endo-atmospheric flight vehicles is transformed into an optimal space curve design problem, where the guidance command is the curvature. On the one hand, the change in speed of the flight vehicle is decoupled from the guidance problem. In this way, the widely adopted constant speed hypothesis in the process of designing the guidance law is eliminated, and, hence, the performance of the proposed MEWFDGGL is not influenced by the varying speed of the flight vehicle. On the other hand, considering the onboard computational burden, a suboptimal form of the MEWFDGGL is proposed to solve the problem, where both the complexity and the computational burden of the guidance law dramatically increase as the number of waypoints increases. The theoretical analysis demonstrates that both the original MEWFDGGL and its suboptimal form can be applied to general waypoint-following tasks with an arbitrary number of waypoints. Finally, the superiority and effectiveness of the proposed MEWFDGGL are verified by a numerical simulation and flight experiments. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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19 pages, 4863 KiB  
Article
A Dual Aircraft Maneuver Formation Controller for MAV/UAV Based on the Hybrid Intelligent Agent
by Luodi Zhao, Yemo Liu, Qiangqiang Peng and Long Zhao
Drones 2023, 7(5), 282; https://doi.org/10.3390/drones7050282 - 22 Apr 2023
Cited by 1 | Viewed by 991
Abstract
This paper proposes a hybrid intelligent agent controller (HIAC) for manned aerial vehicles (MAV)/unmanned aerial vehicles (UAV) formation under the leader–follower control strategy. Based on the high-fidelity three-degrees-of-freedom (DOF) dynamic model of UAV, this method decoupled multiple-input-multiple-output (MIMO) systems into multiple single-input-single-output (SISO) [...] Read more.
This paper proposes a hybrid intelligent agent controller (HIAC) for manned aerial vehicles (MAV)/unmanned aerial vehicles (UAV) formation under the leader–follower control strategy. Based on the high-fidelity three-degrees-of-freedom (DOF) dynamic model of UAV, this method decoupled multiple-input-multiple-output (MIMO) systems into multiple single-input-single-output (SISO) systems. Then, it innovatively combined the deep deterministic policy gradient (DDPG) and the double deep Q network (DDQN) to construct a hybrid reinforcement learning-agent model, which was used to generate onboard desired state commands. Finally, we adopted the dynamic inversion control law and the first-order lag filter to improve the actual flight-control process. Under the working conditions of a continuous S-shaped large overload maneuver for the MAV, the simulations verified that the UAV can achieve accurate tracking for the complex trajectory of the MAV. Compared with the traditional linear quadratic regulator (LQR) and DDPG, the HIAC has better control efficiency and precision. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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17 pages, 7443 KiB  
Article
A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments
by Yue Wang, Xinpeng Li, Xing Zhuang, Fanyu Li and Yutao Liang
Drones 2023, 7(4), 246; https://doi.org/10.3390/drones7040246 - 01 Apr 2023
Cited by 1 | Viewed by 1593
Abstract
Rapidly completing the exploration and construction of unknown environments is an important task of a UAV cluster. However, the formulation of an online autonomous exploration strategy based on a real-time detection map is still a problem that needs to be discussed and optimized. [...] Read more.
Rapidly completing the exploration and construction of unknown environments is an important task of a UAV cluster. However, the formulation of an online autonomous exploration strategy based on a real-time detection map is still a problem that needs to be discussed and optimized. In this paper, we propose a distributed unknown environment exploration framework for a UAV cluster that comprehensively considers the path and terminal state gain, which is called the Distributed Next-Best-Path and Terminal (DNBPT) method. This method calculates the gain by comprehensively calculating the new exploration grid brought by the exploration path and the guidance of the terminal state to the unexplored area to guide the UAV’s next decision. We propose a suitable multistep selective sampling method and an improved Discrete Binary Particle Swarm Optimization algorithm for path optimization. The simulation results show that the DNBPT can realize rapid exploration under high coverage conditions in multiple scenes. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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19 pages, 9835 KiB  
Article
Swarm Cooperative Navigation Using Centralized Training and Decentralized Execution
by Rana Azzam, Igor Boiko and Yahya Zweiri
Drones 2023, 7(3), 193; https://doi.org/10.3390/drones7030193 - 11 Mar 2023
Cited by 3 | Viewed by 2590
Abstract
The demand for autonomous UAV swarm operations has been on the rise following the success of UAVs in various challenging tasks. Yet conventional swarm control approaches are inadequate for coping with swarm scalability, computational requirements, and real-time performance. In this paper, we demonstrate [...] Read more.
