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Drones, Volume 8, Issue 11 (November 2024) – 92 articles

Cover Story (view full-size image): The application of joint sensing and communication (JSAC) technology in air–ground networks offers unique opportunities for improving spectral and energy efficiency. However, these types of networks are also sensitive to the peculiar characteristics of the wireless medium, including shadowing and scattering. This paper investigates an aerial JSAC network and proposes a UAV selection strategy that is shown to improve communication performance. The analytical expressions derived for the received SIR are used to analyze outage and coverage probability (communication part), as well as the ergodic radar estimation information rate and detection probability (sensing part). The results presented reveal the impact of shadowing/fading severity and interference on a system’s performance. View this paper
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22 pages, 1366 KiB  
Article
Mobility-Aware Task Offloading and Resource Allocation in UAV-Assisted Vehicular Edge Computing Networks
by Long Chen, Jiaqi Du and Xia Zhu
Drones 2024, 8(11), 696; https://doi.org/10.3390/drones8110696 - 20 Nov 2024
Viewed by 459
Abstract
The rapid development of the Internet of Vehicles (IoV) and intelligent transportation systems has led to increased demand for real-time data processing and computation in vehicular networks. To address these needs, this paper proposes a task offloading framework for UAV-assisted Vehicular Edge Computing [...] Read more.
The rapid development of the Internet of Vehicles (IoV) and intelligent transportation systems has led to increased demand for real-time data processing and computation in vehicular networks. To address these needs, this paper proposes a task offloading framework for UAV-assisted Vehicular Edge Computing (VEC) systems, which considers the high mobility of vehicles and the limited coverage and computational capacities of drones. We introduce the Mobility-Aware Vehicular Task Offloading (MAVTO) algorithm, designed to optimize task offloading decisions, manage resource allocation, and predict vehicle positions for seamless offloading. MAVTO leverages container-based virtualization for efficient computation, offering flexibility in resource allocation in multiple offload modes: direct, predictive, and hybrid. Extensive experiments using real-world vehicular data demonstrate that the MAVTO algorithm significantly outperforms other methods in terms of task completion success rate, especially under varying task data volumes and deadlines. Full article
(This article belongs to the Special Issue UAV-Assisted Intelligent Vehicular Networks 2nd Edition)
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31 pages, 1890 KiB  
Article
Drone Swarm for Distributed Video Surveillance of Roads and Car Tracking
by David Sánchez Pedroche, Daniel Amigo, Jesús García, José M. Molina and Pablo Zubasti
Drones 2024, 8(11), 695; https://doi.org/10.3390/drones8110695 - 20 Nov 2024
Viewed by 576
Abstract
This study proposes a swarm-based Unmanned Aerial Vehicle (UAV) system designed for surveillance tasks, specifically for detecting and tracking ground vehicles. The proposal is to assess how a system consisting of multiple cooperating UAVs can enhance performance by utilizing fast detection algorithms. Within [...] Read more.
This study proposes a swarm-based Unmanned Aerial Vehicle (UAV) system designed for surveillance tasks, specifically for detecting and tracking ground vehicles. The proposal is to assess how a system consisting of multiple cooperating UAVs can enhance performance by utilizing fast detection algorithms. Within the study, the differences in one-stage and two-stage detection models have been considered, revealing that while two-stage models offer improved accuracy, their increased computation time renders them impractical for real-time applications. Consequently, faster one-stage models, such as the tested YOLOv8 architectures, appear to be a more viable option for real-time operations. Notably, the swarm-based approach enables these faster algorithms to achieve an accuracy level comparable to that of slower models. Overall, the experimentation analysis demonstrates how larger YOLO architectures exhibit longer processing times in exchange for superior tracking success rates. However, the inclusion of additional UAVs introduced in the system outweighed the choice of the tracking algorithm if the mission is correctly configured, thus demonstrating that the swarm-based approach facilitates the use of faster algorithms while maintaining performance levels comparable to slower alternatives. However, the perspectives provided by the included UAVs hold additional significance, as they are essential for achieving enhanced results. Full article
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22 pages, 3514 KiB  
Article
UAV-Mounted RIS-Aided Multi-Target Localization System: An Efficient Sparse-Reconstruction-Based Approach
by Jingjing Li, Jianhui Wang, Weijia Cui and Chunxiao Jian
Drones 2024, 8(11), 694; https://doi.org/10.3390/drones8110694 - 20 Nov 2024
Viewed by 503
Abstract
Unmanned Aerial Vehicle (UAV) technology is increasingly gaining attention in localization systems due to its flexibility and mobility. However, traditional localization techniques often fail in complex environments where line-of-sight paths are obstructed. To address this challenge, this paper presents an innovative UAV-assisted high-precision [...] Read more.
Unmanned Aerial Vehicle (UAV) technology is increasingly gaining attention in localization systems due to its flexibility and mobility. However, traditional localization techniques often fail in complex environments where line-of-sight paths are obstructed. To address this challenge, this paper presents an innovative UAV-assisted high-precision multi-target localization system. The system utilizes UAVs equipped with Reconfigurable Intelligent Surfaces to create a reflective signal path, allowing a receiver sensor to capture these signals, creating favorable conditions for multi-target localization. Exploiting the sparsity of signals, we introduce a direct positioning algorithm that leverages Atomic Norm Minimization (ANM) to estimate the target’s location. To address the high complexity of traditional ANM methods, we propose a novel Coyote-ANM-based direct localization (CADL) approach. This method combines the coyote optimization algorithm with the alternating direction method of multipliers to achieve high-accuracy positioning with reduced computational complexity. Simulation results across various signal-to-noise ratio scenarios demonstrate that the proposed algorithm significantly improves localization accuracy, achieving lower root mean square error values and faster execution times compared to traditional methods. Full article
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21 pages, 4421 KiB  
Article
Joint Optimization Strategy of Task Migration and Power Allocation Based on Soft Actor-Critic in Unmanned Aerial Vehicle-Assisted Internet of Vehicles Environment
by Jingpan Bai, Yifan Zhao, Bozhong Yang, Houling Ji, Botao Liu and Yunhao Chen
Drones 2024, 8(11), 693; https://doi.org/10.3390/drones8110693 - 20 Nov 2024
Viewed by 536
Abstract
In recent years, the unmanned aerial vehicle-assisted internet of vehicles has been extensively studied to enhance communication and computation services in vehicular environments where ground infrastructures are limited or absent. However, due to the limited-service range and battery life of unmanned aerial vehicles, [...] Read more.
