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Drones, Volume 9, Issue 2 (February 2025) – 69 articles

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23 pages, 4584 KiB  
Article
Foggy Drone Teacher: Domain Adaptive Drone Detection Under Foggy Conditions
by Guida Zheng, Benying Tan, Jingxin Wu, Xiao Qin, Yujie Li and Shuxue Ding
Drones 2025, 9(2), 146; https://doi.org/10.3390/drones9020146 (registering DOI) - 16 Feb 2025
Abstract
With the growing use of drones, efficient detection algorithms are crucial, especially under adverse weather conditions. Most existing drone detection algorithms perform well only in clear weather, resulting in significant performance drops in foggy conditions. This study focuses on improving drone detection in [...] Read more.
With the growing use of drones, efficient detection algorithms are crucial, especially under adverse weather conditions. Most existing drone detection algorithms perform well only in clear weather, resulting in significant performance drops in foggy conditions. This study focuses on improving drone detection in foggy environments using the Mean Teacher framework for domain adaptation. The Mean Teacher framework’s performance relies on the quality of the teacher model’s pseudo-labels. To enhance the quality of the pseudo-labels from the teacher model, we introduce Foggy Drone Teacher (FDT), which includes three key components: (1) Adaptive Style and Context Augmentation to reduce domain shift and improve pseudo-label quality; (2) Simplified Domain Alignment with a novel adversarial strategy to boost domain adaptation; and (3) Progressive Domain Adaptation Training, a two-stage process that helps the teacher model produce more stable and accurate pseudo-labels. In addition, owing to the lack of publicly available data, we created Foggy Drone Dataset (FDD) to support this research. Extensive experiments show that our model achieves a 21.1-point increase in AP0.5 compared to the baseline and outperforms state-of-the-art models. This method significantly improves drone detection accuracy in foggy conditions. Full article
(This article belongs to the Special Issue Detection, Identification and Tracking of UAVs and Drones)
26 pages, 6004 KiB  
Article
Design and Control Strategies of Multirotors with Horizontal Thrust-Vectored Propellers
by Ricardo Rosales Martinez, Hannibal Paul and Kazuhiro Shimonomura
Drones 2025, 9(2), 145; https://doi.org/10.3390/drones9020145 (registering DOI) - 16 Feb 2025
Abstract
With the growing adoption of Unmanned Aerial Vehicles (UAVs) in industrial and commercial sectors, the limitations of traditional under-actuated multirotors are becoming increasingly evident, particularly in manipulation tasks. Limited control over the thrust vector direction of the propellers, coupled with its interdependence on [...] Read more.
With the growing adoption of Unmanned Aerial Vehicles (UAVs) in industrial and commercial sectors, the limitations of traditional under-actuated multirotors are becoming increasingly evident, particularly in manipulation tasks. Limited control over the thrust vector direction of the propellers, coupled with its interdependence on the vehicle’s roll, pitch, and yaw moments, significantly restricts manipulation capabilities. To address these challenges, this work presents a control framework for multirotor UAVs equipped with thrust-vectoring components, enabling enhanced control over the direction of lateral forces. The framework supports various actuator configurations by integrating fixed vertical propellers with horizontally mounted thrust-vectoring components. It is capable of handling horizontal thruster setups that generate forces in all directions along the x- and y-axes. Alternatively, it accommodates constrained configurations where the vehicle is limited to generating force in a single direction along either the x- or y-axis. The supported UAVs can follow transmitter commands, setpoints, or predefined trajectories, while the flight controller autonomously manages the propellers and thrusters to achieve the desired motion. Moment evaluations were conducted to assess the torsional capabilities of the vehicles by varying the angles of the thrusters during torsional tasks. The results demonstrate comparable torsional magnitudes to previously studied thrust-vectoring UAVs. Simulations with vehicles of varying inertia and dimensions showed that, even with large horizontal thruster offsets, the vehicles followed desired trajectories while maintaining stable horizontal orientation and smaller attitude variations compared to normal flight. Similar performance was observed with positive and negative vertical offsets, demonstrating the framework’s tolerance for thrusters outside the horizontal plane. Full article
(This article belongs to the Special Issue Dynamics Modeling and Conceptual Design of UAVs)
22 pages, 7394 KiB  
Article
Research on Super-Twisted Sliding Mode Anti-Disturbance of UAV-Mounted Optoelectronic Platform Based on Predictive Adaptive Law
by Jinzhao Li, Xiantao Li, Lu Wang, Shitao Zhang, Zhigang Zhao and Zongyuan Yang
Drones 2025, 9(2), 144; https://doi.org/10.3390/drones9020144 (registering DOI) - 15 Feb 2025
Abstract
Due to long-term wear and attitude disturbance caused by shafting friction and other factors, the model parameters of the UAV-mounted optoelectronic platform are transformed, and the control accuracy and robustness of the platform are reduced. The traditional approach involves utilizing disturbance observers to [...] Read more.
