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Drones, Volume 8, Issue 6 (June 2024) – 56 articles

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25 pages, 1009 KiB  
Article
A Performance Assessment on Rotor Noise-Informed Active Multidrone Sound Source Tracking Methods
by Benjamin Yen, Taiki Yamada, Katsutoshi Itoyama and Kazuhiro Nakadai
Drones 2024, 8(6), 266; https://doi.org/10.3390/drones8060266 - 14 Jun 2024
Viewed by 210
Abstract
This study evaluates and assesses the performance of recent developments in sound source tracking using microphone arrays from multiple drones. Stemming from a baseline study, which triangulates the spatial spectrum calculated from the MUltiple SIgnal Classification (MUSIC) for each drone, otherwise known as [...] Read more.
This study evaluates and assesses the performance of recent developments in sound source tracking using microphone arrays from multiple drones. Stemming from a baseline study, which triangulates the spatial spectrum calculated from the MUltiple SIgnal Classification (MUSIC) for each drone, otherwise known as Particle Filtering with MUSIC (PAFIM), recent studies extended the method by introducing methods to improve the method’s effectiveness. This includes a method to optimise the placement of the drone while tracking the sound source and methods to reduce the influence of high levels of drone rotor noise in the audio recordings. This study evaluates each of the recently proposed methods under a detailed set of simulation settings that are more challenging and realistic than those from previous studies and progressively evaluates each component of the extensions. Results show that applying the rotor noise reduction method and array placement planning algorithm improves tracking accuracy significantly. However, under more realistic input conditions and representations of the problem setting, these methods struggle to achieve decent performance due to factors not considered in their respective studies. As such, based on the performance assessment results, this study summarises a list of recommendations to resolve these shortcomings, with the prospect of further developments or modifications to PAFIM for improved robustness against more realistic settings. Full article
(This article belongs to the Special Issue Technologies and Applications for Drone Audition)
20 pages, 17977 KiB  
Article
Near-and Far-Field Acoustic Characteristics and Sound Source Localization Performance of Low-Noise Propellers with Gapped Gurney Flap
by Ryusuke Noda, Kotaro Hoshiba, Izumi Komatsuzaki, Toshiyuki Nakata and Hao Liu
Drones 2024, 8(6), 265; https://doi.org/10.3390/drones8060265 - 14 Jun 2024
Viewed by 264
Abstract
With the rapid industrialization utilizing multi-rotor drones in recent years, an increase in urban flights is expected in the near future. This may potentially result in noise pollution due to the operation of drones. This study investigates the near- and far-field acoustic characteristics [...] Read more.
With the rapid industrialization utilizing multi-rotor drones in recent years, an increase in urban flights is expected in the near future. This may potentially result in noise pollution due to the operation of drones. This study investigates the near- and far-field acoustic characteristics of low-noise propellers inspired by Gurney flaps. In addition, we examine the impact of these low-noise propellers on the sound source localization performance of drones equipped with a microphone array, which are expected to be used for rescuing people in disasters. Results from in-flight noise measurements indicate significant noise reduction mainly in frequency bands above 1 kHz in both the near- and far-field. An improvement in the success rate of sound source localization with low-noise propellers was also observed. However, the influence of the position of the microphone array with respect to the propellers is more pronounced than that of propeller shape manipulation, suggesting the importance of considering the positional relationships. Computational fluid dynamics analysis of the flow field around the propellers suggests potential mechanisms for noise reduction in the developed low-noise propellers. The results obtained in this study hold potential for contributing to the development of integrated drones aimed at reducing noise and improving sound source localization performance. Full article
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20 pages, 2521 KiB  
Article
Finite-Time Adaptive Quantized Control for Quadrotor Aerial Vehicle with Full States Constraints and Validation on QDrone Experimental Platform
by Xiuyu Zhang, He Li, Guoqiang Zhu, Yanhui Zhang, Chenliang Wang, Yang Wang and Chun-Yi Su
Drones 2024, 8(6), 264; https://doi.org/10.3390/drones8060264 - 14 Jun 2024
Viewed by 190
Abstract
The issue of finite-time stability has garnered significant attention in the control systems of quadrotor aerial vehicles. However, existing techniques for achieving finite-time control often fail to consider the system’s state constraint characteristics and rarely address input quantization issues, thereby limiting their practical [...] Read more.
The issue of finite-time stability has garnered significant attention in the control systems of quadrotor aerial vehicles. However, existing techniques for achieving finite-time control often fail to consider the system’s state constraint characteristics and rarely address input quantization issues, thereby limiting their practical applicability. To address these problems, this paper proposes a finite-time adaptive neural network tracking control scheme based on a novel barrier Lyapunov function for the quadrotor unmanned aerial vehicle (UAV) system. Firstly, an adjustable boundary for the barrier Lyapunov function is introduced in the control system of a quadrotor UAV, enabling convergence of all states within finite-time constraints during trajectory tracking. Subsequently, a filter compensation signal is incorporated into the recursive design process of the controller to mitigate errors caused by filtering. Finally, a smoothing intermediate function is employed to alleviate the impact of input quantization on the quadrotor system. Experimental validation is conducted on the Quanser QDrone experimental platform to demonstrate the efficacy of the proposed control scheme. Full article
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21 pages, 3342 KiB  
Article
Predefined-Time Platoon Control of Unmanned Aerial Vehicle with Range-Limited Communication
by Jiange Wang, Xu Fang and Xiaolei Li
Drones 2024, 8(6), 263; https://doi.org/10.3390/drones8060263 - 13 Jun 2024
Viewed by 319
Abstract
In this paper, the predefined-time platoon control for multiple uncertain unmanned aerial vehicles (UAVs) under range-limited communication and external disturbance constraints is considered. A novel control scheme, which can guarantee communication connectivity, collision avoidance, and the predefined convergence time simultaneously, is proposed. To [...] Read more.