In recent years, the unmanned aerial vehicle-assisted internet of vehicles has been extensively studied to enhance communication and computation services in vehicular environments where ground infrastructures are limited or absent. However, due to the limited-service range and battery life of unmanned aerial vehicles, along with the high mobility of vehicles, an unmanned aerial vehicle cannot continuously cover and serve the same vehicle, leading to interruptions in vehicular application services. Therefore, this paper proposes a joint optimization strategy for task migration and power allocation based on soft actor-critic (JOTMAP-SAC). First, communication models, computational resource allocation models, and computation models are established sequentially based on the computational resource and dynamic coordinate of each node. The joint optimization problem of task migration and power allocation is then formulated. Considering the dynamic nature of the unmanned aerial vehicle-assisted internet of vehicles environment and the continuity of the action space, a soft actor-critic based algorithm for task migration and power allocation is designed. This algorithm iteratively finds the optimal solution to the joint optimization problem, thereby reducing the processing delay in unmanned aerial vehicle-assisted internet of vehicles and ensuring the continuity of internet of vehicles task processing. Full article
(This article belongs to the Section Innovative Urban Mobility)
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20 pages, 4297 KiB  
Article
Precision and Efficiency in Dam Crack Inspection: A Lightweight Object Detection Method Based on Joint Distillation for Unmanned Aerial Vehicles (UAVs)
by Hangcheng Dong, Nan Wang, Dongge Fu, Fupeng Wei, Guodong Liu and Bingguo Liu
Drones 2024, 8(11), 692; https://doi.org/10.3390/drones8110692 - 19 Nov 2024
Viewed by 526
Abstract
Dams in their natural environment will gradually develop cracks and other forms of damage. If not detected and repaired in time, the structural strength of the dam may be reduced, and it may even collapse. Repairing cracks and defects in dams is very [...] Read more.
Dams in their natural environment will gradually develop cracks and other forms of damage. If not detected and repaired in time, the structural strength of the dam may be reduced, and it may even collapse. Repairing cracks and defects in dams is very important to ensure their normal operation. Traditional detection methods rely on manual inspection, which consumes a lot of time and labor, while deep learning methods can greatly alleviate this problem. However, previous studies have often focused on how to better detect crack defects, with the corresponding image resolution not being particularly high. In this study, targeting the scenario of real-time detection by drones, we propose an automatic detection method for dam crack targets directly on high-resolution remote sensing images. First, for high-resolution remote sensing images, we designed a sliding window processing method and proposed corresponding methods to eliminate redundant detection frames. Then, we introduced a Gaussian distribution in the loss function to calculate the similarity of predicted frames and incorporated a self-attention mechanism in the spatial pooling module to further enhance the detection performance of crack targets at various scales. Finally, we proposed a pruning-after-distillation scheme, using the compressed model as the student and the pre-compression model as the teacher and proposed a joint distillation method that allows more efficient distillation under this compression relationship between teacher and student models. Ultimately, a high-performance target detection model can be deployed in a more lightweight form for field operations such as UAV patrols. Experimental results show that our method achieves an mAP of 80.4%, with a parameter count of only 0.725 M, providing strong support for future tasks such as UAV field inspections. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)
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16 pages, 11814 KiB  
Article
Recognition of Maize Tassels Based on Improved YOLOv8 and Unmanned Aerial Vehicles RGB Images
by Jiahao Wei, Ruirui Wang, Shi Wei, Xiaoyan Wang and Shicheng Xu
Drones 2024, 8(11), 691; https://doi.org/10.3390/drones8110691 - 19 Nov 2024
Viewed by 645
Abstract
The tasseling stage of maize, as a critical period of maize cultivation, is essential for predicting maize yield and understanding the normal condition of maize growth. However, the branches overlap each other during the growth of maize seedlings and cannot be used as [...] Read more.
The tasseling stage of maize, as a critical period of maize cultivation, is essential for predicting maize yield and understanding the normal condition of maize growth. However, the branches overlap each other during the growth of maize seedlings and cannot be used as an identifying feature. However, during the tasseling stage, its apical ear blooms and has distinctive features that can be used as an identifying feature. However, the sizes of the maize tassels are small, the background is complex, and the existing network has obvious recognition errors. Therefore, in this paper, unmanned aerial vehicle (UAV) RGB images and an improved YOLOv8 target detection network are used to enhance the recognition accuracy of maize tassels. In the new network, a microscale target detection head is added to increase the ability to perceive small-sized maize tassels; In addition, Spatial Pyramid Pooling—Fast (SPPF) is replaced by the Spatial Pyramid Pooling with Efficient Layer Aggregation Network (SPPELAN) in the backbone network part to connect different levels of detailed features and semantic information. Moreover, a dual-attention module synthesized by GAM-CBAM is added to the neck part to reduce the loss of features of maize tassels, thus improving the network’s detection ability. We also labeled the new maize tassels dataset in VOC format as the training and validation of the network model. In the final model testing results, the new network model’s precision reached 93.6% and recall reached 92.5%, which was an improvement of 2.8–12.6 percentage points and 3.6–15.2 percentage points compared to the mAP50 and F1-score values of other models. From the experimental results, it is shown that the improved YOLOv8 network, with high performance and robustness in small-sized maize tassel recognition, can accurately recognize maize tassels in UAV images, which provides technical support for automated counting, accurate cultivation, and large-scale intelligent cultivation of maize seedlings. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
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28 pages, 5305 KiB  
Article
Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility
by Oguz Kagan Isik, Ivan Petrunin and Antonios Tsourdos
Drones 2024, 8(11), 690; https://doi.org/10.3390/drones8110690 - 19 Nov 2024
Viewed by 681
Abstract
The increasing deployment of unmanned aerial vehicles (UAVs) in urban air mobility (UAM) necessitates robust Global Navigation Satellite System (GNSS) integrity monitoring that can adapt to the complexities of urban environments. The traditional integrity monitoring approaches struggle with the unique challenges posed by [...] Read more.
The increasing deployment of unmanned aerial vehicles (UAVs) in urban air mobility (UAM) necessitates robust Global Navigation Satellite System (GNSS) integrity monitoring that can adapt to the complexities of urban environments. The traditional integrity monitoring approaches struggle with the unique challenges posed by urban settings, such as frequent signal blockages, multipath reflections, and Non-Line-of-Sight (NLoS) receptions. This study introduces a novel machine learning-based GNSS integrity monitoring framework that incorporates environment recognition to create environment-specific error models. Using a comprehensive Hardware-in-the-Loop (HIL) simulation setup, extensive data were generated for suburban, urban, and urban canyon environments to train and validate the models. The proposed Natural Gradient Boosting Protection Level (NGB-PL) method, leveraging the uncertainty prediction capabilities of the NGB algorithm, demonstrated superior performance in estimating protection levels compared to the classical methods. The results indicated that environment-specific models significantly enhanced both accuracy and system availability, particularly in challenging urban scenarios. The integration of environment recognition into the integrity monitoring framework allows the dynamic adaptation to varying environmental conditions, thus substantially improving the reliability and safety of UAV operations in urban air mobility applications. This research offers a novel protection level (PL) estimation method and a framework tailored to GNSS integrity monitoring for UAM, which enhances the availability with narrower PL bound gaps without yielding higher integrity risks. Full article
(This article belongs to the Special Issue Recent Advances in UAV Navigation)
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18 pages, 9378 KiB  
Article
Multi-Rotor Drone-Based Thermal Target Tracking with Track Segment Association for Search and Rescue Missions
by Seokwon Yeom
Drones 2024, 8(11), 689; https://doi.org/10.3390/drones8110689 - 19 Nov 2024
Viewed by 651
Abstract
Multi-rotor drones have expanded their range of applications, one of which being search and rescue (SAR) missions using infrared thermal imaging. This paper addresses thermal target tracking with track segment association (TSA) for SAR missions. Three types of associations including TSA are developed [...] Read more.