Due to long-term wear and attitude disturbance caused by shafting friction and other factors, the model parameters of the UAV-mounted optoelectronic platform are transformed, and the control accuracy and robustness of the platform are reduced. The traditional approach involves utilizing disturbance observers to observe disturbance values and subsequently reduce their impact on the system. However, when there is significant uncertainty in the model parameters, the application of this method is constrained. Therefore, a super-twisted sliding mode control based on predictive adaptive law (SSMC + PAL) (SSMPAL) is proposed. Firstly, to adapt to the impact of changes in platform structural parameters on the system and mitigate speed fluctuations, a predictive adaptive law is devised. Subsequently, a super-twisted sliding mode controller(SSMC) was developed, whose high-order performance effectively mitigates the chattering phenomenon associated with traditional sliding mode control strategies and minimizes the impact of observation errors stemming from significant model parameter uncertainties on system control accuracy. The convergence and robustness of the designed control strategy are proven using Lyapunov’s theorem. Finally, the effectiveness of the algorithm is verified using an actual UAV-mounted optoelectronic platform. The step response test results indicate that, compared to the disturbance observer control strategy, this method reduces the overshoot by 7.8% and significantly shortens the response time and transition process, demonstrating its superior dynamic response capability. Subsequent anti-disturbance and robustness tests further highlight the superiority of SSMPAL over disturbance observers in terms of anti-disturbance ability and stability, highlighting its significant engineering application value. Full article
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23 pages, 17089 KiB  
Article
ESO-DETR: An Improved Real-Time Detection Transformer Model for Enhanced Small Object Detection in UAV Imagery
by Yingfan Liu, Miao He and Bin Hui
Drones 2025, 9(2), 143; https://doi.org/10.3390/drones9020143 - 14 Feb 2025
Abstract
Object detection is a fundamental capability that enables drones to perform various tasks. However, achieving a suitable equilibrium between performance, efficiency, and lightweight design continues to be a significant challenge for current algorithms. To address this issue, we propose an enhanced small object [...] Read more.
Object detection is a fundamental capability that enables drones to perform various tasks. However, achieving a suitable equilibrium between performance, efficiency, and lightweight design continues to be a significant challenge for current algorithms. To address this issue, we propose an enhanced small object detection transformer model called ESO-DETR. First, we present a gated single-head attention backbone block, known as the GSHA block, which enhances the extraction of local details. Besides, ESO-DETR utilizes the multiscale multihead self-attention mechanism (MMSA) to efficiently manage complex features within its backbone network. We also introduce a novel and efficient feature fusion pyramid network for enhanced small object detection, termed ESO-FPN. This network integrates large convolutional kernels with dual-domain attention mechanisms. Lastly, we introduce the EMASlideVariFocal loss (ESVF Loss), which dynamically adjusts the weights to improve the model’s focus on more challenging samples. In comparison with the baseline model, ESO-DETR demonstrates enhancements of 3.9% and 4.0% in the mAP50 metric on the VisDrone and HIT-UAV datasets, respectively, while also reducing parameters by 25%. These results highlight the capability of ESO-DETR to improve detection accuracy while maintaining a lightweight and efficient structure. Full article
27 pages, 662 KiB  
Article
GRU-Based Deep Learning Framework for Real-Time, Accurate, and Scalable UAV Trajectory Prediction
by Seungwon Yoon, Dahyun Jang, Hyewon Yoon, Taewon Park and Kyuchul Lee
Drones 2025, 9(2), 142; https://doi.org/10.3390/drones9020142 - 14 Feb 2025
Abstract
Trajectory prediction is critical for ensuring the safety, reliability, and scalability of Unmanned Aerial Vehicle (UAV) in urban environments. Despite advances in deep learning, existing methods often struggle with dynamic UAV conditions, such as rapid directional changes and limited forecasting horizons, while lacking [...] Read more.