In this paper, the predefined-time platoon control for multiple uncertain unmanned aerial vehicles (UAVs) under range-limited communication and external disturbance constraints is considered. A novel control scheme, which can guarantee communication connectivity, collision avoidance, and the predefined convergence time simultaneously, is proposed. To achieve disturbance robustness, an observer-based distributed control law is firstly proposed with a time-varying gain. Then, a radial basis function neural network (RBFNN) with an adaptive tuning law is applied to approximate uncertainties of the system. Under the time and error transformation techniques, uniformly ultimate boundedness (UUB) stability of the closed-loop system is guaranteed within predefined convergence time. Compared with the existing results, the proposed method allows the system to have UUB within any predefined time without depending on the initial conditions or system parameters. Finally, simulation results are presented to verify the derived theorem. Full article
23 pages, 38781 KiB  
Article
Multi-Objective Deployment of UAVs for Multi-Hop FANET: UAV-Assisted Emergency Vehicular Network
by Haoran Li, Xiaoyao Hao, Juan Wen, Fangyuan Liu and Yiling Zhang
Drones 2024, 8(6), 262; https://doi.org/10.3390/drones8060262 - 13 Jun 2024
Viewed by 329
Abstract
In the event of a sudden natural disaster, the damaged communication infrastructure cannot provide a necessary network service for vehicles. Unfortunately, this is the critical moment when the occupants of trapped vehicles need to urgently use the vehicular network’s emergency service. How to [...] Read more.
In the event of a sudden natural disaster, the damaged communication infrastructure cannot provide a necessary network service for vehicles. Unfortunately, this is the critical moment when the occupants of trapped vehicles need to urgently use the vehicular network’s emergency service. How to efficiently connect the trapped vehicle to the base station is the challenge facing the emergency vehicular network. To address this challenge, this study proposes a UAV-assisted multi-objective and multi-hop ad hoc network (UMMVN) that can be used as an emergency vehicular network. Firstly, it presents an integrated design of a search system to find a trapped vehicle, the communication relay, and the networking, which significantly decreases the UAV’s networking time cost. Secondly, it presents a multi-objective search for a trapped vehicle and navigates UAVs along multiple paths to different objectives. Thirdly, it presents an optimal branching node strategy that allows the adequate use of the overlapping paths to multiple targets, which decreases the networking cost within the limited communication and searching range. The numerical experiments illustrate that the UMMVN performs better than other state-of-the-art networking methods. Full article
(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks 2nd Edition)
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23 pages, 30652 KiB  
Article
EUAVDet: An Efficient and Lightweight Object Detector for UAV Aerial Images with an Edge-Based Computing Platform
by Wanneng Wu, Ao Liu, Jianwen Hu, Yan Mo, Shao Xiang, Puhong Duan and Qiaokang Liang
Drones 2024, 8(6), 261; https://doi.org/10.3390/drones8060261 - 13 Jun 2024
Viewed by 212
Abstract
Crafting an edge-based real-time object detector for unmanned aerial vehicle (UAV) aerial images is challenging because of the limited computational resources and the small size of detected objects. Existing lightweight object detectors often prioritize speed over detecting extremely small targets. To better balance [...] Read more.
Crafting an edge-based real-time object detector for unmanned aerial vehicle (UAV) aerial images is challenging because of the limited computational resources and the small size of detected objects. Existing lightweight object detectors often prioritize speed over detecting extremely small targets. To better balance this trade-off, this paper proposes an efficient and low-complexity object detector for edge computing platforms deployed on UAVs, termed EUAVDet (Edge-based UAV Object Detector). Specifically, an efficient feature downsampling module and a novel multi-kernel aggregation block are first introduced into the backbone network to retain more feature details and capture richer spatial information. Subsequently, an improved feature pyramid network with a faster ghost module is incorporated into the neck network to fuse multi-scale features with fewer parameters. Experimental evaluations on the VisDrone, SeaDronesSeeV2, and UAVDT datasets demonstrate the effectiveness and plug-and-play capability of our proposed modules. Compared with the state-of-the-art YOLOv8 detector, the proposed EUAVDet achieves better performance in nearly all the metrics, including parameters, FLOPs, mAP, and FPS. The smallest version of EUAVDet (EUAVDet-n) contains only 1.34 M parameters and achieves over 20 fps on the Jetson Nano. Our algorithm strikes a better balance between detection accuracy and inference speed, making it suitable for edge-based UAV applications. Full article
(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones)
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19 pages, 1991 KiB  
Article
Distributed Finite-Time ESO-Based Consensus Control for Multiple Fixed-Wing UAVs Subjected to External Disturbances
by Yang Yu, Jianlin Chen, Zixuan Zheng and Jianping Yuan
Drones 2024, 8(6), 260; https://doi.org/10.3390/drones8060260 - 12 Jun 2024
Viewed by 287
Abstract
This paper puts forward a coordinated formation control scheme for multiple fixed-wing unmanned aerial vehicle (UAV) systems with external nonlinear disturbances including not only the drag force and uncertain lateral force, but also the drag, lift, and lateral forces caused by wake vortices. [...] Read more.
This paper puts forward a coordinated formation control scheme for multiple fixed-wing unmanned aerial vehicle (UAV) systems with external nonlinear disturbances including not only the drag force and uncertain lateral force, but also the drag, lift, and lateral forces caused by wake vortices. A novel distributed finite-time extended state observer is designed to estimate both the unmeasurable states and uncertain external nonlinear disturbances of each fixed-wing UAV. In particular, an event-triggered mechanism is employed to reduce the burden of communication networks among multiple fixed-wing UAVs. Meanwhile, an inter-trigger output predictor, replacing the classic zero-order holder, is adopted to obtain cooperative errors between two consecutive triggering moments. Furthermore, a composite distributed controller is proposed to mitigate uncertain disturbances, enabling the coordinated formation flying of multiple fixed-wing UAVs.Finally, two illustrative simulation scenarios are discussed to verify the performance of the presented coordinated formation control scheme. Full article
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25 pages, 14329 KiB  
Article
Advanced Computer Vision Methods for Tracking Wild Birds from Drone Footage
by Dimitris Mpouziotas, Petros Karvelis and Chrysostomos Stylios
Drones 2024, 8(6), 259; https://doi.org/10.3390/drones8060259 - 12 Jun 2024
Viewed by 203
Abstract
Wildlife conservationists have historically depended on manual methods for the identification and tracking of avian species, to monitor population dynamics and discern potential threats. Nonetheless, many of these techniques present inherent challenges and time constraints. With the advancement in computer vision techniques, automated [...] Read more.