Multi-rotor drones have expanded their range of applications, one of which being search and rescue (SAR) missions using infrared thermal imaging. This paper addresses thermal target tracking with track segment association (TSA) for SAR missions. Three types of associations including TSA are developed with an interacting multiple model (IMM) approach. During multiple-target tracking, tracks are initialized, maintained, and terminated. There are three different associations in track maintenance: measurement–track association, track–track association for tracks that exist at the same time (track association and fusion), and track–track association for tracks that exist at separate times (TSA). Measurement–track association selects the statistically nearest measurement and updates the track with the measurement through the IMM filter. Track association and fusion fuses redundant tracks for the same target that are spatially separated. TSA connects tracks that have become broken and separated over time. This process is accomplished through the selection of candidate track pairs, backward IMM filtering, association testing, and an assignment rule. In the experiments, a drone was equipped with an infrared thermal imaging camera, and two thermal videos were captured of three people in a non-visible environment. These three hikers were located close together and occluded by each other or other obstacles in the mountains. The drone was allowed to move arbitrarily. The tracking results were evaluated by the average total track life, average mean track life, and average track purity. The track segment association improved the average mean track life of each video by 99.8% and 250%, respectively. Full article
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27 pages, 5038 KiB  
Article
Advancing Social Equity in Urban UAV Logistics: Insights from the Academic Literature and Social Media
by Dong Zhang, Perry Pei-Ju Yang and Jin-Yeu Tsou
Drones 2024, 8(11), 688; https://doi.org/10.3390/drones8110688 - 19 Nov 2024
Viewed by 611
Abstract
In recent years, the rapid growth of e-commerce and on-demand delivery services has placed a significant strain on urban logistics systems. Technological advances such as unmanned aerial vehicle (UAV)-based logistics systems have thus emerged as promising solutions in urban environments and are increasingly [...] Read more.
In recent years, the rapid growth of e-commerce and on-demand delivery services has placed a significant strain on urban logistics systems. Technological advances such as unmanned aerial vehicle (UAV)-based logistics systems have thus emerged as promising solutions in urban environments and are increasingly being piloted worldwide. However, the implementation of UAV logistics risks exacerbating social inequities, particularly in marginalized communities that may disproportionately bear the noise and safety risks. To mitigate these risks, it is crucial to integrate social equity considerations into urban UAV logistics. This study explores social equity factors through a systematic literature review and social media analysis of Xiaohongshu (the Little Red Book), a popular Chinese social media platform known for its extensive user base and active discussions on social issues. This literature review involves a full-text examination, while latent Dirichlet allocation (LDA) topic modeling is used to analyze social media comment datasets. Each method identifies social equity factors and separately assesses their relative importance, resulting in the final identification of 24 key factors that provide a holistic view of public sentiment and academic discourse. The findings reveal a divide between academic concerns around systemic risks and a public focus on immediate needs. By synthesizing these insights, this study provides a social equity landscape for urban UAV logistics and actionable references for policymakers and stakeholders. Full article
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26 pages, 1748 KiB  
Article
Sparse Online Gaussian Process Adaptive Control of Unmanned Aerial Vehicle with Slung Payload
by Muhammed Rasit Kartal, Dmitry I. Ignatyev and Argyrios Zolotas
Drones 2024, 8(11), 687; https://doi.org/10.3390/drones8110687 - 19 Nov 2024
Viewed by 588
Abstract
In the past decade, Unmanned Aerial Vehicles (UAVs) have garnered significant attention across diverse applications, including surveillance, cargo shipping, and agricultural spraying. Despite their widespread deployment, concerns about maintaining stability and safety, particularly when carrying payloads, persist. The development of such UAV platforms [...] Read more.
In the past decade, Unmanned Aerial Vehicles (UAVs) have garnered significant attention across diverse applications, including surveillance, cargo shipping, and agricultural spraying. Despite their widespread deployment, concerns about maintaining stability and safety, particularly when carrying payloads, persist. The development of such UAV platforms necessitates the implementation of robust control mechanisms to ensure stable and precise maneuvering capabilities. Numerous UAV operations require the integration of payloads, which introduces substantial stability challenges. Notably, operations involving unstable payloads such as liquid or slung payloads pose a considerable challenge in this regard, falling into the category of mismatched uncertain systems. This study focuses on establishing stability for slung payload-carrying systems. Our approach involves a combination of various algorithms: the incremental backstepping control algorithm (IBKS), integrator backstepping (IBS), Proportional–Integral–Derivative (PID), and the Sparse Online Gaussian Process (SOGP), a machine learning technique that identifies and mitigates disturbances. With a comparison of linear and nonlinear methodologies through different scenarios, an investigation for an effective solution has been performed. Implementation of the machine learning component, employing SOGP, effectively detects and counteracts disturbances. Insights are discussed within the remit of rejecting liquid sloshing disturbance. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
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30 pages, 929 KiB  
Review
Drones in Precision Agriculture: A Comprehensive Review of Applications, Technologies, and Challenges
by Ridha Guebsi, Sonia Mami and Karem Chokmani
Drones 2024, 8(11), 686; https://doi.org/10.3390/drones8110686 - 19 Nov 2024
Viewed by 5035
Abstract
In the face of growing challenges in modern agriculture, such as climate change, sustainable resource management, and food security, drones are emerging as essential tools for transforming precision agriculture. This systematic review, based on an in-depth analysis of recent scientific literature (2020–2024), provides [...] Read more.
In the face of growing challenges in modern agriculture, such as climate change, sustainable resource management, and food security, drones are emerging as essential tools for transforming precision agriculture. This systematic review, based on an in-depth analysis of recent scientific literature (2020–2024), provides a comprehensive synthesis of current drone applications in the agricultural sector, primarily focusing on studies from this period while including a few notable exceptions of particular interest. Our study examines in detail the technological advancements in drone systems, including innovative aerial platforms, cutting-edge multispectral and hyperspectral sensors, and advanced navigation and communication systems. We analyze diagnostic applications, such as crop monitoring and multispectral mapping, as well as interventional applications like precision spraying and drone-assisted seeding. The integration of artificial intelligence and IoTs in analyzing drone-collected data is highlighted, demonstrating significant improvements in early disease detection, yield estimation, and irrigation management. Specific case studies illustrate the effectiveness of drones in various crops, from viticulture to cereal cultivation. Despite these advancements, we identify several obstacles to widespread drone adoption, including regulatory, technological, and socio-economic challenges. This study particularly emphasizes the need to harmonize regulations on beyond visual line of sight (BVLOS) flights and improve economic accessibility for small-scale farmers. This review also identifies key opportunities for future research, including the use of drone swarms, improved energy autonomy, and the development of more sophisticated decision-support systems integrating drone data. In conclusion, we underscore the transformative potential of drones as a key technology for more sustainable, productive, and resilient agriculture in the face of global challenges in the 21st century, while highlighting the need for an integrated approach combining technological innovation, adapted policies, and farmer training. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
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27 pages, 1679 KiB  
Article
T–S Fuzzy Observer-Based Output Feedback Lateral Control of UGVs Using a Disturbance Observer
by Seunghoon Lee, Sounghwan Hwang and Han Sol Kim
Drones 2024, 8(11), 685; https://doi.org/10.3390/drones8110685 - 19 Nov 2024
Viewed by 537
Abstract
This paper introduces a novel observer-based fuzzy tracking controller that integrates disturbance estimation to improve state estimation and path tracking in the lateral control systems of Unmanned Ground Vehicles (UGVs). The design of the controller is based on linear matrix inequality (LMI) conditions [...] Read more.