Trajectory prediction is critical for ensuring the safety, reliability, and scalability of Unmanned Aerial Vehicle (UAV) in urban environments. Despite advances in deep learning, existing methods often struggle with dynamic UAV conditions, such as rapid directional changes and limited forecasting horizons, while lacking comprehensive real-time validation and generalization capabilities. This study addresses these challenges by proposing a gated recurrent unit (GRU)-based deep learning framework optimized through Look_Back and Forward_Length labeling to capture complex temporal patterns. The model demonstrated state-of-the-art performance, surpassing existing unmanned aerial vehicles (UAV) and aircraft trajectory prediction approaches, including FlightBERT++, in terms of both accuracy and robustness. It achieved reliable long-range predictions up to 4 s, and its real-time feasibility was validated due to its efficient resource utilization. The model’s generalization capability was confirmed through evaluations on two independent UAV datasets, where it consistently predicted unseen trajectories with high accuracy. These findings highlight the model’s ability to handle rapid maneuvers, extend prediction horizons, and generalize across platforms. This work establishes a robust trajectory prediction framework with practical applications in collision avoidance, mission planning, and anti-drone systems, paving the way for safer and more scalable UAV operations. Full article
59 pages, 45108 KiB  
Review
Safety Systems for Emergency Landing of Civilian Unmanned Aerial Vehicles—A Comprehensive Review
by Mohsen Farajijalal, Hossein Eslamiat, Vikrant Avineni, Eric Hettel and Clark Lindsay
Drones 2025, 9(2), 141; https://doi.org/10.3390/drones9020141 - 14 Feb 2025
Abstract
The expanding use of civilian unmanned aerial vehicles (UAVs) has brought forth a crucial need to address the safety risks they pose in the event of failure, especially when flying in populated areas. This paper reviews recent advancements in recovery systems designed for [...] Read more.
The expanding use of civilian unmanned aerial vehicles (UAVs) has brought forth a crucial need to address the safety risks they pose in the event of failure, especially when flying in populated areas. This paper reviews recent advancements in recovery systems designed for the emergency landing of civilian UAVs. It covers a wide range of recovery methods, categorizing them based on different recovery approaches and UAV types, including multirotor and fixed-wing. The study highlights the diversity of recovery strategies, ranging from parachute and airbag systems to software-based methods and hybrid solutions. It emphasizes the importance of considering UAV-specific characteristics and operational environments when selecting appropriate safety systems. Furthermore, by comparing various emergency landing systems, this study reveals that integrating multiple approaches based on the UAV type and mission requirements can achieve broader cover of emergency situations compared to using a single system for a specific scenario. Examples of UAVs that utilize emergency landing systems are also provided. For each recovery system, three key parameters of operating altitude, flight speed and added weight are presented. Researchers and UAV developers can utilize this information to identify a suitable emergency landing method tailored to their mission requirements and available UAVs. Based on the key trends and challenges found in the literature, this review concludes by proposing specific, actionable recommendations. These recommendations are directed towards researchers, UAV developers, and regulatory bodies, and focus on enhancing the safety of civilian UAV operations through the improvement of emergency landing systems. Full article
(This article belongs to the Section Drone Design and Development)
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28 pages, 2083 KiB  
Article
Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks
by Shreyas Devaraju, Shivam Garg, Alexander Ihler, Elizabeth Serena Bentley and Sunil Kumar
Drones 2025, 9(2), 140; https://doi.org/10.3390/drones9020140 - 13 Feb 2025
Abstract
This paper considers a decentralized and autonomous wireless network of low SWaP (size, weight, and power) fixed-wing UAVs (unmanned aerial vehicles) used for remote exploration and monitoring of targets in an inaccessible area lacking communication infrastructure. Here, the UAVs collaborate to find target(s) [...] Read more.