Wildlife conservationists have historically depended on manual methods for the identification and tracking of avian species, to monitor population dynamics and discern potential threats. Nonetheless, many of these techniques present inherent challenges and time constraints. With the advancement in computer vision techniques, automated bird detection and recognition have become possible. This study aimed to further advance the task of detecting wild birds using computer vision methods with drone footage, as well as entirely automating the process of detection and tracking. However, detecting objects from drone footage presents a significant challenge, due to the elevated altitudes, as well as the dynamic movement of both the drone and the birds. In this study, we developed and introduce a state-of-the-art model titled ORACLE (optimized rigorous advanced cutting-edge model for leveraging protection to ecosystems). ORACLE aims to facilitate robust communication across multiple models, with the goal of data retrieval, rigorously using various computer vision techniques such as object detection and multi-object tracking (MOT). The results of ORACLE’s vision models were evaluated at 91.89% mAP at 50% IoU. Full article
17 pages, 1074 KiB  
Review
Emerging Research Topics in Drone Healthcare Delivery
by Hamish A. Campbell, Vanya Bosiocic, Aliesha Hvala, Mark Brady, Mariana A. Campbell, Kade Skelton and Osmar J. Luiz
Drones 2024, 8(6), 258; https://doi.org/10.3390/drones8060258 - 12 Jun 2024
Viewed by 430
Abstract
The application of drones to assist with healthcare delivery has grown rapidly over the last decade. This industry is supported by a growing research field, and we have undertaken a systematic review of the published literature. Web-based searches returned 290 relevant manuscripts published [...] Read more.
The application of drones to assist with healthcare delivery has grown rapidly over the last decade. This industry is supported by a growing research field, and we have undertaken a systematic review of the published literature. Web-based searches returned 290 relevant manuscripts published between 2010 and 2024. We applied Topic Modelling to this corpus of literature, which examines word association and connectedness within the research papers. The modelling identified two emerging research themes with little connection between them: those who used drones to deliver time-critical medical items and those who used drones to deliver non-time-critical medical items. The former was in response to medical emergencies, while the latter was for enhancing resilience in the healthcare supply chain. The topics within these research themes exhibited notable differences. The delivery of time-critical medical items theme comprised the topics of ‘Emergency Response’, ‘Defibrillator and Organ Delivery’, and ‘Search and Rescue’, whilst non-time-critical delivery researched the topics of ‘Supply Chain Optimisation’ and ‘Cost-Effectiveness’, ‘Overcoming Remoteness’, and ‘Pandemic Response’. Research on ‘Engineering and Design Considerations’ and ‘Ethical and Social Considerations’ cut across both research themes. We undertook further analysis to assess research topic alignment and identify knowledge gaps. We found that efforts are needed to establish a more standardised terminology for better alignment across the two emerging research themes. Future studies should focus on evaluating the impact of drone delivery on patient health using systematic methods. Additionally, exploring the economic viability of drone-based health services and addressing regulatory barriers are crucial for efficient and effective drone deployment in healthcare delivery systems. Full article
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25 pages, 8137 KiB  
Article
Research on Unmanned Aerial Vehicle (UAV) Visual Landing Guidance and Positioning Algorithms
by Xiaoxiong Liu, Wanhan Xue, Xinlong Xu, Minkun Zhao and Bin Qin
Drones 2024, 8(6), 257; https://doi.org/10.3390/drones8060257 - 12 Jun 2024
Viewed by 282
Abstract
Considering the weak resistance to interference and generalization ability of traditional UAV visual landing navigation algorithms, this paper proposes a deep-learning-based approach for airport runway line detection and fusion of visual information with IMU for localization. Firstly, a coarse positioning algorithm based on [...] Read more.
Considering the weak resistance to interference and generalization ability of traditional UAV visual landing navigation algorithms, this paper proposes a deep-learning-based approach for airport runway line detection and fusion of visual information with IMU for localization. Firstly, a coarse positioning algorithm based on YOLOX is designed for airport runway localization. To meet the requirements of model accuracy and inference speed for the landing guidance system, regression loss functions, probability prediction loss functions, activation functions, and feature extraction networks are designed. Secondly, a deep-learning-based runway line detection algorithm including feature extraction, classification prediction and segmentation networks is designed. To create an effective detection network, we propose efficient loss function and network evaluation methods Finally, a visual/inertial navigation system is established based on constant deformation for visual localization. The relative positioning results are fused and optimized with Kalman filter algorithms. Simulation and flight experiments demonstrate that the proposed algorithm exhibits significant advantages in terms of localization accuracy, real-time performance, and generalization ability, and can provide accurate positioning information during UAV landing processes. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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22 pages, 13426 KiB  
Article
Efficient Motion Primitives-Based Trajectory Planning for UAVs in the Presence of Obstacles
by Marta Manzoni, Roberto Rubinacci and Davide Invernizzi
Drones 2024, 8(6), 256; https://doi.org/10.3390/drones8060256 - 12 Jun 2024
Viewed by 254
Abstract
The achievement of full autonomy in Unmanned Aerial Vehicles (UAVs) is significantly dependent on effective motion planning. Specifically, it is crucial to plan collision-free trajectories for smooth transitions from initial to final configurations. However, finding a solution executable by the actual system adds [...] Read more.
The achievement of full autonomy in Unmanned Aerial Vehicles (UAVs) is significantly dependent on effective motion planning. Specifically, it is crucial to plan collision-free trajectories for smooth transitions from initial to final configurations. However, finding a solution executable by the actual system adds complexity: the planned motion must be dynamically feasible. This involves meeting rigorous criteria, including vehicle dynamics, input constraints, and state constraints. This work addresses optimal kinodynamic motion planning for UAVs in the presence of obstacles by employing a hybrid technique instead of conventional search-based or direct trajectory optimization approaches. This technique involves precomputing a library of motion primitives by solving several Two-Point-Boundary-Value Problems (TPBVP) offline. This library is then repeatedly used online within a graph-search framework. Moreover, to make the method computationally tractable, continuity between consecutive motion primitives is enforced only on a subset of the state variables. This approach is compared with a state-of-the-art quadrotor-tailored search-based approach, which generates motion primitives online through control input discretization and forward propagation of the dynamic equations of a simplified model. The effectiveness of both methods is assessed through simulations and real-world experiments, demonstrating their ability to generate resolution-complete, resolution-optimal, collision-free, and dynamically feasible trajectories. Finally, a comparative analysis highlights the advantages, disadvantages, and optimal usage scenarios for each approach. Full article
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18 pages, 2053 KiB  
Article
Design of a UAV Trajectory Prediction System Based on Multi-Flight Modes
by Zhuoyong Shi, Jiandong Zhang, Guoqing Shi, Longmeng Ji, Dinghan Wang and Yong Wu
Drones 2024, 8(6), 255; https://doi.org/10.3390/drones8060255 - 10 Jun 2024
Viewed by 302
Abstract
With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm [...] Read more.