The demand for autonomous UAV swarm operations has been on the rise following the success of UAVs in various challenging tasks. Yet conventional swarm control approaches are inadequate for coping with swarm scalability, computational requirements, and real-time performance. In this paper, we demonstrate the capability of emerging multi-agent reinforcement learning (MARL) approaches to successfully and efficiently make sequential decisions during UAV swarm collaborative tasks. We propose a scalable, real-time, MARL approach for UAV collaborative navigation where members of the swarm have to arrive at target locations at the same time. Centralized training and decentralized execution (CTDE) are used to achieve this, where a combination of negative and positive reinforcement is employed in the reward function. Curriculum learning is used to facilitate the sought performance, especially due to the high complexity of the problem which requires extensive exploration. A UAV model that highly resembles the respective physical platform is used for training the proposed framework to make training and testing realistic. The scalability of the platform to various swarm sizes, speeds, goal positions, environment dimensions, and UAV masses has been showcased in (1) a load drop-off scenario, and (2) UAV swarm formation without requiring any re-training or fine-tuning of the agents. The obtained simulation results have proven the effectiveness and generalizability of our proposed MARL framework for cooperative UAV navigation. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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17 pages, 2268 KiB  
Article
Multi-UAV Formation Control in Complex Conditions Based on Improved Consistency Algorithm
by Canhui Tao, Ru Zhang, Zhiping Song, Baoshou Wang and Yang Jin
Drones 2023, 7(3), 185; https://doi.org/10.3390/drones7030185 - 07 Mar 2023
Cited by 4 | Viewed by 2034
Abstract
Formation control is a prerequisite for the formation to complete specified tasks safely and efficiently. Considering non-symmetrical communication interference and network congestion, this article aims to design a control protocol by studying the formation model with communication delay and switching topology. Based on [...] Read more.
Formation control is a prerequisite for the formation to complete specified tasks safely and efficiently. Considering non-symmetrical communication interference and network congestion, this article aims to design a control protocol by studying the formation model with communication delay and switching topology. Based on the requirements during the flight and the features of the motion model, the three-degrees-of-freedom kinematics equation of the UAV is given by using the autopilot model of longitudinal and lateral decoupling. Acceleration, velocity, and angular velocity constraints in all directions are defined according to the requirements of flight performance and maneuverability. The control protocol is adjusted according to the constraints. The results show that the improved control protocol can quickly converge the UAV formation state to the specified value and maintain the specified formation with communication delay and switching topology. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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18 pages, 1287 KiB  
Article
A Novel Semidefinite Programming-based UAV 3D Localization Algorithm with Gray Wolf Optimization
by Zhijia Li, Xuewen Xia and Yonghang Yan
Drones 2023, 7(2), 113; https://doi.org/10.3390/drones7020113 - 07 Feb 2023
Cited by 4 | Viewed by 1562
Abstract
The unmanned aerial vehicle (UAV) network has gained vigorous evolution in recent decades by virtue of its advanced nature, and UAV-based localization techniques have been extensively applied in a variety of fields. In most applications, the data captured by a UAV are only [...] Read more.
The unmanned aerial vehicle (UAV) network has gained vigorous evolution in recent decades by virtue of its advanced nature, and UAV-based localization techniques have been extensively applied in a variety of fields. In most applications, the data captured by a UAV are only useful when associated with its geographic position. Efficient and low-cost positioning is of great significance for the development of UAV-aided technology. In this paper, we investigate an effective three-dimensional (3D) localization approach for multiple UAVs and propose a flipping ambiguity avoidance optimization algorithm. Specifically, beacon UAVs take charge of gaining global coordinates and collecting distance measurements from GPS-denied UAVs. We adopt a semidefinite programming (SDP)-based approach to estimate the global position of the target UAVs. Furthermore, when high noise interference causes missing distance pairs and measurement errors, an improved gray wolf optimization (I-GWO) algorithm is utilized to improve the positioning accuracy. Simulation results show that the proposed approach is superior to a number of alternative approaches. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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24 pages, 3199 KiB  
Article
PPO-Exp: Keeping Fixed-Wing UAV Formation with Deep Reinforcement Learning
by Dan Xu, Yunxiao Guo, Zhongyi Yu, Zhenfeng Wang, Rongze Lan, Runhao Zhao, Xinjia Xie and Han Long
Drones 2023, 7(1), 28; https://doi.org/10.3390/drones7010028 - 31 Dec 2022
Cited by 6 | Viewed by 3320
Abstract
Flocking for fixed-Wing Unmanned Aerial Vehicles (UAVs) is an extremely complex challenge due to fixed-wing UAV’s control problem and the system’s coordinate difficulty. Recently, flocking approaches based on reinforcement learning have attracted attention. However, current methods also require that each UAV makes the [...] Read more.