This paper considers a decentralized and autonomous wireless network of low SWaP (size, weight, and power) fixed-wing UAVs (unmanned aerial vehicles) used for remote exploration and monitoring of targets in an inaccessible area lacking communication infrastructure. Here, the UAVs collaborate to find target(s) and use routing protocols to forward the sensed data of target(s) to an aerial base station (BS) in real-time through multihop communication, which can then transmit the data to a control center. However, the unpredictability of target locations and the highly dynamic nature of autonomous, decentralized UAV networks result in frequent route breaks or traffic disruptions. Traditional routing schemes cannot quickly adapt to dynamic UAV networks and can incur large control overhead and delays. In addition, their performance suffers from poor network connectivity in sparse networks with multiple objectives (exploration and monitoring of targets), which results in frequent route unavailability. To address these challenges, we propose two routing schemes: Pipe routing and TC-Pipe routing. Pipe routing is a mobility-, congestion-, and energy-aware scheme that discovers routes to the BS on-demand and proactively switches to alternate high-quality routes within a limited region around the routes (referred to as the “pipe”) when needed. TC-Pipe routing extends this approach by incorporating a decentralized topology control mechanism to help maintain robust connectivity in the pipe region around the routes, resulting in improved route stability and availability. The proposed schemes adopt a novel approach by integrating the topology control with routing protocol and mobility model, and rely only on local information in a distributed manner. Comprehensive evaluations under diverse network and traffic conditions—including UAV density and speed, number of targets, and fault tolerance—show that the proposed schemes improve throughput by reducing flow interruptions and packet drops caused by mobility, congestion, and node failures. At the same time, the impact on coverage performance (measured in terms of coverage and coverage fairness) is minimal, even with multiple targets. Additionally, the performance of both schemes degrades gracefully as the percentage of UAV failures in the network increases. Compared to schemes that use dedicated UAVs as relay nodes to establish a route to the BS when the UAV density is low, Pipe and TC-Pipe routing offer better coverage and connectivity trade-offs, with the TC-Pipe providing the best trade-off. Full article
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18 pages, 1143 KiB  
Article
Lightweight Secure Communication Supporting Batch Authentication for UAV Swarm
by Pengbin Han, Aina Sui and Jiang Wu
Drones 2025, 9(2), 139; https://doi.org/10.3390/drones9020139 - 13 Feb 2025
Abstract
In recent years, with the widespread application of UAV swarm, the security problems faced have been gradually discovered, such as the lack of reliable identity authentication, which makes UAVs vulnerable to invasion. To solve these security problems, a lightweight secure communication scheme supporting [...] Read more.
In recent years, with the widespread application of UAV swarm, the security problems faced have been gradually discovered, such as the lack of reliable identity authentication, which makes UAVs vulnerable to invasion. To solve these security problems, a lightweight secure communication scheme supporting batch authentication for UAV swarm is proposed. Firstly, a layered secure communication model for UAV swarm is designed. Then, a secure transmission protocol is implemented by using elliptic curves under this model, which not only reduces the number of encryptions but also ensures the randomness and one-time use of the session key. Moreover, a UAV identity authentication scheme supporting batch signature verification is proposed, which improves the efficiency of identity authentication. The experiments show that, when the number of UAVs is 60, the computation cost of the proposed scheme is 0.071 s, and the communication cost is 0.203 s, fully demonstrating the efficiency and practicability of the scheme. Through comprehensive security analysis, the capability of the proposed scheme to resist various attacks is demonstrated. Full article
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24 pages, 14282 KiB  
Article
Multi-View, Multi-Target Tracking in Low-Altitude Scenes with UAV Involvement
by Pengnian Wu, Yixuan Li, Zhihao Li, Xuqi Yang and Dong Xue
Drones 2025, 9(2), 138; https://doi.org/10.3390/drones9020138 - 13 Feb 2025
Abstract
Cooperative visual tracking involving unmanned aerial vehicles (UAVs) in low-altitude environments is a dynamic and rapidly evolving domain. Existing models encounter challenges with targets, such as scale variation, appearance similarity, and frequent occlusions, which hinder the effective use of target information for cross-view [...] Read more.