With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm of UAV flight state recognition and trajectory prediction. Presenting an innovative approach focused on improving the precision of unmanned aerial vehicle (UAV) path forecasting via the identification of flight states, this study demonstrates its efficacy through the implementation of two prediction models. Firstly, UAV flight data acquisition was realized in this paper by the use of multi-sensors. Finally, two models for UAV trajectory prediction were designed based on machine learning methods and classical mathematical prediction methods, respectively, and the results before and after flight pattern recognition are compared. The experimental results show that the prediction error of the UAV trajectory prediction method based on multiple flight modes is smaller than the traditional trajectory prediction method in different flight stages. Full article
28 pages, 8098 KiB  
Article
A Fusion Approach for UAV Onboard Flight Trajectory Management and Decision Making Based on the Combination of Enhanced A* Algorithm and Quadratic Programming
by Shuguang Sun, Haolin Wang, Yanzhi Xu, Tianguang Wang, Ruihua Liu and Wantong Chen
Drones 2024, 8(6), 254; https://doi.org/10.3390/drones8060254 - 8 Jun 2024
Viewed by 230
Abstract
The rapid advancement of unmanned aerial vehicle (UAV) technologies has led to an increasing demand for UAV operations in low-altitude, high-density, and complex airspace such as mountains or urban areas. In order to handle complex scenarios and ensure flight safety for UAVs with [...] Read more.
The rapid advancement of unmanned aerial vehicle (UAV) technologies has led to an increasing demand for UAV operations in low-altitude, high-density, and complex airspace such as mountains or urban areas. In order to handle complex scenarios and ensure flight safety for UAVs with different flight missions beyond visual line of sight in such environments, a fusion framework of onboard autonomous flight trajectory management and decision-making system using global strategical path planning and local tactical trajectory optimization combination is proposed in this paper. The global strategical path planning is implemented by an enhanced A* algorithm under the multi-constraint of UAV positioning uncertainty and obstacle density to improve the safety and cost-effectiveness. The local tactical trajectory optimization is realized using quadratic programming to ensure smoothness, kinematic feasibility, and obstacle avoidance of the planned trajectory in dynamic environments. Receding-horizon control is used to ensure the flight path and trajectory planning efficiently and seamlessly. To assess the performance of the system, a terrain database and a navigation system are employed for environment and navigation performance simulation. The experimental results confirm that the fusion approach can realize better safety and cost-effectiveness through path planning with kino-dynamic feasible trajectory optimization. Full article
29 pages, 3486 KiB  
Article
A Survey on Reputation Systems for UAV Networks
by Simeon Ogunbunmi, Yu Chen, Erik Blasch and Genshe Chen
Drones 2024, 8(6), 253; https://doi.org/10.3390/drones8060253 - 8 Jun 2024
Viewed by 189
Abstract
The proliferation of unmanned aerial vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall [...] Read more.
The proliferation of unmanned aerial vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication, data integrity, and privacy within the highly dynamic and adaptable environments of UAV networks. Decentralized reputation systems have emerged as a promising solution for enhancing the reliability and trustworthiness of data and communications within these networks while safeguarding UAV security. This paper presents an exhaustive survey of trust and reputation systems, exploring existing frameworks and proposed innovations alongside their inherent challenges. The crucial role of reputation systems is to strengthen trust, security, and privacy throughout these networks, and various strategies can be incorporated to mitigate existing vulnerabilities. As a useful resource for researchers and practitioners seeking to advance the state of the art in UAV network security, we hope this survey will spark further community discussion and stimulate innovative ideas in this burgeoning field. Full article
29 pages, 17246 KiB  
Article
Visual Object Tracking Based on the Motion Prediction and Block Search in UAV Videos
by Lifan Sun, Xinxiang Li, Zhe Yang and Dan Gao
Drones 2024, 8(6), 252; https://doi.org/10.3390/drones8060252 - 7 Jun 2024
Viewed by 271
Abstract
With the development of computer vision and Unmanned Aerial Vehicles (UAVs) technology, visual object tracking has become an indispensable core technology for UAVs, and it has been widely used in both civil and military fields. Visual object tracking from the UAV perspective experiences [...] Read more.
With the development of computer vision and Unmanned Aerial Vehicles (UAVs) technology, visual object tracking has become an indispensable core technology for UAVs, and it has been widely used in both civil and military fields. Visual object tracking from the UAV perspective experiences interference from various complex conditions such as background clutter, occlusion, and being out of view, which can easily lead to tracking drift. Once tracking drift occurs, it will lead to almost complete failure of the subsequent tracking. Currently, few trackers have been designed to solve the tracking drift problem. Thus, this paper proposes a tracking algorithm based on motion prediction and block search to address the tracking drift problem caused by various complex conditions. Specifically, when the tracker experiences tracking drift, we first use a Kalman filter to predict the motion state of the target, and then use a block search module to relocate the target. In addition, to improve the tracker’s ability to adapt to changes in the target’s appearance and the environment, we propose a dynamic template updating network (DTUN) that allows the tracker to make appropriate template decisions based on various tracking conditions. We also introduce three tracking evaluation metrics: namely, average peak correlation energy, size change ratio, and tracking score. They serve as prior information for tracking status identification in the DTUN and the block prediction module. Extensive experiments and comparisons with many competitive algorithms on five aerial benchmarks, UAV20L, UAV123, UAVDT, DTB70, and VisDrone2018-SOT, demonstrate that our method achieves significant performance improvements. Especially in UAV20L long-term tracking, our method outperforms the baseline in terms of success rate and accuracy by 19.1% and 20.8%, respectively. This demonstrates the superior performance of our method in the task of long-term tracking from the UAV perspective, and we achieve a real-time speed of 43 FPS. Full article
17 pages, 4092 KiB  
Article
Multi-UAV Formation Path Planning Based on Compensation Look-Ahead Algorithm
by Tianye Sun, Wei Sun, Changhao Sun and Ruofei He
Drones 2024, 8(6), 251; https://doi.org/10.3390/drones8060251 - 7 Jun 2024
Viewed by 348
Abstract
This study primarily studies the shortest-path planning problem for unmanned aerial vehicle (UAV) formations under uncertain target sequences. In order to enhance the efficiency of collaborative search in drone clusters, a compensation look-ahead algorithm based on optimizing the four-point heading angles is proposed. [...] Read more.