This paper introduces a novel observer-based fuzzy tracking controller that integrates disturbance estimation to improve state estimation and path tracking in the lateral control systems of Unmanned Ground Vehicles (UGVs). The design of the controller is based on linear matrix inequality (LMI) conditions derived from a Takagi–Sugeno fuzzy model and a relaxation technique that incorporates additional null terms. The state observer is developed to estimate both the vehicle’s state and external disturbances, such as road curvature. By incorporating the disturbance observer, the proposed approach effectively mitigates performance degradation caused by discrepancies between the system and observer dynamics. The simulation results, conducted in MATLAB and a commercial autonomous driving simulator, demonstrate that the proposed control method substantially enhances state estimation accuracy and improves the robustness of path tracking under varying conditions. Full article
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15 pages, 19251 KiB  
Article
Mapping Stratigraphy and Artifact Distribution with Unmanned Aerial Vehicle-Based Three-Dimensional Models—A Case Study from the Post Research Area in Northwestern Texas, USA
by Stance Hurst, Eileen Johnson and Doug Cunningham
Drones 2024, 8(11), 684; https://doi.org/10.3390/drones8110684 - 19 Nov 2024
Viewed by 463
Abstract
This study applies UAV-based photogrammetry to map and examine the stratigraphy and archaeological artifact distribution in two localities within the Post research area in northwest Texas. A DJI Inspire 1 UAV equipped with a Zenmuse X5 camera captured nadir and oblique images. These [...] Read more.
This study applies UAV-based photogrammetry to map and examine the stratigraphy and archaeological artifact distribution in two localities within the Post research area in northwest Texas. A DJI Inspire 1 UAV equipped with a Zenmuse X5 camera captured nadir and oblique images. These were processed using Agisoft Metashape to generate 3D models. These models enabled the precise mapping of stratigraphic boundaries, revealing the distinctions between Triassic-age bedrock, Pleistocene-age alluvial deposits, and Holocene-age aeolian sediments. Field surveys from 2022 to 2024 documented over 5000 artifacts with sub-centimeter accuracy, including diagnostic projectile points and ceramics. This research highlights the advantages of UAV-derived 3D models in rapidly and accurately documenting stratigraphy and archaeological data. It demonstrates the value of UAV technology for visualizing landscape-scale processes and artifact contexts, offering a new approach to understanding the interactions between geomorphology and archaeology. The findings contribute to advancing UAV applications in both geomorphological and archaeological research. Full article
(This article belongs to the Special Issue Drone-Based Photogrammetric Mapping for Change Detection)
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18 pages, 5301 KiB  
Article
Research and Design of an Active Light Source System for UAVs Based on Light Intensity Matching Model
by Rui Ming, Tao Wu, Zhiyan Zhou, Haibo Luo and Shahbaz Gul Hassan
Drones 2024, 8(11), 683; https://doi.org/10.3390/drones8110683 - 19 Nov 2024
Viewed by 558
Abstract
The saliency feature is a key factor in achieving vision-based tracking for multi-UAV control. However, due to the complex and variable environments encountered during multi-UAV operations—such as changes in lighting conditions and scale variations—the UAV’s visual features may degrade, especially under high-speed movement, [...] Read more.
The saliency feature is a key factor in achieving vision-based tracking for multi-UAV control. However, due to the complex and variable environments encountered during multi-UAV operations—such as changes in lighting conditions and scale variations—the UAV’s visual features may degrade, especially under high-speed movement, ultimately resulting in failure of the vision tracking task and reducing the stability and robustness of swarm flight. Therefore, this paper proposes an adaptive active light source system based on light intensity matching to address the issue of visual feature loss caused by environmental light intensity and scale variations in multi-UAV collaborative navigation. The system consists of three components: an environment sensing and control module, a variable active light source module, and a light source power module. This paper first designs the overall framework of the active light source system, detailing the functions of each module and their collaborative working principles. Furthermore, optimization experiments are conducted on the variable active light source module. By comparing the recognition effects of the variable active light source module under different parameters, the best configuration is selected. In addition, to improve the robustness of the active light source system under different lighting conditions, this paper also constructs a light source color matching model based on light intensity matching. By collecting and comparing visible light images of different color light sources under various intensities and constructing the light intensity matching model using the comprehensive peak signal-to-noise ratio parameter, the model is optimized to ensure the best vision tracking performance under different lighting conditions. Finally, to validate the effectiveness of the proposed active light source system, quantitative and qualitative recognition comparison experiments were conducted in eight different scenarios with UAVs equipped with active light sources. The experimental results show that the UAV equipped with an active light source has improved the recall of yoloV7 and RT-DETR recognition algorithms by 30% and 29.6%, the mAP50 by 21% and 19.5%, and the recognition accuracy by 13.1% and 13.6, respectively. Qualitative experiments also demonstrated that the active light source effectively improved the recognition success rate under low lighting conditions. Extensive qualitative and quantitative experiments confirm that the UAV active light source system based on light intensity matching proposed in this paper effectively enhances the effectiveness and robustness of vision-based tracking for multi-UAVs, particularly in complex and variable environments. This research provides an efficient and computationally effective solution for vision-based multi-UAV systems, further enhancing the visual tracking capabilities of multi-UAVs under complex conditions. Full article
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18 pages, 1403 KiB  
Article
Novel Energy-Aware 3D UAV Path Planning and Collision Avoidance Using Receding Horizon and Optimization-Based Control
by Gamil Ahmed and Tarek Sheltami
Drones 2024, 8(11), 682; https://doi.org/10.3390/drones8110682 - 19 Nov 2024
Viewed by 583
Abstract
Unmanned Aerial Vehicles (UAVs) have gained significant popularity in recent years thanks to their agility, mobility, and cost-effectiveness. However, UAV navigation presents several challenges, particularly in path planning, which requires determining an optimal route while avoiding obstacles and adhering to various constraints. Another [...] Read more.