Cooperative visual tracking involving unmanned aerial vehicles (UAVs) in low-altitude environments is a dynamic and rapidly evolving domain. Existing models encounter challenges with targets, such as scale variation, appearance similarity, and frequent occlusions, which hinder the effective use of target information for cross-view identity association. To address these challenges, this study introduces a model for multi-view, multi-target tracking in low-altitude scenes involving UAVs (MVTL-UAV), an effective multi-target tracking model specifically designed for low-altitude scenarios involving UAVs. The proposed method is built upon existing end-to-end detection and tracking frameworks, introducing three innovative modules: loss reinforcement, coupled constraints, and coefficient improvement. Collectively, these advancements enhance the accuracy of cross-view target identity matching. Our method is trained using the DIVOTrack dataset, which comprises data collected from a single UAV and two handheld cameras. Empirical results indicate that our approach achieves a 2.19% improvement in cross-view matching accuracy (CVMA) and a 1.95% improvement in the cross-view ID F1 metric (CVIDF1) when compared to current state-of-the-art methodologies. Importantly, the model’s performance is improved without compromising computational efficiency, thereby enhancing its practical value in resource-constrained environments. As a result, our model demonstrates satisfactory performance in various low-altitude target tracking scenarios involving UAVs, establishing a new benchmark in this research area. Full article
(This article belongs to the Section Drone Design and Development)
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26 pages, 3034 KiB  
Article
Federated Twin Delayed Deep Deterministic Policy Gradient for Delay and Energy Consumption Optimization in Urban Air Mobility with UAV-Assisted MEC
by Chunyu Pan, Zhonghao Luo, Jiuchuan Zhang, Lei Shi, Jirong Yi and Zhaohui Yang
Drones 2025, 9(2), 137; https://doi.org/10.3390/drones9020137 - 12 Feb 2025
Abstract
With the rapid expansion of urban populations and the accelerated pace of urbanization, the concept of urban air mobility (UAM) has emerged. During flights, UAM aircraft need to transmit real-time sensing information to base stations for further processing and analysis. Large-scale real-time data [...] Read more.
With the rapid expansion of urban populations and the accelerated pace of urbanization, the concept of urban air mobility (UAM) has emerged. During flights, UAM aircraft need to transmit real-time sensing information to base stations for further processing and analysis. Large-scale real-time data require leveraging the computing capabilities of edge servers at the network edge to reduce transmission delay and energy consumption of UAM aircraft. In cases where edge servers are unable to process information, an unmanned aerial vehicle (UAV) equipped with computing capabilities and operating in low-altitude airspace can serve as a relay to assist in communication and computation. Due to the limited payloads and flight times of UAVs and UAM aircraft, delay and energy consumption within the system pose significant challenges. To tackle these challenges, two fundamental objectives have been proposed: minimizing delay and minimizing energy consumption. Furthermore, an optimization problem has been proposed to minimize the weighted sum of delay and energy consumption. Then, a UAM federated twin delayed deep deterministic policy gradient (UF-TD3) algorithm has been proposed to solve the original problems characterized by complex, non-convex, and inseparable variables. Simulation results show that the proposed UF-TD3 algorithm converges quickly and significantly outperforms four other baseline algorithms in optimizing delay and energy consumption performance. Moreover, compared to the conventional delay minimization strategy and energy minimization strategy, the proposed strategy of minimizing the weighted sum of delay and energy consumption can reduce the delay by 63.8% and reduce energy by 73.96%. Full article
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24 pages, 1561 KiB  
Article
Connectivity Preservation and Obstacle Avoidance Control for Multiple Quadrotor UAVs with Limited Communication Distance
by Xianghong Xue, Bin Yuan, Yingmin Yi, Lingxia Mu and Youmin Zhang
Drones 2025, 9(2), 136; https://doi.org/10.3390/drones9020136 - 12 Feb 2025
Abstract
This paper studies the distributed formation control problem for multiple unmanned aerial vehicles (UAVs), focusing on preserving connectivity and avoiding obstacles within the constraints of a limited communication distance and in the presence of multiple dynamic obstacles. The UAV network is modeled as [...] Read more.
