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Multi-UAV Networks
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Multi-UAV Networks

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 26822

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

Special Issue Editors

Dr. Zhihong Liu

E-Mail Website
Guest Editor
College of Intelligence and Technology, National University of Defense Technology, Changsha 410073, China
Interests: multi-UAV systems; UAV swarms; cooperative decision and control
Special Issues, Collections and Topics in MDPI journals
Dr. Shihao Yan

E-Mail Website
Guest Editor
School of Science, Edith Cowan University, Perth, Australia 270 Joondalup Drive, Joondalup WA 6027,Australia
Interests: UAV-aided communications; covert communications; covert sensing; location spoofing detection; physical layer security; and IRS-aided wireless communications
Special Issues, Collections and Topics in MDPI journals
Dr. Yirui Cong

E-Mail Website
Guest Editor
College of Intelligence and Technology, National University of Defense Technology, Changsha 410073, China
Interests: control theory; communication theory; filtering theory
Special Issues, Collections and Topics in MDPI journals
Dr. Kehao Wang

E-Mail Website
Guest Editor
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: stochastic optimization; operation research; scheduling; wireless network communications; embedded operating system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, multi-UAV systems have been revealing great potential values in applications such as surveillance, disaster rescue, mapping, cargo delivery, etc. Through coordination among multiple or even a swarm of UAVs, multi-UAV systems can improve the mission capability, survivability, and flexibility. Regarding the key technologies, Multi-UAV networks are the essential building blocks in multi-UAV systems, which provide the foundation for information transmission and further enable team behavior. Although the emergence of 5G and FANET (Flying Ad hoc Network) provides options for improving the Quality of Service (QoS) in multi-UAV networks, the off-the-shelf wireless networks are still not well suited for agile networks. Issues such as unpredictable latency and jitter, and high variance in throughput, may arise, in particular when the scale of the UAVs tends to be large. Besides, the networking and the control problems cannot be isolated while improving the overall performance of multi-UAV systems. How to develop key techniques for the control and navigation of multiple UAVs over wireless networks becomes a necessity, which raises many theoretical and practical open problems in the cross fields of control, estimation, and communications.

The Special Issue solicits key theoretical and practical contributions to networking as well as the control problems for multi-UAV systems. It aims to bring the studies from related fields (control, navigation, and networking) together.

We solicit high-quality original research papers on topics including, but not limited to:

  • Multi-UAV Swarms in 5G Networks
  • Reliable Wireless Networks for Multi-UAV Systems
  • Delay-tolerant Networking (DTN) protocols for Multi-UAV Systems
  • Software Defined Networks (SDN) assisted UAV Networking
  • Cooperative Control of Multi-UAV Systems
  • Networked Control Under Communications Constraints
  • Localization of Mobile Nodes in Wireless Networks
  • Filtering/Distributed Filtering Theory and Applications
  • Adaptive/Learning-based Observer and Parameter Estimation
  • Task Scheduling of Multi-UAV Systems
  • Interference Management of Multi-UAV Systems
  • Resource Allocation of Multi-UAV Systems
  • Multi-Function Device of Multi-UAV Systems

Dr. Zhihong Liu
Dr. Shihao Yan
Dr. Yirui Cong
Dr. Kehao Wang
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

  • multi-UAV systems 
  • UAV networking 
  • UAV communications 
  • cooperative control 
  • networked control 
  • navigation

Published Papers (16 papers)