Unmanned Aerial Vehicles (UAVs) have gained significant popularity in recent years thanks to their agility, mobility, and cost-effectiveness. However, UAV navigation presents several challenges, particularly in path planning, which requires determining an optimal route while avoiding obstacles and adhering to various constraints. Another critical challenge is the limited flight time imposed by the onboard battery. This paper introduces a novel approach for energy-efficient three-dimensional online path planning for UAV formations operating in complex environments. We formulate the path planning problem as a minimization optimization problem, and employ Mixed-Integer Linear Programming (MILP) to achieve optimal solutions. The cost function is designed to minimize energy consumption while considering the inter-collision and intra-collision avoidance constraints within a limited detection range. To achieve this, an optimization approach incorporating Receding Horizon Control (RHC) is applied. The entire path is divided into segments or sub-paths, with constraints used to avoid collisions with obstacles and other members of the fleet. The proposed optimization approach enables fast navigation through dense environments and ensures a collision-free path for all UAVs. A path-smoothing strategy is proposed to further reduce energy consumption caused by sharp turns. The results demonstrate the effectiveness and accuracy of the proposed approach in dense environments with high risk of collision. We compared our proposed approach against recent works, and the results illustrate that the proposed approach outperforms others in terms of UAV formation, number of collisions, and partial path generation time. Full article
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19 pages, 4245 KiB  
Article
Lightweight UAV Small Target Detection and Perception Based on Improved YOLOv8-E
by Yongjuan Zhao, Lijin Wang, Guannan Lei, Chaozhe Guo and Qiang Ma
Drones 2024, 8(11), 681; https://doi.org/10.3390/drones8110681 - 19 Nov 2024
Viewed by 652
Abstract
Traditional unmanned aerial vehicle (UAV) detection methods struggle with multi-scale variations during flight, complex backgrounds, and low accuracy, whereas existing deep learning detection methods have high accuracy but high dependence on equipment, making it difficult to detect small UAV targets efficiently. To address [...] Read more.
Traditional unmanned aerial vehicle (UAV) detection methods struggle with multi-scale variations during flight, complex backgrounds, and low accuracy, whereas existing deep learning detection methods have high accuracy but high dependence on equipment, making it difficult to detect small UAV targets efficiently. To address the above challenges, this paper proposes an improved lightweight high-precision model, YOLOv8-E (Enhanced YOLOv8), for the fast and accurate detection and identification of small UAVs in complex environments. First, a Sobel filter is introduced to enhance the C2f module to form the C2f-ESCFFM (Edge-Sensitive Cross-Stage Feature Fusion Module) module, which achieves higher computational efficiency and feature representation capacity while preserving detection accuracy as much as possible by fusing the SobelConv branch for edge extraction and the convolution branch to extract spatial information. Second, the neck network is based on the HSFPN (High-level Screening-feature Pyramid Network) architecture, and the CAA (Context Anchor Attention) mechanism is introduced to enhance the semantic parsing of low-level features to form a new CAHS-FPN (Context-Augmented Hierarchical Scale Feature Pyramid Network) network, enabling the fusion of deep and shallow features. This improves the feature representation capability of the model, allowing it to detect targets of different sizes efficiently. Finally, the optimized detail-enhanced convolution (DEConv) technique is introduced into the head network, forming the LSCOD (Lightweight Shared Convolutional Object Detector Head) module, enhancing the generalization ability of the model by integrating a priori information and adopting the strategy of shared convolution. This ensures that the model enhances its localization and classification performance without increasing parameters or computational costs, thus effectively improving the detection performance of small UAV targets. The experimental results show that compared with the baseline model, the YOLOv8-E model achieved (mean average precision at IoU = 0.5) an [email protected] improvement of 6.3%, reaching 98.4%, whereas the model parameter scale was reduced by more than 50%. Overall, YOLOv8-E significantly reduces the demand for computational resources while ensuring high-precision detection. Full article
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28 pages, 35225 KiB  
Article
Edge Computing-Driven Real-Time Drone Detection Using YOLOv9 and NVIDIA Jetson Nano
by Raj Hakani and Abhishek Rawat
Drones 2024, 8(11), 680; https://doi.org/10.3390/drones8110680 - 19 Nov 2024
Viewed by 1316
Abstract
Drones, with their ability to vertically take off and land with their stable hovering performance, are becoming favorable in both civilian and military domains. However, this introduces risks of its misuse, which may include security threats to airports, institutes of national importance, VIP [...] Read more.
Drones, with their ability to vertically take off and land with their stable hovering performance, are becoming favorable in both civilian and military domains. However, this introduces risks of its misuse, which may include security threats to airports, institutes of national importance, VIP security, drug trafficking, privacy breaches, etc. To address these issues, automated drone detection systems are essential for preventing unauthorized drone activities. Real-time detection requires high-performance devices such as GPUs. For our experiments, we utilized the NVIDIA Jetson Nano to support YOLOv9-based drone detection. The performance evaluation of YOLOv9 to detect drones is based on metrics like mean average precision (mAP), frames per second (FPS), precision, recall, and F1-score. Experimental data revealed significant improvements over previous models, with a mAP of 95.7%, a precision of 0.946, a recall of 0.864, and an F1-score of 0.903, marking a 4.6% enhancement over YOLOv8. This paper utilizes YOLOv9, optimized with pre-trained weights and transfer learning, achieving significant accuracy in real-time drone detection. Integrated with the NVIDIA Jetson Nano, the system effectively identifies drones at altitudes ranging from 15 feet to 110 feet while adapting to various environmental conditions. The model’s precision and adaptability make it particularly suitable for deployment in security-sensitive areas, where quick and accurate detection is crucial. This research establishes a solid foundation for future counter-drone applications and shows great promise for enhancing situational awareness in critical, high-risk environments. Full article
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26 pages, 3838 KiB  
Article
High-Order Disturbance Observer-Based Fuzzy Fixed-Time Safe Tracking Control for Uncertain Unmanned Helicopter with Partial State Constraints and Multisource Disturbances
by Ruonan Ren, Zhikai Wang, Haoxiang Ma, Baofeng Ji and Fazhan Tao
Drones 2024, 8(11), 679; https://doi.org/10.3390/drones8110679 - 18 Nov 2024
Viewed by 354
Abstract
In the real-world operation of unmanned helicopters, various state constraints, system uncertainties and multisource disturbances pose considerable risks to their safe fight. This paper focuses on anti-disturbance adaptive safety fixed-time control design for the uncertain unmanned helicopter subject to partial state constraints and [...] Read more.
In the real-world operation of unmanned helicopters, various state constraints, system uncertainties and multisource disturbances pose considerable risks to their safe fight. This paper focuses on anti-disturbance adaptive safety fixed-time control design for the uncertain unmanned helicopter subject to partial state constraints and multiple disturbances. Firstly, a developed safety protection algorithm is integrated with the fixed-time stability theory, which assures the tracking performance and guarantees that the partial states are always constrained within the time-varying safe range. Then, the compensation mechanism is developed to weaken the adverse impact induced by the filter errors. Simultaneously, the influence of the multisource disturbances on the system stability are weakened through the Ito^ differential equation and high-order disturbance observer. Further, the fuzzy logic system is constructed to approximate the system uncertainties caused by the sensor measurement errors and complex aerodynamic characteristics. Stability analysis proves that the controlled unmanned helicopter is semi-globally fixed-time stable in probability, and the state errors converge to a desired region of the origin. Finally, simulations are provided to illustrate the performance of the proposed scheme. Full article
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27 pages, 10812 KiB  
Article
Grid Matrix-Based Ground Risk Map Generation for Unmanned Aerial Vehicles in Urban Environments
by Yuanjun Zhu, Xuejun Zhang, Yan Li, Yang Liu and Jianxiang Ma
Drones 2024, 8(11), 678; https://doi.org/10.3390/drones8110678 - 17 Nov 2024
Viewed by 494
Abstract
As a novel mode of urban air mobility (UAM), unmanned aerial vehicles (UAVs) pose a great amount of risk to ground people. Assessing ground risk and mitigation effects correctly is a focused issue. This paper proposes a grid-based risk matrix framework for assessing [...] Read more.