This paper studies the distributed formation control problem for multiple unmanned aerial vehicles (UAVs), focusing on preserving connectivity and avoiding obstacles within the constraints of a limited communication distance and in the presence of multiple dynamic obstacles. The UAV network is modeled as a proximity graph, where the edges are defined by the distances between the UAVs. A hierarchical control strategy is employed to manage the position and attitude subsystems independently. A distributed position formation controller is developed for the position subsystems, utilizing bounded artificial potential functions to preserve the network connectivity and avoid collisions between UAVs while achieving the desired formation. The position controller also integrates a time-varying sliding manifold and obstacle avoidance potential functions to prevent collisions with dynamic obstacles. Additionally, an attitude controller is designed for the attitude subsystem to track the desired attitude angles generated by the positioning subsystem. Numerical simulations validate that the proposed controllers effectively preserve the communication network’s connectivity, avoid collisions between the UAVs and dynamic obstacles, and achieve the desired formation simultaneously. Full article
(This article belongs to the Special Issue Advances in Quadrotor Unmanned Aerial Vehicles)
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16 pages, 3381 KiB  
Article
Drone LiDAR Occlusion Analysis and Simulation from Retrieved Pathways to Improve Ground Mapping of Forested Environments
by Zhang Miao, Christopher Gomez, Yoshinori Shinohara and Norifumi Hotta
Drones 2025, 9(2), 135; https://doi.org/10.3390/drones9020135 - 12 Feb 2025
Abstract
Drone-mounted LiDAR systems have revolutionized forest mapping, but data quality is often compromised by occlusions caused by vegetation and terrain features. This study presents a novel framework for analyzing and predicting LiDAR occlusion patterns in forested environments, combining the geometric reconstruction of flight [...] Read more.
Drone-mounted LiDAR systems have revolutionized forest mapping, but data quality is often compromised by occlusions caused by vegetation and terrain features. This study presents a novel framework for analyzing and predicting LiDAR occlusion patterns in forested environments, combining the geometric reconstruction of flight paths with the statistical modeling of ground visibility. Using field data collected at Unzen Volcano, Japan, we first developed an algorithm to retrieve drone flight paths from timestamped pointclouds, enabling post-processing optimization, even when original flight data are unavailable. We then created a mathematical model to quantify the shadow effects from obstacles and implemented Monte Carlo simulations to optimize flight parameters for different forest stand characteristics. The results demonstrate that lower-altitude flights (40 m) with narrow scanning angles achieve the highest ground visibility (81%) but require more flight paths, while higher-altitude flights with wider scanning angles offer efficient coverage (47% visibility) with single flight paths. For a forest stand with 250 trees per 25 hectares (heights 5–15 m), statistical analysis showed that scanning angles above 90 degrees consistently delivered 46–47% ground visibility, regardless of the flight height. This research provides quantitative guidance for optimizing drone LiDAR surveys in forested environments, though future work is needed to incorporate canopy complexity and seasonal variations. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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25 pages, 7621 KiB  
Article
UAV-Based Pseudolite Navigation System Architecture Design and the Flight Path Optimization
by Ruocheng Guo, Hong Yuan, Yang Zhang, Xiao Chen and Guanbing Zhang
Drones 2025, 9(2), 134; https://doi.org/10.3390/drones9020134 - 12 Feb 2025
Abstract
In a scenario where GNSS signal is blocked due to interference or occlusion, it is of considerable value to establish a regional navigation system providing emergency services for ground users by using long-endurance and long-range fixed-wing Unmanned Aerial Vehicles (UAVs). The main work [...] Read more.
In a scenario where GNSS signal is blocked due to interference or occlusion, it is of considerable value to establish a regional navigation system providing emergency services for ground users by using long-endurance and long-range fixed-wing Unmanned Aerial Vehicles (UAVs). The main work of this paper consists of two parts. First, we designed a set of UAV-based pseudolite navigation system (UAV-PNS) architecture based on fixed-wing UAVs. Then, considering the flight cost of the UAV swarm, the optimization of the UAV swarm’s flight path aimed at improving regional navigation performance was studied. In this paper, the fitness functions for UAVs’ flight path optimization are proposed, taking into account the navigation and positioning performance, the aircraft utilization rate of UAVs under flight constraints, and the response speed of the system to the emergency mission. Based on this, an acceptance–rejection mutated non-dominated sorting genetic algorithm III (ARMNSGA-III) is proposed for the UAVs’ flight path optimization. The research results show that the flight path strongly guarantees navigation service performance with constraints on the operating cost. The ARMNSGA-III proposed in this paper can provide a 44.01% algorithm timeliness improvement compared to the NSGA-III in the flight path optimization, supporting rapid establishment and continuous service of the UAV-PNS in emergency scenarios. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)
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15 pages, 10497 KiB  
Article
Application of the Fault Injection Method for the Verification of the Behavior of Multiple Unmanned Aircraft Systems Flying in Formation
by Iván Felipe Rodríguez, Ana María Ambrosio, Danny Stevens Traslaviña, Jaime Enrique Orduy and Pedro Fernando Melo
Drones 2025, 9(2), 133; https://doi.org/10.3390/drones9020133 - 12 Feb 2025
Abstract
This research aims to present an analysis of the behavior of multiple Remotely Piloted Aircraft Systems (multi-RPAS) flying in formation, a key aspect of advanced aerial mobility in the aerospace industry. This involves the positioning and relative distance in three dimensions (3D) of [...] Read more.