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Research

14 pages, 3536 KiB  
Article
Object Detection in Drone Video with Temporal Attention Gated Recurrent Unit Based on Transformer
by Zihao Zhou, Xianguo Yu and Xiangcheng Chen
Drones 2023, 7(7), 466; https://doi.org/10.3390/drones7070466 - 12 Jul 2023
Cited by 5 | Viewed by 1576
Abstract
Unmanned aerial vehicle (UAV) based object detection plays a pivotal role in civil and military fields. Unfortunately, the problem is more challenging than general visual object detection due to the significant appearance deterioration in images captured by drones. Considering that video contains more [...] Read more.
Unmanned aerial vehicle (UAV) based object detection plays a pivotal role in civil and military fields. Unfortunately, the problem is more challenging than general visual object detection due to the significant appearance deterioration in images captured by drones. Considering that video contains more abundant visual features and motion information, a better idea for UAV based image object detection is to enhance target appearance in reference frame by aggregating the features in neighboring frames. However, simple feature aggregation methods will frequently introduce the interference of background into targets. To solve this problem, we proposed a more effective module, termed Temporal Attention Gated Recurrent Unit (TA-GRU), to extract effective temporal information based on recurrent neural networks and transformers. TA-GRU works as an add-on module to bring existing static object detectors to high performance video object detectors, with negligible extra computational cost. To validate the efficacy of our module, we selected YOLOv7 as baseline and carried out comprehensive experiments on the VisDrone2019-VID dataset. Our TA-GRU empowered YOLOv7 to not only boost the detection accuracy by 5.86% in the mean average precision (mAP) on the challenging VisDrone dataset, but also to reach a running speed of 24 frames per second (fps). Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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15 pages, 6758 KiB  
Article
Decentralized Multi-UAV Cooperative Exploration Using Dynamic Centroid-Based Area Partition
by Jianjun Gui, Tianyou Yu, Baosong Deng, Xiaozhou Zhu and Wen Yao
Drones 2023, 7(6), 337; https://doi.org/10.3390/drones7060337 - 23 May 2023
Cited by 3 | Viewed by 1430
Abstract
Efficient exploration is a critical issue in swarm UAVs with substantial research interest due to its applications in search and rescue missions. In this study, we propose a cooperative exploration approach that uses multiple unmanned aerial vehicles (UAVs). Our approach allows UAVs to [...] Read more.
Efficient exploration is a critical issue in swarm UAVs with substantial research interest due to its applications in search and rescue missions. In this study, we propose a cooperative exploration approach that uses multiple unmanned aerial vehicles (UAVs). Our approach allows UAVs to explore separate areas dynamically, resulting in increased efficiency and decreased redundancy. We use a novel dynamic centroid-based method to partition the 3D working area for each UAV, with each UAV generating new targets in its partitioned area only using the onboard computational resource. To ensure the cooperation and exploration of the unknown, we use a next-best-view (NBV) method based on rapidly-exploring random tree (RRT), which generates a tree in the partitioned area until a threshold is reached. We compare this approach with three classical methods using Gazebo simulation, including a Voronoi-based area partition method, a coordination method for reducing scanning repetition between UAVs, and a greedy method that works according to its exploration planner without any interaction. We also conduct practical experiments to verify the effectiveness of our proposed method. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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16 pages, 2344 KiB  
Article
A Lightweight Authentication Protocol for UAVs Based on ECC Scheme
by Shuo Zhang, Yaping Liu, Zhiyu Han and Zhikai Yang
Drones 2023, 7(5), 315; https://doi.org/10.3390/drones7050315 - 09 May 2023
Cited by 2 | Viewed by 1971
Abstract
With the rapid development of unmanned aerial vehicles (UAVs), often referred to as drones, their security issues are attracting more and more attention. Due to open-access communication environments, UAVs may raise security concerns, including authentication threats as well as the leakage of location [...] Read more.
With the rapid development of unmanned aerial vehicles (UAVs), often referred to as drones, their security issues are attracting more and more attention. Due to open-access communication environments, UAVs may raise security concerns, including authentication threats as well as the leakage of location and other sensitive data to unauthorized entities. Elliptic curve cryptography (ECC) is widely favored in authentication protocol design due to its security and performance. However, we found it still has the following two problems: inflexibility and a lack of backward security. This paper proposes an ECC-based identity authentication protocol LAPEC for UAVs. LAPEC can guarantee the backward secrecy of session keys and is more flexible to use. The time cost of LAPEC was analyzed, and its overhead did not increase too much when compared with other authentication methods. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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26 pages, 9247 KiB  