Flocking for fixed-Wing Unmanned Aerial Vehicles (UAVs) is an extremely complex challenge due to fixed-wing UAV’s control problem and the system’s coordinate difficulty. Recently, flocking approaches based on reinforcement learning have attracted attention. However, current methods also require that each UAV makes the decision decentralized, which increases the cost and computation of the whole UAV system. This paper researches a low-cost UAV formation system consisting of one leader (equipped with the intelligence chip) with five followers (without the intelligence chip), and proposes a centralized collision-free formation-keeping method. The communication in the whole process is considered and the protocol is designed by minimizing the communication cost. In addition, an analysis of the Proximal Policy Optimization (PPO) algorithm is provided; the paper derives the estimation error bound, and reveals the relationship between the bound and exploration. To encourage the agent to balance their exploration and estimation error bound, a version of PPO named PPO-Exploration (PPO-Exp) is proposed. It can adjust the clip constraint parameter and make the exploration mechanism more flexible. The results of the experiments show that PPO-Exp performs better than the current algorithms in these tasks. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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15 pages, 4203 KiB  
Article
Onboard Distributed Trajectory Planning through Intelligent Search for Multi-UAV Cooperative Flight
by Kunfeng Lu, Ruiguang Hu, Zheng Yao and Huixia Wang
Drones 2023, 7(1), 16; https://doi.org/10.3390/drones7010016 - 26 Dec 2022
Cited by 2 | Viewed by 1464
Abstract
Trajectory planning and obstacle avoidance play essential roles in the cooperative flight of multiple unmanned aerial vehicles (UAVs). In this paper, a unified framework for onboard distributed trajectory planning is proposed, which takes full advantage of intelligent discrete and continuous search algorithms. Firstly, [...] Read more.
Trajectory planning and obstacle avoidance play essential roles in the cooperative flight of multiple unmanned aerial vehicles (UAVs). In this paper, a unified framework for onboard distributed trajectory planning is proposed, which takes full advantage of intelligent discrete and continuous search algorithms. Firstly, the Monte Carlo tree search (MCTS) is used as the task allocation algorithm to solve the cooperative obstacle avoidance problem. Taking the task allocation decisions as the constraint, knowledge-based particle swarm optimization (Know-PSO) is used as the optimization algorithm to solve the onboard distributed cooperative trajectory planning problem. Simulation results demonstrate that the proposed intelligent MCTS-PSO search framework is effective and flexible for multiple UAVs to conduct the cooperative trajectory planning and obstacle avoidance. Further, it has been applied in practical experiments and achieved promising results. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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19 pages, 4416 KiB  
Article
A Lightweight Uav Swarm Detection Method Integrated Attention Mechanism
by Chuanyun Wang, Linlin Meng, Qian Gao, Jingjing Wang, Tian Wang, Xiaona Liu, Furui Du, Linlin Wang and Ershen Wang
Drones 2023, 7(1), 13; https://doi.org/10.3390/drones7010013 - 25 Dec 2022
Cited by 4 | Viewed by 1808
Abstract
Aiming at the problems of low detection accuracy and large computing resource consumption of existing Unmanned Aerial Vehicle (UAV) detection algorithms for anti-UAV, this paper proposes a lightweight UAV swarm detection method based on You Only Look Once Version X (YOLOX). This method [...] Read more.
Aiming at the problems of low detection accuracy and large computing resource consumption of existing Unmanned Aerial Vehicle (UAV) detection algorithms for anti-UAV, this paper proposes a lightweight UAV swarm detection method based on You Only Look Once Version X (YOLOX). This method uses depthwise separable convolution to simplify and optimize the network, and greatly simplifies the total parameters, while the accuracy is only partially reduced. Meanwhile, a Squeeze-and-Extraction (SE) module is introduced into the backbone to improve the model′s ability to extract features; the introduction of a Convolutional Block Attention Module (CBAM) in the feature fusion network makes the network pay more attention to important features and suppress unnecessary features. Furthermore, Distance-IoU (DIoU) is used to replace Intersection over Union (IoU) to calculate the regression loss for model optimization, and data augmentation technology is used to expand the dataset to achieve a better detection effect. The experimental results show that the mean Average Precision (mAP) of the proposed method reaches 82.32%, approximately 2% higher than the baseline model, while the number of parameters is only about 1/10th of that of YOLOX-S, with the size of 3.85 MB. The proposed approach is, thus, a lightweight model with high detection accuracy and suitable for various edge computing devices. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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16 pages, 4270 KiB  
Article
Multi-UAV Autonomous Path Planning in Reconnaissance Missions Considering Incomplete Information: A Reinforcement Learning Method
by Yu Chen, Qi Dong, Xiaozhou Shang, Zhenyu Wu and Jinyu Wang
Drones 2023, 7(1), 10; https://doi.org/10.3390/drones7010010 - 23 Dec 2022
Cited by 13 | Viewed by 2960
Abstract
Unmanned aerial vehicles (UAVs) are important in reconnaissance missions because of their flexibility and convenience. Vitally, UAVs are capable of autonomous navigation, which means they can be used to plan safe paths to target positions in dangerous surroundings. Traditional path-planning algorithms do not [...] Read more.