This study primarily studies the shortest-path planning problem for unmanned aerial vehicle (UAV) formations under uncertain target sequences. In order to enhance the efficiency of collaborative search in drone clusters, a compensation look-ahead algorithm based on optimizing the four-point heading angles is proposed. Building upon the receding-horizon algorithm, this method introduces the heading angles of adjacent points to approximately compensate and decouple the triangular equations of the optimal trajectory, and a general formula for calculating the heading angles is proposed. The simulation data indicate that the model using the compensatory look forward algorithm exhibits a maximum improvement of 12.9% compared to other algorithms. Furthermore, to solve the computational complexity and sample size requirements for optimal solutions in the Dubins multiple traveling salesman model, a path-planning model for multiple UAV formations is introduced based on the Euclidean traveling salesman problem (ETSP) pre-allocation. By pre-allocating sub-goals, the model reduces the computational scale of individual samples while maintaining a constant sample size. The simulation results show an 8.4% and 17.5% improvement in sparse regions for the proposed Euclidean Dubins traveling salesman problem (EDTSP) model for takeoff from different points. Full article
(This article belongs to the Special Issue Distributed Control, Optimization, and Game of UAV Swarm Systems)
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16 pages, 6441 KiB  
Article
Three-Dimensional Documentation and Reconversion of Architectural Heritage by UAV and HBIM: A Study of Santo Stefano Church in Italy
by Guiye Lin, Guokai Li, Andrea Giordano, Kun Sang, Luigi Stendardo and Xiaochun Yang
Drones 2024, 8(6), 250; https://doi.org/10.3390/drones8060250 - 6 Jun 2024
Viewed by 289
Abstract
Historic buildings hold significant cultural value and their repair and protection require diverse approaches. With the advent of 3D digitalization, drones have gained significance in heritage studies. This research focuses on applying digital methods for restoring architectural heritage. It utilizes non-contact measurement technology, [...] Read more.
Historic buildings hold significant cultural value and their repair and protection require diverse approaches. With the advent of 3D digitalization, drones have gained significance in heritage studies. This research focuses on applying digital methods for restoring architectural heritage. It utilizes non-contact measurement technology, specifically unmanned aerial vehicles (UAVs), for data collection, creating 3D point cloud models using heritage building information modeling (HBIM), and employing virtual reality (VR) for architectural heritage restoration. Employing the “close + surround” oblique photography technique combined with image matching, computer vision, and other technologies, a detailed and comprehensive 3D model of the real scene can be constructed. It provides crucial data support for subsequent protection research and transformation efforts. Using the case of the Santo Stefano Church in Volterra, Italy, an idealized reconstructed 3D model database was established after data collection to preserve essential resources such as the original spatial data and relationships of architectural sites. Through the analysis of relevant historical data and the implementation of VR, the idealized and original appearance of the case was authentically restored. As a result, in the virtual simulation space, the building’s style was realistically displayed with an immersive experience. This approach not only safeguards cultural heritage but also enhances the city’s image and promotes tourism resources, catering to the diverse needs of tourists. Full article
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29 pages, 35171 KiB  
Article
Machine Learning-Based Monitoring for Planning Climate-Resilient Conservation of Built Heritage
by Lidia Fiorini, Alessandro Conti, Eugenio Pellis, Valentina Bonora, Andrea Masiero and Grazia Tucci
Drones 2024, 8(6), 249; https://doi.org/10.3390/drones8060249 - 6 Jun 2024
Viewed by 328
Abstract
The increasing frequency and intensity of extreme weather events are accelerating the mechanisms of surface degradation of heritage buildings, and it is therefore appropriate to find automatic techniques to reduce the time and cost of monitoring and to support their planned conservation. A [...] Read more.
The increasing frequency and intensity of extreme weather events are accelerating the mechanisms of surface degradation of heritage buildings, and it is therefore appropriate to find automatic techniques to reduce the time and cost of monitoring and to support their planned conservation. A fully automated approach is presented here for the segmentation and classification of the architectural elements that make up one of the façades of Palazzo Pitti. The aim of this analysis is to provide tools for a more detailed assessment of the risk of detachment of parts of the pietraforte sandstone elements. Machine learning techniques were applied for the segmentation and classification of information from a DEM obtained via a photogrammetric drone survey. An unsupervised geometry-based classification of the segmented objects was performed using K-means for identifying the most vulnerable elements according to their shapes. The results were validated through comparing them with those obtained via manual segmentation and classification, as well as with studies carried out by experts in the field. The initial results, which can be integrated with non-geometric information, show the usefulness of drone surveys in the context of automatic monitoring of heritage buildings. Full article
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23 pages, 9478 KiB  
Article
Research on Multi-UAV Obstacle Avoidance with Optimal Consensus Control and Improved APF
by Pengfei Zhang, Yin He, Zhongliu Wang, Shujie Li and Qinyang Liang
Drones 2024, 8(6), 248; https://doi.org/10.3390/drones8060248 - 6 Jun 2024
Viewed by 267
Abstract
To address collision challenges between multi-UAVs (unmanned aerial vehicles) during obstacle avoidance, a novel formation control method is proposed. Leveraging the concept of APF (artificial potential field), the proposed approach integrates UAV maneuver constraints with a consensus formation control algorithm, optimizing UAV velocities [...] Read more.
To address collision challenges between multi-UAVs (unmanned aerial vehicles) during obstacle avoidance, a novel formation control method is proposed. Leveraging the concept of APF (artificial potential field), the proposed approach integrates UAV maneuver constraints with a consensus formation control algorithm, optimizing UAV velocities through the particle swarm optimization (PSO) algorithm. The optimal consensus control algorithm is then employed to achieve the optimal convergence rate of the UAV formation. To mitigate the limitations of traditional APF, a collinear force deflection angle is introduced, along with an obstacle avoidance method aimed at preventing UAVs from being trapped in locally optimal solutions. Additionally, an obstacle avoidance algorithm based on virtual force fields between UAVs is designed. Comparative analysis against the basic algorithm demonstrates the effectiveness of the designed optimal consensus algorithm in improving formation convergence performance. Moreover, the improved APF resolves local optimal solution issues, enabling UAVs to effectively navigate around obstacles. Simulation results validate the efficacy of this method in achieving multi-UAV formation control while effectively avoiding obstacles. Full article
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28 pages, 2789 KiB  
Article
Drone-Based Instant Delivery Hub-and-Spoke Network Optimization
by Zhi-Hua Hu, Yan-Ling Huang, Yao-Na Li and Xiao-Qiong Bao
Drones 2024, 8(6), 247; https://doi.org/10.3390/drones8060247 - 5 Jun 2024
Viewed by 392
Abstract
Drone-based transportation is emerging as a novel mode in city logistics, featuring first-mile pickup and last-mile instant delivery using drones and truck transshipment. A fundamental challenge involves coordinating merchants, drones, transshipment hubs, trucks, and consumer communities through the hub-and-spoke network (HSN). This study [...] Read more.