As a novel mode of urban air mobility (UAM), unmanned aerial vehicles (UAVs) pose a great amount of risk to ground people. Assessing ground risk and mitigation effects correctly is a focused issue. This paper proposes a grid-based risk matrix framework for assessing the ground risk associated with two types of UAVs, namely fixed-wing and quadrotor. The framework has a three-stage structure of “intrinsic risk assessment—mitigation effect—final map generation”. First, the intrinsic risk to ground populations caused by potential UAV crashes is quantified. Second, the mitigation effects are measured by establishing a mathematical model with a focus on the ground sheltering and parachute systems. Finally, a modular approach is presented for generating a ground risk map of UAVs, aiming to effectively characterize the effects of each influencing factor on the failure process of UAVs. The framework facilitates the modular analysis and quantification of the impact of diverse risk factors on UAV ground risk. It also provides a new perspective for analyzing ground risk mitigation measures, such as ground sheltering and UAV parachute systems. A case study experiment on a realistic urban environment in Shenzhen shows that the risk map generated by the presented framework can accurately characterize the distribution of ground risk posed by various UAVs. Full article
(This article belongs to the Topic Civil and Public Domain Applications of Unmanned Aviation)
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15 pages, 3820 KiB  
Article
Exploring Ground Reflection Effects on Received Signal Strength Indicator and Path Loss in Far-Field Air-to-Air for Unmanned Aerial Vehicle-Enabled Wireless Communication
by Sarun Duangsuwan and Punyawi Jamjareegulgarn
Drones 2024, 8(11), 677; https://doi.org/10.3390/drones8110677 - 16 Nov 2024
Viewed by 553
Abstract
Unmanned aerial vehicle (UAV)-enabled wireless communications are becoming increasingly important in applications such as maritime and forest rescue operations. UAV systems often depend on wireless networking and mobile edge computing (MEC) devices for effective deployment, particularly in swarm UAV-enabled MEC configurations focusing on [...] Read more.
Unmanned aerial vehicle (UAV)-enabled wireless communications are becoming increasingly important in applications such as maritime and forest rescue operations. UAV systems often depend on wireless networking and mobile edge computing (MEC) devices for effective deployment, particularly in swarm UAV-enabled MEC configurations focusing on channel modeling and path loss characteristics for air-to-air (A2A) communications. This paper examines path loss characteristics in far-field (FF) ground reflection scenarios, specifically comparing two environments: FF1 (forest floor) and FF2 (seawater floor). LoRa modules operating at 868 MHz were deployed for communication between a transmitting UAV (Tx-UAV) and a receiving UAV (Rx-UAV) to conduct this study. We investigated the received signal strength indicator (RSSI) and path loss characteristics across channel bandwidths of 125 kHz and 250 kHz and spread factors (SF) of 7, 9, and 12. Experimental results show that ground reflection has minimal impact in the FF1 scenario, whereas, in the FF2 scenario, ground reflection significantly influences communication. Therefore, in the seawater environment, a UAV-enabled LoRa MEC configuration using a 250 kHz bandwidth and an SF of 7 is recommended to minimize the effects of ground reflection. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Swarm-Enabled Edge Computing)
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21 pages, 11350 KiB  
Article
A Fast Obstacle Detection Algorithm Based on 3D LiDAR and Multiple Depth Cameras for Unmanned Ground Vehicles
by Fenglin Pang, Yutian Chen, Yan Luo, Zigui Lv, Xuefei Sun, Xiaobin Xu and Minzhou Luo
Drones 2024, 8(11), 676; https://doi.org/10.3390/drones8110676 - 15 Nov 2024
Viewed by 576
Abstract
With the advancement of technology, unmanned ground vehicles (UGVs) have shown increasing application value in various tasks, such as food delivery and cleaning. A key capability of UGVs is obstacle detection, which is essential for avoiding collisions during movement. Current mainstream methods use [...] Read more.
With the advancement of technology, unmanned ground vehicles (UGVs) have shown increasing application value in various tasks, such as food delivery and cleaning. A key capability of UGVs is obstacle detection, which is essential for avoiding collisions during movement. Current mainstream methods use point cloud information from onboard sensors, such as light detection and ranging (LiDAR) and depth cameras, for obstacle perception. However, the substantial volume of point clouds generated by these sensors, coupled with the presence of noise, poses significant challenges for efficient obstacle detection. Therefore, this paper presents a fast obstacle detection algorithm designed to ensure the safe operation of UGVs. Building on multi-sensor point cloud fusion, an efficient ground segmentation algorithm based on multi-plane fitting and plane combination is proposed in order to prevent them from being considered as obstacles. Additionally, instead of point cloud clustering, a vertical projection method is used to count the distribution of the potential obstacle points through converting the point cloud to a 2D polar coordinate system. Points in the fan-shaped area with a density lower than a certain threshold will be considered as noise. To verify the effectiveness of the proposed algorithm, a cleaning UGV equipped with one LiDAR sensor and four depth cameras is used to test the performance of obstacle detection in various environments. Several experiments have demonstrated the effectiveness and real-time capability of the proposed algorithm. The experimental results show that the proposed algorithm achieves an over 90% detection rate within a 20 m sensing area and has an average processing time of just 14.1 ms per frame. Full article
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24 pages, 4837 KiB  
Article
Improved Grey Wolf Algorithm: A Method for UAV Path Planning
by Xingyu Zhou, Guoqing Shi and Jiandong Zhang
Drones 2024, 8(11), 675; https://doi.org/10.3390/drones8110675 - 14 Nov 2024
Viewed by 699
Abstract
The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning [...] Read more.
The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning algorithms are often hindered by slow convergence rates, susceptibility to local optima, and limited robustness. To surpass these limitations, we enhance the application of GWO in UAV path planning by improving its trajectory evaluation function, convergence factor, and position update method. We propose a collaborative UAV path planning model that includes constraint analysis and an evaluation function. Subsequently, an Enhanced Grey Wolf Optimizer model (NI–GWO) is introduced, which optimizes the convergence coefficient using a nonlinear function and integrates the Dynamic Window Approach (DWA) algorithm into the model based on the fitness of individual wolves, enabling it to perform dynamic obstacle avoidance tasks. In the final stage, a UAV path planning simulation platform is employed to evaluate and compare the effectiveness of the original and improved algorithms. Simulation results demonstrate that the proposed NI–GWO algorithm can effectively solve the path planning problem for UAVs in uncertain environments. Compared to Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), GWO, and MP–GWO algorithms, the NI–GWO algorithm can achieve the optimal fitness value and has significant advantages in terms of average path length, time, number of collisions, and obstacle avoidance capabilities. Full article
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23 pages, 2756 KiB  
Review
A Review of Drone Technology and Operation Processes in Agricultural Crop Spraying
by Argelia García-Munguía, Paloma Lucía Guerra-Ávila, Efraín Islas-Ojeda, Jorge Luis Flores-Sánchez, Otilio Vázquez-Martínez, Alberto Margarito García-Munguía and Otilio García-Munguía
Drones 2024, 8(11), 674; https://doi.org/10.3390/drones8110674 - 14 Nov 2024
Viewed by 1944
Abstract
Precision agriculture is revolutionizing the management and production of agricultural crops. The development of new technologies in agriculture, such as unmanned aerial vehicles (UAVs), has proven to be an efficient option for spraying various compounds on crops. UAVs significantly contribute to enhancing precision [...] Read more.