Unmanned aerial vehicles (UAVs) are important in reconnaissance missions because of their flexibility and convenience. Vitally, UAVs are capable of autonomous navigation, which means they can be used to plan safe paths to target positions in dangerous surroundings. Traditional path-planning algorithms do not perform well when the environmental state is dynamic and partially observable. It is difficult for a UAV to make the correct decision with incomplete information. In this study, we proposed a multi-UAV path planning algorithm based on multi-agent reinforcement learning which entails the adoption of centralized training–decentralized execution architecture to coordinate all the UAVs. Additionally, we introduced a hidden state of the recurrent neural network to utilize the historical observation information. To solve the multi-objective optimization problem, We designed a joint reward function to guide UAVs to learn optimal policies under the multiple constraints. The results demonstrate that by using our method, we were able to solve the problem of incomplete information and low efficiency caused by partial observations and sparse rewards in reinforcement learning, and we realized kdiff multi-UAV cooperative autonomous path planning in unknown environment. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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27 pages, 5612 KiB  
Article
A Multi-Agent System Using Decentralized Decision-Making Techniques for Area Surveillance and Intruder Monitoring
by Niki Patrinopoulou, Ioannis Daramouskas, Dimitrios Meimetis, Vaios Lappas and Vassilios Kostopoulos
Drones 2022, 6(11), 357; https://doi.org/10.3390/drones6110357 - 16 Nov 2022
Cited by 3 | Viewed by 2646
Abstract
A decentralized swarm of quadcopters designed for monitoring an open area and detecting intruders is proposed. The system is designed to be scalable and robust. The most important aspect of the system is the swarm intelligent decision-making process that was developed. The rest [...] Read more.
A decentralized swarm of quadcopters designed for monitoring an open area and detecting intruders is proposed. The system is designed to be scalable and robust. The most important aspect of the system is the swarm intelligent decision-making process that was developed. The rest of the algorithms essential for the system to be completed are also described. The designed algorithms were developed using ROS and tested with SITL simulations in the GAZEBO environment. The proposed approach was tested against two other similar surveilling swarms and one approach using static cameras. The addition of the real-time decision-making capability offers the swarm a clear advantage over similar systems, as depicted in the simulation results. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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20 pages, 680 KiB  
Article
A Distributed Task Rescheduling Method for UAV Swarms Using Local Task Reordering and Deadlock-Free Task Exchange
by Jie Li, Runfeng Chen and Ting Peng
Drones 2022, 6(11), 322; https://doi.org/10.3390/drones6110322 - 27 Oct 2022
Cited by 4 | Viewed by 1539
Abstract
Distributed task scheduling is an ongoing concern in the field of multi-vehicles, especially in recent years; UAV swarm performing complex tasks endows it with new characteristics, such as self-organization, scalability, reconfigurability, etc. This requires the swarm to have distributed rescheduling capability to dynamically [...] Read more.
Distributed task scheduling is an ongoing concern in the field of multi-vehicles, especially in recent years; UAV swarm performing complex tasks endows it with new characteristics, such as self-organization, scalability, reconfigurability, etc. This requires the swarm to have distributed rescheduling capability to dynamically include as many unassigned tasks or new tasks as possible, while satisfying tight time constraints. As one of the most advanced rescheduling methods, the Performance Impact (PI)-MaxAss algorithm provides an important reference for this paper. However, its task exchange-based strategy faces the deadlock problem, and the task rescheduling method should not be limited to this. To this end, a new distributed rescheduling method is proposed for UAV swarms, which combines the local task reordering strategy and the improved task exchange strategy. On the one hand, based on the analysis of the fact that the scheduler is unreasonable for individuals, this paper proposes a local task reordering strategy denoted as PI-Reorder, which simply adds the reordering strategy to the recursive inclusion phase of the PI-MinAvg algorithm, so that unassigned tasks or new tasks can be included without relying on the task exchange. On the other hand, from the phenomenon that two or more vehicles occasionally get caught in an infinite cycle of exchanging the same tasks, the deadlock problem of PI-MaxAss is analyzed, which is then solved by introducing a deadlock-free task exchange strategy, where some defined counters are used to detect and isolate the deadlocks. Then, a rescue scenario is used to demonstrate the performance of the proposed methods, PI-Hybrid compared with PI-MaxAss. Monte Carlo simulation results show that, compared with PI-MaxAss, this method can not only increase the number of allocations to varying degrees, but also reduce the average waiting time, while ensuring deadlock avoidance. The methods can be used not only for the secondary optimization of the existing task exchange scheduling algorithms to escape local optima, but also for task reconfiguration of swarm tasks after adding or removing tasks. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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19 pages, 6875 KiB  
Article
A Group Maintenance Method of Drone Swarm Considering System Mission Reliability
by Jinlong Guo, Lizhi Wang and Xiaohong Wang
Drones 2022, 6(10), 269; https://doi.org/10.3390/drones6100269 - 22 Sep 2022
Cited by 6 | Viewed by 2302
Abstract
Based on the characteristics of drone swarm such as low cost, strong integrity, and frequent information exchange, as well as the high cost of timely maintenance of traditional units. This paper proposes a swarm maintenance method based on the reliability assessment of multi-layer [...] Read more.