Article
BCDAIoD: An Efficient Blockchain-Based Cross-Domain Authentication Scheme for Internet of Drones
by Gongzhe Qiao, Yi Zhuang, Tong Ye and Yuan Qiao
Drones 2023, 7(5), 302; https://doi.org/10.3390/drones7050302 - 04 May 2023
Cited by 1 | Viewed by 1697
Abstract
During long-distance flight, unmanned aerial vehicles (UAVs) need to perform cross-domain authentication to prove their identity and receive information from the ground control station (GCS). However, the GCS needs to verify all drones arriving at the area it is responsible for, which leads [...] Read more.
During long-distance flight, unmanned aerial vehicles (UAVs) need to perform cross-domain authentication to prove their identity and receive information from the ground control station (GCS). However, the GCS needs to verify all drones arriving at the area it is responsible for, which leads to the GCS being unable to complete authentication in time when facing cross-domain requests from a large number of drones. Additionally, due to potential threats from attackers, drones and GCSs are likely to be deceived. To improve the efficiency and security of cross-domain authentication, we propose an efficient blockchain-based cross-domain authentication scheme for the Internet of Drones (BCDAIoD). By using a consortium chain with a multi-chain architecture, the proposed method can query and update different types of data efficiently. By mutual authentication before cross-domain authentication, drones can compose drone groups to lighten the authentication workload of domain management nodes. BCDAIoD uses the notification mechanism between domains to enable path planning for drones in advance, which can further improve the efficiency of cross-domain authentication. The performance of BCDAIoD was evaluated through experiments. The results show that the cross-domain authentication time cost and computational overhead of BCDAIoD are significantly lower those of than existing methods when the number of drones is large. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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24 pages, 2957 KiB  
Article
Research on the Cooperative Passive Location of Moving Targets Based on Improved Particle Swarm Optimization
by Li Hao, Fan Xiangyu and Shi Manhong
Drones 2023, 7(4), 264; https://doi.org/10.3390/drones7040264 - 12 Apr 2023
Cited by 4 | Viewed by 1211
Abstract
Aiming at the cooperative passive location of moving targets by UAV swarm, this paper constructs a passive location and tracking algorithm for a moving target based on the A optimization criterion and the improved particle swarm optimization (PSO) algorithm. Firstly, the localization method [...] Read more.
Aiming at the cooperative passive location of moving targets by UAV swarm, this paper constructs a passive location and tracking algorithm for a moving target based on the A optimization criterion and the improved particle swarm optimization (PSO) algorithm. Firstly, the localization method of cluster cooperative passive localization is selected and the measurement model is constructed. Then, the problem of improving passive location accuracy is transformed into the problem of obtaining more target information. From the perspective of information theory, using the A criterion as the optimization target, the passive localization process for static targets is further deduced. The Recursive Neural Network (RNN) is used to predict the probability distribution of the target’s location in the next moment so as to improve the localization method and make it suitable for the localization of moving targets. The particle swarm algorithm is improved by using grouping and time period strategy, and the algorithm flow of moving target location is constructed. Finally, through the simulation verification and algorithm comparison, the advantages of the algorithm in this paper are presented. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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21 pages, 482 KiB  
Article
Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array
by Yifan Li, Feng Shu, Jinsong Hu, Shihao Yan, Haiwei Song, Weiqiang Zhu, Da Tian, Yaoliang Song and Jiangzhou Wang
Drones 2023, 7(4), 256; https://doi.org/10.3390/drones7040256 - 10 Apr 2023
Cited by 3 | Viewed by 1212
Abstract
To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order [...] Read more.
To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number. Therefore, we perform feature extraction on the the eigenvalue sequence of a sample covariance matrix to construct a feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM) and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70% with the massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive MIMO arrays, and they also have much worse performance than machine learning-based classifiers. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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17 pages, 1370 KiB  