Precision agriculture is revolutionizing the management and production of agricultural crops. The development of new technologies in agriculture, such as unmanned aerial vehicles (UAVs), has proven to be an efficient option for spraying various compounds on crops. UAVs significantly contribute to enhancing precision agriculture. This review aims to determine whether integrating advanced precision technologies into drones for crop spraying enhances spraying accuracy compared to drones utilizing standard spraying technologies. To achieve this, 100 articles published between 2019 and 2024 were selected and analyzed. The information was summarized into five main areas: (1) improved spraying with agricultural drone technologies, (2) operational parameters, (3) spraying applications of chemical and natural compounds with agricultural drones, (4) evaluations of control pest efficacy, and (5) considerable limitations. Finally, considerations are presented on the advantages of drone technology with artificial intelligence (AI); the practical effects of reducing pesticides, which, in some cases, have reached a reduction of 30% compared to the recommended dose; and future directions for improving precision agriculture. The use of drones in precision agriculture presents technical and scientific challenges for the maximization of spraying efficiency and the minimization of agrochemical use. Full article
(This article belongs to the Special Issue Recent Advances in Crop Protection Using UAV and UGV)
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23 pages, 2805 KiB  
Article
Autonomous Underwater Vehicle Docking Under Realistic Assumptions Using Deep Reinforcement Learning
by Narcís Palomeras and Pere Ridao
Drones 2024, 8(11), 673; https://doi.org/10.3390/drones8110673 - 13 Nov 2024
Viewed by 1159
Abstract
This paper addresses the challenge of docking an Autonomous Underwater Vehicle (AUV) under realistic conditions. Traditional model-based controllers are often constrained by the complexity and variability of the ocean environment. To overcome these limitations, we propose a Deep Reinforcement Learning (DRL) approach to [...] Read more.
This paper addresses the challenge of docking an Autonomous Underwater Vehicle (AUV) under realistic conditions. Traditional model-based controllers are often constrained by the complexity and variability of the ocean environment. To overcome these limitations, we propose a Deep Reinforcement Learning (DRL) approach to manage the homing and docking maneuver. First, we define the proposed docking task in terms of its observations, actions, and reward function, aiming to bridge the gap between theoretical DRL research and docking algorithms tested on real vehicles. Additionally, we introduce a novel observation space that combines raw noisy observations with filtered data obtained using an Extended Kalman Filter (EKF). We demonstrate the effectiveness of this approach through simulations with various DRL algorithms, showing that the proposed observations can produce stable policies in fewer learning steps, outperforming not only traditional control methods but also policies obtained by the same DRL algorithms in noise-free environments. Full article
(This article belongs to the Special Issue Advances in Autonomous Underwater Drones)
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29 pages, 11493 KiB  
Article
Three-Dimensional Path Following Control for Underactuated AUV Based on Ocean Current Observer
by Long He, Ya Zhang, Shizhong Li, Bo Li and Zeihui Yuan
Drones 2024, 8(11), 672; https://doi.org/10.3390/drones8110672 - 13 Nov 2024
Viewed by 674
Abstract
In the marine environment, the motion characteristics of Autonomous Underwater Vehicles (AUVs) are influenced by unknown factors such as time-varying ocean currents, thereby amplifying the complexity involved in the design of path-following controllers. In this study, a backstepping sliding mode control method based [...] Read more.
In the marine environment, the motion characteristics of Autonomous Underwater Vehicles (AUVs) are influenced by unknown factors such as time-varying ocean currents, thereby amplifying the complexity involved in the design of path-following controllers. In this study, a backstepping sliding mode control method based on a current observer and nonlinear disturbance observer (NDO) has been developed, addressing the 3D path-following issue for AUVs operating in the ocean environment. Accounting for uncertainties like variable ocean currents, this research establishes the AUV’s kinematics and dynamics models and formulates the tracking error within the Frenet–Serret coordinate system. The kinematic controller is designed through the line-of-sight method and the backstepping method, and the dynamic controller is developed using the nonlinear disturbance observer and the integral sliding mode control method. Furthermore, an ocean current observer is developed for the real-time estimation of current velocities, thereby mitigating the effects of ocean currents on navigational performance. Theoretical analysis confirms the system’s asymptotic stability, while numerical simulation attests to the proposed method’s efficacy and robustness in 3D path following. Full article
(This article belongs to the Special Issue Advances in Autonomy of Underwater Vehicles (AUVs))
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25 pages, 43161 KiB  
Article
Mamba-UAV-SegNet: A Multi-Scale Adaptive Feature Fusion Network for Real-Time Semantic Segmentation of UAV Aerial Imagery
by Longyang Huang, Jintao Tan and Zhonghui Chen
Drones 2024, 8(11), 671; https://doi.org/10.3390/drones8110671 - 13 Nov 2024
Viewed by 788
Abstract
Accurate semantic segmentation of high-resolution images captured by unmanned aerial vehicles (UAVs) is crucial for applications in environmental monitoring, urban planning, and precision agriculture. However, challenges such as class imbalance, small-object detection, and intricate boundary details complicate the analysis of UAV imagery. To [...] Read more.
Accurate semantic segmentation of high-resolution images captured by unmanned aerial vehicles (UAVs) is crucial for applications in environmental monitoring, urban planning, and precision agriculture. However, challenges such as class imbalance, small-object detection, and intricate boundary details complicate the analysis of UAV imagery. To address these issues, we propose Mamba-UAV-SegNet, a novel real-time semantic segmentation network specifically designed for UAV images. The network integrates a Multi-Head Mamba Block (MH-Mamba Block) for enhanced multi-scale feature representation, an Adaptive Boundary Enhancement Fusion Module (ABEFM) for improved boundary-aware feature fusion, and an edge-detail auxiliary training branch to capture fine-grained details. The practical utility of our method is demonstrated through its application to farmland segmentation. Extensive experiments on the UAV-City, VDD, and UAVid datasets show that our model outperforms state-of-the-art methods, achieving mean Intersection over Union (mIoU) scores of 71.2%, 77.5%, and 69.3%, respectively. Ablation studies confirm the effectiveness of each component and their combined contributions to overall performance. The proposed method balances segmentation accuracy and computational efficiency, maintaining real-time inference speeds suitable for practical UAV applications. Full article
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16 pages, 5467 KiB  
Article
Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive Segmentation
by Dae-Hyun Lee, Baek-Gyeom Seong, Seung-Yun Baek, Chun-Gu Lee, Yeong-Ho Kang, Xiongzhe Han and Seung-Hwa Yu
Drones 2024, 8(11), 670; https://doi.org/10.3390/drones8110670 - 13 Nov 2024
Viewed by 526
Abstract
Unmanned aerial spraying systems (UASSs) are widely used today for the effective control of pests affecting crops, and more advanced UASS techniques are now being developed. To evaluate such systems, artificial targets are typically used to assess droplet coverage through image processing. To [...] Read more.