Based on the characteristics of drone swarm such as low cost, strong integrity, and frequent information exchange, as well as the high cost of timely maintenance of traditional units. This paper proposes a swarm maintenance method based on the reliability assessment of multi-layer complex network missions, which combines the multi-layer complex network system evaluation method with the group maintenance method. On the basis of considering the problem of maintenance grouping cost, the failure mechanism of drones in different modes and the impact of drone maintenance on the system are studied. According to the failure model of single node, the complex network method is used to establish the swarm system’s topology model and evaluate the system mission reliability. The maintenance grouping strategy is optimized by using the multi-objective planning of cost and system mission reliability. Compared with the existing just in time maintenance methods, this method can greatly reduce the total maintenance cost of the swarm system maintenance under the condition of ensuring the high mission robustness of the swarm. In addition, a universal drone swarm mission scenario is used to illustrate the method, and the results verify the feasibility and effectiveness of the method. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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19 pages, 4537 KiB  
Article
Bearing-Based Distributed Formation Control of Unmanned Aerial Vehicle Swarm by Quaternion-Based Attitude Synchronization in Three-Dimensional Space
by Muhammad Baber Sial, Yuwei Zhang, Shaoping Wang, Sara Ali, Xinjiang Wang, Xinyu Yang, Zirui Liao and Zunheng Yang
Drones 2022, 6(9), 227; https://doi.org/10.3390/drones6090227 - 30 Aug 2022
Cited by 7 | Viewed by 2174
Abstract
Most of the recent research on distributed formation control of unmanned aerial vehicle (UAV) swarms is founded on position, distance, and displacement-based approaches; however, a very promising approach, i.e., bearing-based formation control, is still in its infancy and needs extensive research effort. In [...] Read more.
Most of the recent research on distributed formation control of unmanned aerial vehicle (UAV) swarms is founded on position, distance, and displacement-based approaches; however, a very promising approach, i.e., bearing-based formation control, is still in its infancy and needs extensive research effort. In formation control problems of UAVs, Euler angles are mostly used for orientation calculation, but Euler angles are susceptible to singularities, limiting their use in practical applications. This paper proposed an effective method for time-varying velocity and orientation leader agents for distributed bearing-based formation control of quadcopter UAVs in three-dimensional space. It combines bearing-based formation control and quaternion-based attitude control using undirected graph topology between agents without the knowledge of global position and orientation. The performance validation of the control scheme was done with numerical simulations, which depicted that UAV formation achieved the desired geometric pattern, translation, scaling, and rotation in 3D space dynamically. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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23 pages, 9378 KiB  
Article
Multi-UAV Collaboration to Survey Tibetan Antelopes in Hoh Xil
by Rui Huang, Han Zhou, Tong Liu and Hanlin Sheng
Drones 2022, 6(8), 196; https://doi.org/10.3390/drones6080196 - 06 Aug 2022
Cited by 5 | Viewed by 1927
Abstract
Reducing the total mission time is essential in wildlife surveys owing to the dynamic movement of animals throughout their migrating environment and potentially extreme changes in weather. This paper proposed a multi-UAV path planning method for counting various flora and fauna populations, which [...] Read more.
Reducing the total mission time is essential in wildlife surveys owing to the dynamic movement of animals throughout their migrating environment and potentially extreme changes in weather. This paper proposed a multi-UAV path planning method for counting various flora and fauna populations, which can fully use the UAVs’ limited flight time to cover large areas. Unlike the current complete coverage path planning methods, based on sweep and polygon, our work encoded the path planning problem as the satisfiability modulo theory using a one-hot encoding scheme. Each instance generated a set of feasible paths at each iteration and recovered the set of shortest paths after sufficient time. We also flexibly optimized the paths based on the number of UAVs, endurance and camera parameters. We implemented the planning algorithm with four UAVs to conduct multiple photographic aerial wildlife surveys in areas around Zonag Lake, the birthplace of Tibetan antelope. Over 6 square kilometers was surveyed in about 2 h. In contrast, previous human-piloted single-drone surveys of the same area required over 4 days to complete. A generic few-shot detector that can perform effective counting without training on the target object is utilized in this paper, which can achieve an accuracy of over 97%. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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21 pages, 6187 KiB  
Article
A Four-Dimensional Space-Time Automatic Obstacle Avoidance Trajectory Planning Method for Multi-UAV Cooperative Formation Flight
by Jie Zhang, Hanlin Sheng, Qian Chen, Han Zhou, Bingxiong Yin, Jiacheng Li and Mengmeng Li
Drones 2022, 6(8), 192; https://doi.org/10.3390/drones6080192 - 31 Jul 2022
Cited by 11 | Viewed by 2350
Abstract
Trajectory planning of multiple unmanned aerial vehicles (UAVs) is the basis for them to form the formation flight. By considering trajectory planning of multiple UAVs in formation flight in three-dimensional space, a trajectory planning method in four-dimensional space-time is proposed which, firstly, according [...] Read more.