Drone-based transportation is emerging as a novel mode in city logistics, featuring first-mile pickup and last-mile instant delivery using drones and truck transshipment. A fundamental challenge involves coordinating merchants, drones, transshipment hubs, trucks, and consumer communities through the hub-and-spoke network (HSN). This study formulated the optimization problem for HSN to minimize logistics costs and loss of orders constrained by service time limits. The ε-constraint model, two evolutionary algorithms based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) using permutation (EAp) and rand key-based (EAr) encoding/decoding schemes were devised to solve the bi-objective mathematical program. Three groups of twelve experiments were conducted using ideal datasets and datasets generated from Shenzhen city to validate the models and algorithms. Relaxing the logistics objective by 10% and subsequently minimizing the loss of orders can significantly reduce average unmet orders by 24.61%; when spokes were beyond 20, the ε-constraint model failed to achieve solutions within an acceptable time. While EAp and EAr demonstrated competence, EAr proved to be more competitive in computation time, hypervolume, spacing metric, and the number of non-dominated solutions on the Pareto fronts. Key parameters influencing the HSN solutions include drone and truck speeds, acceptable delivery times, and the processing and waiting time at hubs. Full article
(This article belongs to the Special Issue Advances of Drones in Logistics)
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15 pages, 2432 KiB  
Article
Rotor Speed Prediction Model of Multi-Rotor Unmanned Aerial Spraying System and Its Matching with the Overall Load
by Yifang Han, Pengchao Chen, Xiangcheng Xie, Zongyin Cui, Jiapei Wu, Yubin Lan and Yilong Zhan
Drones 2024, 8(6), 246; https://doi.org/10.3390/drones8060246 - 5 Jun 2024
Viewed by 300
Abstract
During continuous spraying operations, the liquid in the pesticide tank gradually decreases, and the flight speed changes as the route is altered. To maintain stable flight, the rotor speed of a multi-rotor unmanned aerial spraying system (UASS) constantly adjusts. To explore the variation [...] Read more.
During continuous spraying operations, the liquid in the pesticide tank gradually decreases, and the flight speed changes as the route is altered. To maintain stable flight, the rotor speed of a multi-rotor unmanned aerial spraying system (UASS) constantly adjusts. To explore the variation law of rotor speed in a multi-rotor UASS under objective operation attributes, based on indoor and outdoor experimental data, this paper constructs a mathematical model of the relationship between rotor speed and thrust. The model fitting parameter (R2) is equal to 0.9996. Through the neural network, the rotor speed prediction model is constructed with the real-time flight speed and the payload of the pesticide tank as the input. The overall correlation coefficient (R2) of the model training set is 0.728, and the correlation coefficients (R2) of the verification set and the test set are 0.719 and 0.726, respectively. Finally, the rotor speed is matched with the load of the whole UASS through thrust conversion. It is known that the single-axis load capacity under full-load state only reaches about 50% of its maximum load capacity, and the load increase is more than 75.83% compared with the no-load state. This study provides a theoretical and methodological reference for accurately predicting the performance characterization results of a power system during actual operation and investigating the dynamic feedback mechanism of a UASS during continuous operation. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
20 pages, 619 KiB  
Article
Intelligent Online Offloading and Resource Allocation for HAP Drones and Satellite Collaborative Networks
by Cheng Gao, Xilin Bian, Bo Hu, Shanzhi Chen and Heng Wang
Drones 2024, 8(6), 245; https://doi.org/10.3390/drones8060245 - 5 Jun 2024
Viewed by 360
Abstract
High-altitude platform (HAP) drones and satellites collaborate to form a network that provides edge computing services to terrestrial internet of things (IoT) devices, which is considered a promising method. In this network, IoT devices’ tasks can be split into multiple parts and processed [...] Read more.
High-altitude platform (HAP) drones and satellites collaborate to form a network that provides edge computing services to terrestrial internet of things (IoT) devices, which is considered a promising method. In this network, IoT devices’ tasks can be split into multiple parts and processed by servers at non-terrestrial nodes in different locations, thereby reducing task processing delays. However, splitting tasks and allocating communication and computing resources are important challenges. In this paper, we investigate the task offloading and resource allocation problem in multi-HAP drones and multi-satellite collaborative networks. In particular, we formulate a task splitting and communication and computing resource optimization problem to minimize the total delay of all IoT devices’ tasks. To solve this problem, we first transform and decompose the original problem into two subproblems. We design a task splitting optimization algorithm based on deep reinforcement learning, which can achieve online task offloading decision-making. This algorithm structurally designs the actor network to ensure that output actions are always valid. Furthermore, we utilize convex optimization methods to optimize the resource allocation subproblem. The simulation results show that our algorithm can effectively converge and significantly reduce the total task processing delay when compared with other baseline algorithms. Full article
(This article belongs to the Special Issue UAV-Assisted Mobile Wireless Networks and Applications)
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27 pages, 4408 KiB  
Article
Automatic Road Pavement Distress Recognition Using Deep Learning Networks from Unmanned Aerial Imagery
by Farhad Samadzadegan, Farzaneh Dadrass Javan, Farnaz Ashtari Mahini, Mehrnaz Gholamshahi and Francesco Nex
Drones 2024, 8(6), 244; https://doi.org/10.3390/drones8060244 - 4 Jun 2024
Viewed by 485
Abstract
Detecting and recognizing distress types on road pavement is crucial for selecting the most appropriate methods to repair, maintain, prevent further damage, and ensure the smooth functioning of daily activities. However, this task presents challenges, such as dealing with crowded backgrounds, the presence [...] Read more.