Article
Intelligent Mining Road Object Detection Based on Multiscale Feature Fusion in Multi-UAV Networks
by Xinkai Xu, Shuaihe Zhao, Cheng Xu, Zhuang Wang, Ying Zheng, Xu Qian and Hong Bao
Drones 2023, 7(4), 250; https://doi.org/10.3390/drones7040250 - 05 Apr 2023
Cited by 5 | Viewed by 1655
Abstract
In complex mining environments, driverless mining trucks are required to cooperate with multiple intelligent systems. They must perform obstacle avoidance based on factors such as the site road width, obstacle type, vehicle body movement state, and ground concavity-convexity. Targeting the open-pit mining area, [...] Read more.
In complex mining environments, driverless mining trucks are required to cooperate with multiple intelligent systems. They must perform obstacle avoidance based on factors such as the site road width, obstacle type, vehicle body movement state, and ground concavity-convexity. Targeting the open-pit mining area, this paper proposes an intelligent mining road object detection (IMOD) model developed using a 5G-multi-UAV and a deep learning approach. The IMOD model employs data sensors to monitor surface data in real time within a multisystem collaborative 5G network. The model transmits data to various intelligent systems and edge devices in real time, and the unmanned mining card constructs the driving area on the fly. The IMOD model utilizes a convolutional neural network to identify obstacles in front of driverless mining trucks in real time, optimizing multisystem collaborative control and driverless mining truck scheduling based on obstacle data. Multiple systems cooperate to maneuver around obstacles, including avoiding static obstacles, such as standing and lying dummies, empty oil drums, and vehicles; continuously avoiding multiple obstacles; and avoiding dynamic obstacles such as walking people and moving vehicles. For this study, we independently collected and constructed an obstacle image dataset specific to the mining area, and experimental tests and analyses reveal that the IMOD model maintains a smooth route and stable vehicle movement attitude, ensuring the safety of driverless mining trucks as well as of personnel and equipment in the mining area. The ablation and robustness experiments demonstrate that the IMOD model outperforms the unmodified YOLOv5 model, with an average improvement of approximately 9.4% across multiple performance measures. Additionally, compared with other algorithms, this model shows significant performance improvements. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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17 pages, 1414 KiB  
Article
Connectivity-Maintenance UAV Formation Control in Complex Environment
by Liangbin Zhu, Cheng Ma, Jinglei Li, Yue Lu and Qinghai Yang
Drones 2023, 7(4), 229; https://doi.org/10.3390/drones7040229 - 26 Mar 2023
Cited by 1 | Viewed by 1427
Abstract
Cooperative formation control is the research basis for various tasks in the multi-UAV network. However, in a complex environment with different interference sources and obstacles, it is difficult for multiple UAVs to maintain their connectivity while avoiding obstacles. In this paper, a Connectivity-Maintenance [...] Read more.
Cooperative formation control is the research basis for various tasks in the multi-UAV network. However, in a complex environment with different interference sources and obstacles, it is difficult for multiple UAVs to maintain their connectivity while avoiding obstacles. In this paper, a Connectivity-Maintenance UAV Formation Control (CMUFC) algorithm is proposed to help multi-UAV networks maintain their communication connectivity by changing the formation topology adaptively under interference and reconstructing the broken communication topology of a multi-UAV network. Furthermore, through the speed-based artificial potential field (SAPF), this algorithm helps the multi-UAV formation to avoid various obstacles. Simulation results verify that the CMUFC algorithm is capable of forming, maintaining, and reconstructing multi-UAV formation in complex environments. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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22 pages, 576 KiB  
Article
Distributed Offloading for Multi-UAV Swarms in MEC-Assisted 5G Heterogeneous Networks
by Mingfang Ma and Zhengming Wang
Drones 2023, 7(4), 226; https://doi.org/10.3390/drones7040226 - 24 Mar 2023
Cited by 4 | Viewed by 1598
Abstract
Mobile edge computing (MEC) is a novel paradigm that offers numerous possibilities for Internet of Things (IoT) applications. In typical use cases, unmanned aerial vehicles (UAVs) that can be applied to monitoring and logistics have received wide attention. However, subject to their own [...] Read more.