Trajectory planning of multiple unmanned aerial vehicles (UAVs) is the basis for them to form the formation flight. By considering trajectory planning of multiple UAVs in formation flight in three-dimensional space, a trajectory planning method in four-dimensional space-time is proposed which, firstly, according to the formation configuration, adopts the Hungarian algorithm to optimize the formation task allocation. Based on that, by considering the flight safety of UAVs in formation, a hierarchical decomposition algorithm in four-dimensional space-time is innovatively put forward with spatial positions and time constraints both considered. It is applied to trajectory planning and automatic obstacle avoidance under the condition of no communication available between UAVs in the formation. The simulation results illustrated that the proposed method is effective in cooperative trajectory planning and automatic obstacle avoidance in advance for multiple UAVs. Meanwhile, it has been tested in a Swarm Unmanned Aerial System project and boasts quite significant value in engineering applications. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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17 pages, 5257 KiB  
Article
Aircraft Carrier Pose Tracking Based on Adaptive Region in Visual Landing
by Jiexin Zhou, Qiufu Wang, Zhuo Zhang and Xiaoliang Sun
Drones 2022, 6(7), 182; https://doi.org/10.3390/drones6070182 - 21 Jul 2022
Cited by 1 | Viewed by 1589
Abstract
Due to its structural simplicity and its strong anti-electromagnetic ability, landing guidance based on airborne monocular vision has gained more and more attention. Monocular 6D pose tracking of the aircraft carrier is one of the key technologies in visual landing guidance. However, owing [...] Read more.
Due to its structural simplicity and its strong anti-electromagnetic ability, landing guidance based on airborne monocular vision has gained more and more attention. Monocular 6D pose tracking of the aircraft carrier is one of the key technologies in visual landing guidance. However, owing to the large range span in the process of carrier landing, the scale of the carrier target in the image variates greatly. There is still a lack of robust monocular pose tracking methods suitable for this scenario. To tackle this problem, a new aircraft carrier pose tracking algorithm based on scale-adaptive local region is proposed in this paper. Firstly, the projected contour of the carrier target is uniformly sampled to establish local circular regions. Then, the local area radius is adjusted according to the pixel scale of the projected contour to build the optimal segmentation energy function. Finally, the 6D pose tracking of the carrier target is realized by iterative optimization. Experimental results on both synthetic and real image sequences show that the proposed method achieves robust and efficient 6D pose tracking of the carrier target under the condition of large distance span, which meets the application requirements of carrier landing guidance. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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32 pages, 12554 KiB  
Article
MCO Plan: Efficient Coverage Mission for Multiple Micro Aerial Vehicles Modeled as Agents
by Liseth Viviana Campo, Agapito Ledezma and Juan Carlos Corrales
Drones 2022, 6(7), 181; https://doi.org/10.3390/drones6070181 - 21 Jul 2022
Cited by 3 | Viewed by 2007
Abstract
Micro aerial vehicle (MAV) fleets have gained essential recognition in the decision schemes for precision agriculture, disaster management, and other coverage missions. However, they have some challenges in becoming massively deployed. One of them is resource management in restricted workspaces. This paper proposes [...] Read more.
Micro aerial vehicle (MAV) fleets have gained essential recognition in the decision schemes for precision agriculture, disaster management, and other coverage missions. However, they have some challenges in becoming massively deployed. One of them is resource management in restricted workspaces. This paper proposes a plan to balance resources when considering the practical use of MAVs and workspace in daily chores. The coverage mission plan is based on five stages: world abstraction, area partitioning, role allocation, task generation, and task allocation. The tasks are allocated according to agent roles, Master, Coordinator, or Operator (MCO), which describe their flight autonomy, connectivity, and decision skill. These roles are engaged with the partitioning based on the Voronoi-tessellation but extended to heterogeneous polygons. The advantages of the MCO Plan were evident compared with conventional Boustrophedon decomposition and clustering by K-means. The MCO plan achieved a balanced magnitude and trend of heterogeneity between both methods, involving MAVs with few or intermediate resources. The resulting efficiency was tested in the GAMA platform, with gained energy between 2% and 10% in the mission end. In addition, the MCO plan improved mission times while the connectivity was effectively held, even more, if the Firefly algorithm generated coverage paths. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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22 pages, 11040 KiB  
Article
VSAI: A Multi-View Dataset for Vehicle Detection in Complex Scenarios Using Aerial Images
by Jinghao Wang, Xichao Teng, Zhang Li, Qifeng Yu, Yijie Bian and Jiaqi Wei
Drones 2022, 6(7), 161; https://doi.org/10.3390/drones6070161 - 27 Jun 2022
Cited by 6 | Viewed by 4698
Abstract
Arbitrary-oriented vehicle detection via aerial imagery is essential in remote sensing and computer vision, with various applications in traffic management, disaster monitoring, smart cities, etc. In the last decade, we have seen notable progress in object detection in natural imagery; however, such development [...] Read more.