Detecting and recognizing distress types on road pavement is crucial for selecting the most appropriate methods to repair, maintain, prevent further damage, and ensure the smooth functioning of daily activities. However, this task presents challenges, such as dealing with crowded backgrounds, the presence of multiple distress types in images, and their small sizes. In this study, the YOLOv8 network, a cutting-edge single-stage model, is employed to recognize seven common pavement distress types, including transverse cracks, longitudinal cracks, alligator cracks, oblique cracks, potholes, repairs, and delamination, using a dataset comprising 5796 terrestrial and unmanned aerial images. The network’s optimized architecture and multiple convolutional layers facilitate the extraction of high-level semantic features, enhancing algorithm accuracy, speed, and robustness. By combining high and low semantic features, the network achieves improved accuracy in addressing challenges and distinguishing between different distress types. The implemented Convolutional Neural Network demonstrates a recognition precision of 77%, accuracy of 81%, mAP of 79%, f1-score of 74%, and recall of 75%, underscoring the model’s effectiveness in recognizing various pavement distress forms in both aerial and terrestrial images. These results highlight the model’s satisfactory performance and its potential for effectively recognizing and categorizing pavement distress for efficient infrastructure maintenance and management. Full article
(This article belongs to the Special Issue Applications of UAVs in Civil Infrastructure)
19 pages, 14326 KiB  
Article
A New Method of UAV Swarm Formation Flight Based on AOA Azimuth-Only Passive Positioning
by Zhen Kang, Yihang Deng, Hao Yan, Luhan Yang, Shan Zeng and Bing Li
Drones 2024, 8(6), 243; https://doi.org/10.3390/drones8060243 - 4 Jun 2024
Viewed by 301
Abstract
UAV swarm passive positioning technology only requires the reception of electromagnetic signals to achieve the positioning and tracking of radiation sources. It avoids the active positioning strategy that requires active emission of signals and has the advantages of good concealment, long acting distance, [...] Read more.
UAV swarm passive positioning technology only requires the reception of electromagnetic signals to achieve the positioning and tracking of radiation sources. It avoids the active positioning strategy that requires active emission of signals and has the advantages of good concealment, long acting distance, and strong anti-interference ability, which has received more and more attention. In this paper, we propose a new UAV swarm formation flight method based on pure azimuth passive positioning. Specifically, we propose a two-circle positioning model, which describes the positional deviation of the receiving UAV using trigonometric functions relative to the target in polar coordinates. Furthermore, we design a two-step adjustment strategy that enables the receiving UAV to reach the target position efficiently. Based on the above design, we constructed an optimized UAV swarm formation scheme. In experiments with UAV numbers of 8 and 20, compared to the representative comparison strategy, the proposed UAV formation scheme reduces the total length of the UAV formation by 34.76% and 55.34%, respectively. It demonstrates the effectiveness of the proposed method in the application of assigning target positions to UAV swarms. Full article
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32 pages, 23404 KiB  
Article
Coverage Path Planning with Adaptive Hyperbolic Grid for Step-Stare Imaging System
by Jiaxin Zhao
Drones 2024, 8(6), 242; https://doi.org/10.3390/drones8060242 - 4 Jun 2024
Viewed by 286
Abstract
Step-stare imaging systems are widely used in aerospace optical remote sensing. In order to achieve fast scanning of the target region, efficient coverage path planning (CPP) is a key challenge. However, traditional CPP methods are mostly designed for fixed cameras and disregard the [...] Read more.
Step-stare imaging systems are widely used in aerospace optical remote sensing. In order to achieve fast scanning of the target region, efficient coverage path planning (CPP) is a key challenge. However, traditional CPP methods are mostly designed for fixed cameras and disregard the irregular shape of the sensor’s projection caused by the step-stare rotational motion. To address this problem, this paper proposes an efficient, seamless CPP method with an adaptive hyperbolic grid. First, we convert the coverage problem in Euclidean space to a tiling problem in spherical space. A spherical approximate tiling method based on a zonal isosceles trapezoid is developed to construct a seamless hyperbolic grid. Then, we present a dual-caliper optimization algorithm to further compress the grid and improve the coverage efficiency. Finally, both boustrophedon and branch-and-bound approaches are utilized to generate rotation paths for different scanning scenarios. Experiments were conducted on a custom dataset consisting of 800 diverse geometric regions (including 2 geometry types and 40 samples for 10 groups). The proposed method demonstrates comparable performance of closed-form path length relative to that of a heuristic optimization method while significantly improving real-time capabilities by a minimum factor of 2464. Furthermore, in comparison to traditional rule-based methods, our approach has been shown to reduce the rotational path length by at least 27.29% and 16.71% in circle and convex polygon groups, respectively, indicating a significant improvement in planning efficiency. Full article
(This article belongs to the Section Drone Design and Development)
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18 pages, 20475 KiB  
Article
Insulator Extraction from UAV LiDAR Point Cloud Based on Multi-Type and Multi-Scale Feature Histogram
by Maolin Chen, Jiyang Li, Jianping Pan, Cuicui Ji and Wei Ma
Drones 2024, 8(6), 241; https://doi.org/10.3390/drones8060241 - 4 Jun 2024
Viewed by 260
Abstract
Insulators are key components to ensure the normal operation of power facilities in transmission corridors. Existing insulator identification methods mainly use image data and lack the acquisition of three-dimensional information. This paper proposes an efficient insulator extraction method based on UAV (unmanned aerial [...] Read more.
Insulators are key components to ensure the normal operation of power facilities in transmission corridors. Existing insulator identification methods mainly use image data and lack the acquisition of three-dimensional information. This paper proposes an efficient insulator extraction method based on UAV (unmanned aerial vehicle) LiDAR (light detection and ranging) point cloud, using five histogram features: horizontal density (HD), horizontal void (HV), horizontal width (HW), vertical width (VW) and vertical void (VV). Firstly, a voxel-based method is employed to roughly extract power lines and pylons from the original point cloud. Secondly, the VV histogram is used to categorize the pylons into suspension and tension types, and the HD histogram is used to locate the tower crossarm and further refine the roughly extracted powerlines. Then, for the suspension tower, insulators are segmented based on the HV histogram and HD difference histogram. For the tension tower, the HW histogram is used to recognize the jumper conductor (JC) and transmission conductor (TC) from the power line. The HW histogram and VW histogram are used to extract the tension insulator in the TC and suspension insulator in the JC, respectively. Finally, considering the problem of setting a suitable grid width when constructing the feature histogram, an adaptive method of multi-scale histograms is proposed to refine the extraction result. Two 220 kV long transmission lines are used for the validation, and the overall object-based accuracy for suspension and tension towers are 100% and 97.3%, respectively. Compared with the point feature-based method, the mean F1 score of the proposed method improved by 0.3, and the runtime for each tower is within 2 s. Full article
(This article belongs to the Section Drones in Ecology)
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21 pages, 7702 KiB  
Article
PHSI-RTDETR: A Lightweight Infrared Small Target Detection Algorithm Based on UAV Aerial Photography
by Sen Wang, Huiping Jiang, Zhongjie Li, Jixiang Yang, Xuan Ma, Jiamin Chen and Xingqun Tang
Drones 2024, 8(6), 240; https://doi.org/10.3390/drones8060240 - 3 Jun 2024
Viewed by 279
Abstract
To address the issues of low model accuracy caused by complex ground environments and uneven target scales and high computational complexity in unmanned aerial vehicle (UAV) aerial infrared image target detection, this study proposes a lightweight UAV aerial infrared small target detection algorithm [...] Read more.