Mobile edge computing (MEC) is a novel paradigm that offers numerous possibilities for Internet of Things (IoT) applications. In typical use cases, unmanned aerial vehicles (UAVs) that can be applied to monitoring and logistics have received wide attention. However, subject to their own flexible maneuverability, limited computational capability, and battery energy, UAVs need to offload computation-intensive tasks to ensure the quality of service. In this paper, we solve this problem for UAV systems in a 5G heterogeneous network environment by proposing an innovative distributed framework that jointly considers transmission assessment and task offloading. Specifically, we devised a fuzzy logic-based offloading assessment mechanism at the UAV side, which can adaptively avoid risky wireless links based on the motion state of an UAV and performance transmission metrics. We introduce a multi-agent advantage actor–critic deep reinforcement learning (DRL) framework to enable the UAVs to optimize the system utility by learning the best policies from the environment. This requires decisions on computing modes as well as the choices of radio access technologies (RATs) and MEC servers in the case of offloading. The results validate the convergence and applicability of our scheme. Compared with the benchmarks, the proposed scheme is superior in many aspects, such as reducing task completion delay and energy consumption. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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13 pages, 398 KiB  
Article
UAV Deployment Optimization for Secure Precise Wireless Transmission
by Tong Shen, Guiyang Xia, Jingjing Ye, Lichuan Gu, Xiaobo Zhou and Feng Shu
Drones 2023, 7(4), 224; https://doi.org/10.3390/drones7040224 - 24 Mar 2023
Cited by 1 | Viewed by 1197
Abstract
This paper develops an unmanned aerial vehicle (UAV) deployment scheme in the context of the directional modulation-based secure precise wireless transmissions (SPWTs) to achieve more secure and more energy efficiency transmission, where the optimal UAV position for the SPWT is derived to maximize [...] Read more.
This paper develops an unmanned aerial vehicle (UAV) deployment scheme in the context of the directional modulation-based secure precise wireless transmissions (SPWTs) to achieve more secure and more energy efficiency transmission, where the optimal UAV position for the SPWT is derived to maximize the secrecy rate (SR) without frequency diverse array (FDA) and injecting any artificial noise (AN) signaling. To be specific, the proposed scheme reveals that the optimal position of UAV for maximizing the SR performance has to be placed at the null space of Eves channel, which impels the received energy of the confidential message at the unintended receiver deteriorating to zero, whilst benefits the one at the intended receiver by achieving its maximum value. Moreover, the highly cost FDA structure is eliminated and transmit power is all allocated for transmitting a useful message which shows its energy efficiency. Finally, simulation results verify the optimality of our proposed scheme in terms of the achievable SR performance. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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26 pages, 742 KiB  
Article
Adjustable Fully Adaptive Cross-Entropy Algorithms for Task Assignment of Multi-UAVs
by Kehao Wang, Xun Zhang, Xuyang Qiao, Xiaobai Li, Wei Cheng, Yirui Cong and Kezhong Liu
Drones 2023, 7(3), 204; https://doi.org/10.3390/drones7030204 - 16 Mar 2023
Cited by 3 | Viewed by 1223
Abstract
This paper investigates the multiple unmanned aerial vehicle (multi-UAV) cooperative task assignment problem. Specifically, we assign different types of UAVs to accomplish the classification, attack, and verification tasks of targets under resource, precedence, and timing constraints. Due to complex coupling among these tasks, [...] Read more.
This paper investigates the multiple unmanned aerial vehicle (multi-UAV) cooperative task assignment problem. Specifically, we assign different types of UAVs to accomplish the classification, attack, and verification tasks of targets under resource, precedence, and timing constraints. Due to complex coupling among these tasks, we decompose the considered problem into two subproblems: one with continuous and independent tasks and another with continuous and correlative tasks. To solve them, we first present an adjustable, fully adaptive cross-entropy (AFACE) algorithm based on the cross-entropy (CE) method, which serves as a stepping stone for developing other algorithms. Secondly, to overcome task precedence in the first subproblem, we propose a mutually independent AFACE (MIAFACE) algorithm, which converges faster than the CE method when obtaining the optimal scheme vectors of these continuous and independent tasks. Thirdly, to deal with task coupling in the second subproblem, we present a mutually correlative AFACE (MCAFACE) algorithm to find the optimal scheme vectors of these continuous and correlative tasks, while its computational complexity is inferior to that of the MIAFACE algorithm. Finally, numerical simulations demonstrate that the proposed MIAFACE (MCAFACE, respectively) algorithm consumes less time than the existing algorithms for the continuous and independent (correlative, respectively) task assignment problem. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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22 pages, 1427 KiB  