Arbitrary-oriented vehicle detection via aerial imagery is essential in remote sensing and computer vision, with various applications in traffic management, disaster monitoring, smart cities, etc. In the last decade, we have seen notable progress in object detection in natural imagery; however, such development has been sluggish for airborne imagery, not only due to large-scale variations and various spins/appearances of instances but also due to the scarcity of the high-quality aerial datasets, which could reflect the complexities and challenges of real-world scenarios. To address this and to improve object detection research in remote sensing, we collected high-resolution images using different drone platforms spanning a large geographic area and introduced a multi-view dataset for vehicle detection in complex scenarios using aerial images (VSAI), featuring arbitrary-oriented views in aerial imagery, consisting of different types of complex real-world scenes. The imagery in our dataset was captured with a wide variety of camera angles, flight heights, times, weather conditions, and illuminations. VSAI contained 49,712 vehicle instances annotated with oriented bounding boxes and arbitrary quadrilateral bounding boxes (47,519 small vehicles and 2193 large vehicles); we also annotated the occlusion rate of the objects to further increase the generalization abilities of object detection networks. We conducted experiments to verify several state-of-the-art algorithms in vehicle detection on VSAI to form a baseline. As per our results, the VSAI dataset largely shows the complexity of the real world and poses significant challenges to existing object detection algorithms. The dataset is publicly available. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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20 pages, 28275 KiB  
Article
Distance-Based Formation Control for Fixed-Wing UAVs with Input Constraints: A Low Gain Method
by Jiarun Yan, Yangguang Yu and Xiangke Wang
Drones 2022, 6(7), 159; https://doi.org/10.3390/drones6070159 - 27 Jun 2022
Cited by 14 | Viewed by 2165
Abstract
Due to the nonlinear and asymmetric input constraints of the fixed-wing UAVs, it is a challenging task to design controllers for the fixed-wing UAV formation control. Distance-based formation control does not require global positions as well as the alignment of coordinates, which brings [...] Read more.
Due to the nonlinear and asymmetric input constraints of the fixed-wing UAVs, it is a challenging task to design controllers for the fixed-wing UAV formation control. Distance-based formation control does not require global positions as well as the alignment of coordinates, which brings in great convenience for designing a distributed control law. Motivated by the facts mentioned above, in this paper, the problem of distance-based formation of fixed-wing UAVs with input constraints is studied. A low-gain formation controller, which is a generalized gradient controller of the potential function, is proposed. The desired formation can be achieved by the designed controller under the input constraints of the fixed-wing UAVs with proven stability. Finally, the effectiveness of the proposed method is verified by the numerical simulation and the semi-physical simulation. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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13 pages, 1249 KiB  
Article
Multi-UAV Coverage through Two-Step Auction in Dynamic Environments
by Yihao Sun, Qin Tan, Chao Yan, Yuan Chang, Xiaojia Xiang and Han Zhou
Drones 2022, 6(6), 153; https://doi.org/10.3390/drones6060153 - 20 Jun 2022
Cited by 7 | Viewed by 2026
Abstract
The cooperation of multiple unmanned aerial vehicles (Multi-UAV) can effectively solve the area coverage problem. However, developing an online multi-UAV coverage approach remains a challenge due to energy constraints and environmental dynamics. In this paper, we design a comprehensive framework for area coverage [...] Read more.
The cooperation of multiple unmanned aerial vehicles (Multi-UAV) can effectively solve the area coverage problem. However, developing an online multi-UAV coverage approach remains a challenge due to energy constraints and environmental dynamics. In this paper, we design a comprehensive framework for area coverage with multiple energy-limited UAVs in dynamic environments, which we call MCTA (Multi-UAV Coverage through Two-step Auction). Specifically, the online two-step auction mechanism is proposed to select the optimal action. Then, an obstacle avoidance mechanism is designed by defining several heuristic rules. After that, considering energy constraints, we develop the reverse auction mechanism to balance workload between multiple UAVs. Comprehensive experiments demonstrate that MCTA can achieve a high coverage rate while ensuring a low repeated coverage rate and average step deviation in most circumstances. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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15 pages, 19096 KiB  
Article
Bioinspired Environment Exploration Algorithm in Swarm Based on Lévy Flight and Improved Artificial Potential Field
by Chen Wang, Dongliang Wang, Minqiang Gu, Huaxing Huang, Zhaojun Wang, Yutong Yuan, Xiaomin Zhu, Wu Wei and Zhun Fan
Drones 2022, 6(5), 122; https://doi.org/10.3390/drones6050122 - 09 May 2022
Cited by 7 | Viewed by 4052
Abstract
Inspired by the behaviour of animal populations in nature, we propose a novel exploration algorithm based on Lévy flight (LF) and artificial potential field (APF). The agent is extended to the swarm level using the APF method through the LF search environment. Virtual [...] Read more.