To address the issues of low model accuracy caused by complex ground environments and uneven target scales and high computational complexity in unmanned aerial vehicle (UAV) aerial infrared image target detection, this study proposes a lightweight UAV aerial infrared small target detection algorithm called PHSI-RTDETR. Initially, an improved backbone feature extraction network is designed using the lightweight RPConv-Block module proposed in this paper, which effectively captures small target features, significantly reducing the model complexity and computational burden while improving accuracy. Subsequently, the HiLo attention mechanism is combined with an intra-scale feature interaction module to form an AIFI-HiLo module, which is integrated into a hybrid encoder to enhance the focus of the model on dense targets, reducing the rates of missed and false detections. Moreover, the slimneck-SSFF architecture is introduced as the cross-scale feature fusion architecture of the model, utilizing GSConv and VoVGSCSP modules to enhance adaptability to infrared targets of various scales, producing more semantic information while reducing network computations. Finally, the original GIoU loss is replaced with the Inner-GIoU loss, which uses a scaling factor to control auxiliary bounding boxes to speed up convergence and improve detection accuracy for small targets. The experimental results show that, compared to RT-DETR, PHSI-RTDETR reduces model parameters by 30.55% and floating-point operations by 17.10%. Moreover, detection precision and speed are increased by 3.81% and 13.39%, respectively, and mAP50, impressively, reaches 82.58%, demonstrating the great potential of this model for drone infrared small target detection. Full article
(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones 2nd Edition)
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21 pages, 8600 KiB  
Article
Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane
by Xijiang Chen, Qing An, Bufan Zhao, Wuyong Tao, Tieding Lu, Han Zhang, Xianquan Han and Emirhan Ozdemir
Drones 2024, 8(6), 239; https://doi.org/10.3390/drones8060239 - 2 Jun 2024
Viewed by 267
Abstract
The extraction of UAV building point cloud contour points is the basis for the expression of a three-dimensional lightweight building outline. Previous unmanned aerial vehicle (UAV) building point cloud contour extraction methods have mainly focused on the expression of the roof contour, but [...] Read more.
The extraction of UAV building point cloud contour points is the basis for the expression of a three-dimensional lightweight building outline. Previous unmanned aerial vehicle (UAV) building point cloud contour extraction methods have mainly focused on the expression of the roof contour, but did not extract the wall contour. In view of this, an algorithm based on the geometric features of the neighborhood points of the region-growing clustering fusion surface is proposed to extract the boundary points of the UAV building point cloud. Firstly, the region growth plane is fused to obtain a more accurate segmentation plane. Then, the neighboring points are projected onto the neighborhood plane and a vector between the object point and neighborhood point is constructed. Finally, the azimuth of each vector is calculated, and the boundary points of each segmented plane are extracted according to the difference in adjacent azimuths. Experiment results show that the best boundary points can be extracted when the number of adjacent points is 24 and the difference in adjacent azimuths is 120. The proposed method is superior to other methods in the contour extraction of UAV buildings point clouds. Moreover, it can extract not only the building roof contour points, but also the wall contour points, including the window contour points. Full article
(This article belongs to the Special Issue Resilient UAV Autonomy and Remote Sensing)
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31 pages, 1435 KiB  
Article
Analysis of Unmanned Aerial Vehicle-Assisted Cellular Vehicle-to-Everything Communication Using Markovian Game in a Federated Learning Environment
by Xavier Fernando and Abhishek Gupta
Drones 2024, 8(6), 238; https://doi.org/10.3390/drones8060238 - 2 Jun 2024
Viewed by 331
Abstract
The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a [...] Read more.
The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a cluster utilized a strategy-based mechanism to maximize their model completion and transmission probability. We modeled a two-stage zero sum Markovian game with incomplete information to jointly study the utility maximization of the participating vehicles and the UAV in the FL environment. We modeled the aggregating process at the UAV as a mixed strategy game between the UAV and each vehicle. By employing Nash equilibrium, the UAV determined the probability of sufficient updates received from each vehicle. We analyzed and proposed decision-making strategies for several representative interactions involving gross data offloading and federated learning. When multiple vehicles enter a parameter transmission conflict, various strategy combinations are evaluated to decide which vehicles transmit their data to the UAV. The optimal payoff in a transmission window is derived using the Karush–Khun–Tucker (KKT) optimality conditions. We also studied the variation in optimal model parameter transmission probability, average packet delay, UAV transmit power, and the UAV–Vehicle optimal communication probabilities under different conditions. Full article
21 pages, 2906 KiB  
Article
Path-Following Formation of Fixed-Wing UAVs under Communication Delay: A Vector Field Approach
by Thiem V. Pham and Thanh Dong Nguyen
Drones 2024, 8(6), 237; https://doi.org/10.3390/drones8060237 - 2 Jun 2024
Viewed by 180
Abstract
In many applications, such as atmospheric observation or disaster monitoring, cooperative control of a fleet of UAVs is crucial because it is effective in repeated tasks. In this work, we provide a workable and useful cooperative guiding algorithm for several fixed-wing UAVs to [...] Read more.
In many applications, such as atmospheric observation or disaster monitoring, cooperative control of a fleet of UAVs is crucial because it is effective in repeated tasks. In this work, we provide a workable and useful cooperative guiding algorithm for several fixed-wing UAVs to construct a path-following formation with communication delays. The two primary components of our concept are path-following (lateral guidance) and path formation (longitudinal guidance). The former is in charge of ensuring that, in the presence of wind disturbance, the lateral distance between the UAV and its targeted path converges using a well-known vector field technique. In the event of a communication delay, the latter ensures that several fixed-wing UAVs will create a predetermined formation shape. Furthermore, we provide a maximum delay bound that is dependent on the topology and a controller’s gain. Lastly, in order to confirm the viability and advantages of our suggested approach, we construct an effective platform for a hardware-in-the-loop (HIL) test. Full article
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