Unmanned aerial spraying systems (UASSs) are widely used today for the effective control of pests affecting crops, and more advanced UASS techniques are now being developed. To evaluate such systems, artificial targets are typically used to assess droplet coverage through image processing. To evaluate performance accurately, high-quality binary image processing is necessary; however, this involves labor for sample collection, transportation, and storage, as well as the risk of potential contamination during the process. Therefore, rapid assessment in the field is essential. In the present study, we evaluated droplet coverage on water-sensitive papers (WSPs) under field conditions. A dataset was constructed consisting of paired training examples, each comprising source and target data. The source data were high-quality labeled images obtained from WSP samples through image processing, while the target data were aligned RoIs within field images captured in situ. Droplet coverage estimation was performed using an encoder–decoder model, trained on the labeled images, with features adapted to field images via self-supervised learning. The results indicate that the proposed method detected droplet coverage in field images with an error of less than 5%, demonstrating a strong correlation between measured and estimated values (R2 = 0.99). The method proposed in this paper enables immediate and accurate evaluation of the performance of UASSs in situ. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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22 pages, 38355 KiB  
Article
Novel Design and Computational Fluid Dynamic Analysis of a Foldable Hybrid Aerial Underwater Vehicle
by Guangrong Chen, Lei Yan, Ao Cao, Xinyuan Zhu, Hongbo Ding and Yuxiang Lin
Drones 2024, 8(11), 669; https://doi.org/10.3390/drones8110669 - 12 Nov 2024
Viewed by 688
Abstract
Hybrid Aerial Underwater Vehicles (HAUVs), capable of operating effectively in both aerial and underwater environments, offer promising solutions for a wide range of applications. This paper presents the design and development of a novel foldable wing HAUV, detailing the overall structural framework and [...] Read more.
Hybrid Aerial Underwater Vehicles (HAUVs), capable of operating effectively in both aerial and underwater environments, offer promising solutions for a wide range of applications. This paper presents the design and development of a novel foldable wing HAUV, detailing the overall structural framework and key design considerations. We employed fluid simulation software to perform comprehensive hydrodynamic and aerodynamic analyses, simulating the vehicle’s behavior during aerial flight, underwater navigation, water entry and exit, and surface gliding. The motion characteristics under different speed and angle conditions were analyzed. Additionally, a physical prototype was constructed, and experimental tests were conducted to evaluate its performance in both aerial and underwater environments. The experimental results confirmed the vehicle’s ability to seamlessly transition between air and water, demonstrating its viability for dual-environment operations. Full article
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24 pages, 626 KiB  
Article
Joint Design of Altitude and Channel Statistics Based Energy Beamforming for UAV-Enabled Wireless Energy Transfer
by Jinho Kang
Drones 2024, 8(11), 668; https://doi.org/10.3390/drones8110668 - 11 Nov 2024
Viewed by 654
Abstract
In recent years, UAV-enabled wireless energy transfer (WET) has attracted significant attention for its ability to provide ground devices with efficient and stable power by flexibly navigating three-dimensional (3D) space and utilizing favorable line-of-sight (LoS) channels. At the same time, energy beamforming utilizing [...] Read more.
In recent years, UAV-enabled wireless energy transfer (WET) has attracted significant attention for its ability to provide ground devices with efficient and stable power by flexibly navigating three-dimensional (3D) space and utilizing favorable line-of-sight (LoS) channels. At the same time, energy beamforming utilizing multiple antennas, in which energy beams are focused toward devices in desirable directions, has been highlighted as a key technology for substantially enhancing radio frequency (RF)-based WET efficiency. Despite its significant utility, energy beamforming has not been studied in the context of UAV-enabled WET system design. In this paper, we propose the joint design of UAV altitude and channel statistics based energy beamforming to minimize the overall charging time required for all energy-harvesting devices (EHDs) to meet their energy demands while reducing the additional resources and costs associated with channel estimation. Unlike previous works, in which only the LoS dominant channel without small-scale fading was considered, we adopt a more general air-to-ground (A2G) Rician fading channel, where the LoS probability as well as the Rician factor is dependent on the UAV altitude. To tackle this highly nonconvex and nonlinear design problem, we first examine the scenario of a single EHD, drawing insights by deriving an optimal energy beamforming solution in closed form. We then devise efficient methods for jointly designing altitude and energy beamforming in scenarios with multiple EHDs. Our numerical results demonstrate that the proposed joint design considerably reduces the overall charging time while significantly lowering the computational complexity compared to conventional methods. Full article
(This article belongs to the Special Issue UAV-Assisted Mobile Wireless Networks and Applications)
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16 pages, 2378 KiB  
Article
Adjuvants for Drone-Based Aerial Chemical Applications to Mitigate Off-Target Drift
by Narayanan Kannan, Daniel Martin, Rajani Srinivasan and Weiqiang Zhang
Drones 2024, 8(11), 667; https://doi.org/10.3390/drones8110667 - 11 Nov 2024
Viewed by 837
Abstract
Off-target drift from aerial pesticide applications in croplands can be a major source of pesticide exposure to pollinators. Pesticide adjuvants (PAs) are added to pesticides but can be as toxic as pesticides’ active ingredients. Ongoing experiments have identified sodium alginate (SA) as a [...] Read more.
Off-target drift from aerial pesticide applications in croplands can be a major source of pesticide exposure to pollinators. Pesticide adjuvants (PAs) are added to pesticides but can be as toxic as pesticides’ active ingredients. Ongoing experiments have identified sodium alginate (SA) as a drift-reducing PA less toxic to honeybees. Hence, SA and fenugreek polymer (FP) have been tested as drift-reducing PAs for aerial applications using the Remotely Piloted Aerial Application System (RPAAS). Two spray experiments were carried out in the field: (i) water only (W) and (ii) water and adjuvant (WA). Droplet spectrum and on-target coverage were collected using a VisiSize P15 image analyzer and kromekote cards, respectively. The drift reduction potentials (DRPs) of the adjuvants were analyzed based on droplet size (diameters of 10%, 50%, and 90% volume) and the proportion of driftable volume with droplets < 200 µm. Compared to the W only, the W-A treatment produced larger droplets, suggesting the presence of DRP. There were 14.5%, 8.3% to 14.4%, and 2.3% to 7.7% driftable fines in the W, WA (SA), and WA (FP) treatments, respectively. The FP treatment improved the on-target coverage (3.0% to 3.1%) compared to water (2.7%). Our results indicate that SA and FP have the potential to mitigate off-target drift and protect pollinator health. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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