Inspired by the behaviour of animal populations in nature, we propose a novel exploration algorithm based on Lévy flight (LF) and artificial potential field (APF). The agent is extended to the swarm level using the APF method through the LF search environment. Virtual leaders generate moving steps to explore the environment through the LF mechanism. To achieve collision-free movement in an unknown constrained environment, a swarm-following mechanism is established, which requires the agents to follow the virtual leader to carry out the LF. The proposed method, combining the advantages of LF and APF which achieve the effect of flocking in an exploration environment, does not rely on complex sensors for environment labelling, memorising, or huge computing power. Agents simply perform elegant and efficient search behaviours as natural creatures adapt to the environment and change formations. The method is especially suitable for the camouflaged flocking exploration environment of bionic robots such as flapping drones. Simulation experiments and real-world experiments on E-puck2 robots were conducted to evaluate the effectiveness of the proposed LF-APF algorithm. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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Review

Jump to: Research

28 pages, 1420 KiB  
Review
A Review of Swarm Robotics in a NutShell
by Muhammad Muzamal Shahzad, Zubair Saeed, Asima Akhtar, Hammad Munawar, Muhammad Haroon Yousaf, Naveed Khan Baloach and Fawad Hussain
Drones 2023, 7(4), 269; https://doi.org/10.3390/drones7040269 - 14 Apr 2023
Cited by 6 | Viewed by 7314
Abstract
A swarm of robots is the coordination of multiple robots that can perform a collective task and solve a problem more efficiently than a single robot. Over the last decade, this area of research has received significant interest from scientists due to its [...] Read more.
A swarm of robots is the coordination of multiple robots that can perform a collective task and solve a problem more efficiently than a single robot. Over the last decade, this area of research has received significant interest from scientists due to its large field of applications in military or civil, including area exploration, target search and rescue, security and surveillance, agriculture, air defense, area coverage and real-time monitoring, providing wireless services, and delivery of goods. This research domain of collective behaviour draws inspiration from self-organizing systems in nature, such as honey bees, fish schools, social insects, bird flocks, and other social animals. By replicating the same set of interaction rules observed in these natural swarm systems, robot swarms can be created. The deployment of robot swarm or group of intelligent robots in a real-world scenario that can collectively perform a task or solve a problem is still a substantial research challenge. Swarm robots are differentiated from multi-agent robots by specific qualifying criteria, including the presence of at least three agents and the sharing of relative information such as altitude, position, and velocity among all agents. Each agent should be intelligent and follow the same set of interaction rules over the whole network. Also, the system’s stability should not be affected by leaving or disconnecting an agent from a swarm. This survey illustrates swarm systems’ basics and draws some projections from its history to its future. It discusses the important features of swarm robots, simulators, real-world applications, and future ideas. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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39 pages, 2281 KiB  
Review
Towards Resilient UAV Swarms—A Breakdown of Resiliency Requirements in UAV Swarms
by Abhishek Phadke and F. Antonio Medrano
Drones 2022, 6(11), 340; https://doi.org/10.3390/drones6110340 - 03 Nov 2022
Cited by 12 | Viewed by 5145
Abstract
UAVs have rapidly become prevalent in applications related to surveillance, military operations, and disaster relief. Their low cost, operational flexibility, and unmanned capabilities make them ideal for accomplishing tasks in areas deemed dangerous for humans to enter. They can also accomplish previous high-cost [...] Read more.
UAVs have rapidly become prevalent in applications related to surveillance, military operations, and disaster relief. Their low cost, operational flexibility, and unmanned capabilities make them ideal for accomplishing tasks in areas deemed dangerous for humans to enter. They can also accomplish previous high-cost and labor-intensive tasks, such as land surveying, in a faster and cheaper manner. Researchers studying UAV applications have realized that a swarm of UAVs working collaboratively on tasks can achieve better results. The dynamic work environment of UAVs makes controlling the vehicles a challenge. This is magnified by using multiple agents in a swarm. Resiliency is a broad concept that effectively defines how well a system handles disruptions in its normal functioning. The task of building resilient swarms has been attempted by researchers for the past decade. However, research on current trends shows gaps in swarm designs that make evaluating the resiliency of such swarms less than ideal. The authors believe that a complete well-defined system built from the ground up is the solution. This survey evaluates existing literature on resilient multi-UAV systems and lays down the groundwork for how best to develop a truly resilient system. Full article
(This article belongs to the Special Issue Intelligent Coordination of UAV Swarm Systems)
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