Article
A Distributed Collaborative Allocation Method of Reconnaissance and Strike Tasks for Heterogeneous UAVs
by Hanqiang Deng, Jian Huang, Quan Liu, Tuo Zhao, Cong Zhou and Jialong Gao
Drones 2023, 7(2), 138; https://doi.org/10.3390/drones7020138 - 15 Feb 2023
Cited by 4 | Viewed by 1616
Abstract
Unmanned aerial vehicles (UAVs) are becoming more and more widely used in battlefield reconnaissance and target strikes because of their high cost-effectiveness, but task planning for large-scale UAV swarms is a problem that needs to be solved. To solve the high-risk problem caused [...] Read more.
Unmanned aerial vehicles (UAVs) are becoming more and more widely used in battlefield reconnaissance and target strikes because of their high cost-effectiveness, but task planning for large-scale UAV swarms is a problem that needs to be solved. To solve the high-risk problem caused by incomplete information for the combat area and the potential coordination between targets when a heterogeneous UAV swarm performs reconnaissance and strike missions, this paper proposes a distributed task-allocation algorithm. The method prioritizes tasks by evaluating the swarm’s capability superiority to tasks to reduce the search space, uses the time coordination mechanism and deterrent maneuver strategy to reduce the risk of reconnaissance missions, and uses the distributed negotiation mechanism to allocate reconnaissance tasks and coordinated strike tasks. The simulation results under the distributed framework verify the effectiveness of the distributed negotiation mechanism, and the comparative experiments under different strategies show that the time coordination mechanism and the deterrent maneuver strategy can effectively reduce the mission risk when the target is unknown. The comparison with the centralized global optimization algorithm verifies the efficiency and effectiveness of the proposed method when applied to large-scale UAV swarms. Since the distributed negotiation task-allocation architecture avoids dependence on the highly reliable network and the central node, it can further improve the reliability and scalability of the swarm, and make it applicable to more complex combat environments. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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23 pages, 632 KiB  
Article
Service Function Chain Scheduling in Heterogeneous Multi-UAV Edge Computing
by Yangang Wang, Hai Wang, Xianglin Wei, Kuang Zhao, Jianhua Fan, Juan Chen, Yongyang Hu and Runa Jia
Drones 2023, 7(2), 132; https://doi.org/10.3390/drones7020132 - 13 Feb 2023
Cited by 1 | Viewed by 1462
Abstract
Supporting Artificial Intelligence (AI)-enhanced intelligent applications on the resource-limited Unmanned Aerial Vehicle (UAV) platform is difficult due to the resource gap between the two. It is promising to partition an AI application into a service function (SF) chain and then dispatch the SFs [...] Read more.
Supporting Artificial Intelligence (AI)-enhanced intelligent applications on the resource-limited Unmanned Aerial Vehicle (UAV) platform is difficult due to the resource gap between the two. It is promising to partition an AI application into a service function (SF) chain and then dispatch the SFs onto multiple UAVs. However, it is still a challenging task to efficiently schedule the computation and communication resources of multiple UAVs to support a large number of SF chains (SFCs). Under the multi-UAV edge computing paradigm, this paper formulates the SFC scheduling problem as a 0–1 nonlinear integer programming problem. Then, a two-stage heuristic algorithm is put forward to solve this problem. At the first stage, if the resources are surplus, the SFCs are deployed to UAV edge servers in parallel based on our proposed pairing principle between SFCs and UAVs for minimizing the completion time sum of tasks. In contrast, a revenue maximization heuristic method is adopted to deploy the arrived SFCs in a serial service mode when the resource is insufficient. A series of experiments are conducted to evaluate the performance of our proposal. Results show that our algorithm outperforms other benchmark algorithms in the completion time sum of tasks, the overall revenue, and the task execution success ratio Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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26 pages, 804 KiB  
Article
Joint Trajectory Planning, Time and Power Allocation to Maximize Throughput in UAV Network
by Kehao Wang, Jiangwei Xu, Xiaobai Li, Pei Liu, Hui Cao and Kezhong Liu
Drones 2023, 7(2), 68; https://doi.org/10.3390/drones7020068 - 18 Jan 2023
Viewed by 1473
Abstract
Due to the advantages of strong mobility, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) are widely used in various industries. As a flying relay, UAVs can establish line-of-sight (LOS) links for different scenarios, effectively improving communication quality. In this paper, considering [...] Read more.
Due to the advantages of strong mobility, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) are widely used in various industries. As a flying relay, UAVs can establish line-of-sight (LOS) links for different scenarios, effectively improving communication quality. In this paper, considering the limited energy budget of UAVs and the existence of multiple jammers, we introduce a simultaneous wireless information and power transfer (SWIPT) technology and study the problems of joint-trajectory planning, time, and power allocation to increase communication performance. Specifically, the network includes multiple UAVs, source nodes (SNs), destination nodes (DNs), and jammers. We assume that the UAVs need to communicate with DNs, the SNs use the SWIPT technology to transmit wireless energy and information to UAVs, and the jammers can interfere with the channel from UAVs to DNs. In this network, our target was to maximize the throughput of DNs by optimizing the UAV’s trajectory, time, and power allocation under the constraints of jammers and the actual motion of UAVs (including UAV energy budget, maximum speed, and anti-collision constraints). Since the formulated problem was non-convex and difficult to solve directly, we first decomposed the original problem into three subproblems. We then solved the subproblems by applying a successive convex optimization technology and a slack variables method. Finally, an efficient joint optimization algorithm was proposed to obtain a sub-optimal solution by using a block coordinate descent method. Simulation results indicated that the proposed algorithm has better performance than the four baseline schemes. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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11 pages, 1435 KiB  
Article
Consensus Control of Large-Scale UAV Swarm Based on Multi-Layer Graph
by Taiqi Wang, Shuaihe Zhao, Yuanqing Xia, Zhenhua Pan and Hanwen Tian
Drones 2022, 6(12), 402; https://doi.org/10.3390/drones6120402 - 07 Dec 2022
Cited by 2 | Viewed by 1746
Abstract
An efficient control of large-scale unmanned aerial vehicle (UAV) swarm to establish a complex formation is one of the most challenging tasks. This paper investigates a novel multi-layer topology network and consensus control approach for a large-scale UAV swarm moving under a stable [...] Read more.
An efficient control of large-scale unmanned aerial vehicle (UAV) swarm to establish a complex formation is one of the most challenging tasks. This paper investigates a novel multi-layer topology network and consensus control approach for a large-scale UAV swarm moving under a stable configuration. The proposed topology can make the swarm remain robust in spite of the large number of UAVs. Then a potential function-based controller is developed to control the UAVs in realizing autonomous configuration swarming under the consideration of mutual collision, and the stability of the controller from the individual UAV to the entire swarm system is analyzed by a Lyapunov approach. Afterwards, a yaw angle adjustment approach for the UAVs to reach consensus is developed for the multi-layer swarm, then the direction state of each UAV converges with a fast rate. Finally, simulations are performed on the large-scale UAV swarm system to demonstrate the effectiveness of the proposed scheme. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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23 pages, 2494 KiB  
Article
Joint Communication and Action Learning in Multi-Target Tracking of UAV Swarms with Deep Reinforcement Learning
by Wenhong Zhou, Jie Li and Qingjie Zhang
Drones 2022, 6(11), 339; https://doi.org/10.3390/drones6110339 - 02 Nov 2022
Cited by 6 | Viewed by 2231
Abstract
Communication is the cornerstone of UAV swarms to transmit information and achieve cooperation. However, artificially designed communication protocols usually rely on prior expert knowledge and lack flexibility and adaptability, which may limit the communication ability between UAVs and is not conducive to swarm [...] Read more.
Communication is the cornerstone of UAV swarms to transmit information and achieve cooperation. However, artificially designed communication protocols usually rely on prior expert knowledge and lack flexibility and adaptability, which may limit the communication ability between UAVs and is not conducive to swarm cooperation. This paper adopts a new data-driven approach to study how reinforcement learning can be utilized to jointly learn the cooperative communication and action policies for UAV swarms. Firstly, the communication policy of a UAV is defined, so that the UAV can autonomously decide the content of the message sent out according to its real-time status. Secondly, neural networks are designed to approximate the communication and action policies of the UAV, and their policy gradient optimization procedures are deduced, respectively. Then, a reinforcement learning algorithm is proposed to jointly learn the communication and action policies of UAV swarms. Numerical simulation results verify that the policies learned by the proposed algorithm are superior to the existing benchmark algorithms in terms of multi-target tracking performance, scalability in different scenarios, and robustness under communication failures. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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