This research aims to present an analysis of the behavior of multiple Remotely Piloted Aircraft Systems (multi-RPAS) flying in formation, a key aspect of advanced aerial mobility in the aerospace industry. This involves the positioning and relative distance in three dimensions (3D) of two RPAS, taking into account their operational requirements and limitations, recognizing the operating states, and addressing potential situations encountered during formation flight. For this study, the “Conformance and Fault Injection—CoFI” methodology is employed. This methodology guides the user towards a comprehensive understanding of the system and enables the creation of a set of finite state machines representing the system’s behavior under study. Consequently, models and requirements for the behavior of multi-RPAS flying in formation are presented. By applying the CoFI methodology to inject faults into the operation and predict behavior in anomalous situations, both normal and abnormal behavior models, as well as the flight behavior requirements of the multi-RPAS formation, are outlined. This analysis is expected to facilitate the identification of formation flight behavior in multi-RPAS, thereby reducing associated operational risks. Full article
(This article belongs to the Special Issue Flight Control and Collision Avoidance of UAVs)
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22 pages, 6757 KiB  
Article
Co-Registration of Multi-Modal UAS Pushbroom Imaging Spectroscopy and RGB Imagery Using Optical Flow
by Ryan S. Haynes, Arko Lucieer, Darren Turner and Emiliano Cimoli
Drones 2025, 9(2), 132; https://doi.org/10.3390/drones9020132 - 11 Feb 2025
Abstract
Remote sensing from unoccupied aerial systems (UASs) has witnessed exponential growth. The increasing use of imaging spectroscopy sensors and RGB cameras on UAS platforms demands accurate, cross-comparable multi-sensor data. Inherent errors during image capture or processing can introduce spatial offsets, diminishing spatial accuracy [...] Read more.
Remote sensing from unoccupied aerial systems (UASs) has witnessed exponential growth. The increasing use of imaging spectroscopy sensors and RGB cameras on UAS platforms demands accurate, cross-comparable multi-sensor data. Inherent errors during image capture or processing can introduce spatial offsets, diminishing spatial accuracy and hindering cross-comparison and change detection analysis. To address this, we demonstrate the use of an optical flow algorithm, eFOLKI, for co-registering imagery from two pushbroom imaging spectroscopy sensors (VNIR and NIR/SWIR) to an RGB orthomosaic. Our study focuses on two ecologically diverse vegetative sites in Tasmania, Australia. Both sites are structurally complex, posing challenging datasets for co-registration algorithms with initial georectification spatial errors of up to 9 m planimetrically. The optical flow co-registration significantly improved the spatial accuracy of the imaging spectroscopy relative to the RGB orthomosaic. After co-registration, spatial alignment errors were greatly improved, with RMSE and MAE values of less than 13 cm for the higher-spatial-resolution dataset and less than 33 cm for the lower resolution dataset, corresponding to only 2–4 pixels in both cases. These results demonstrate the efficacy of optical flow co-registration in reducing spatial discrepancies between multi-sensor UAS datasets, enhancing accuracy and alignment to enable robust environmental monitoring. Full article
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21 pages, 13154 KiB  
Article
Cover Crop Biomass Predictions with Unmanned Aerial Vehicle Remote Sensing and TensorFlow Machine Learning
by Aakriti Poudel, Dennis Burns, Rejina Adhikari, Dulis Duron, James Hendrix, Thanos Gentimis, Brenda Tubana and Tri Setiyono
Drones 2025, 9(2), 131; https://doi.org/10.3390/drones9020131 - 11 Feb 2025
Abstract
The continuous assessment of cover crop growth throughout the season is a crucial baseline observation for making informed crop management decisions and sustainable farming operation. Precision agriculture techniques involving applications of sensors and unmanned aerial vehicles provide precise and prompt spectral and structural [...] Read more.
The continuous assessment of cover crop growth throughout the season is a crucial baseline observation for making informed crop management decisions and sustainable farming operation. Precision agriculture techniques involving applications of sensors and unmanned aerial vehicles provide precise and prompt spectral and structural data, which allows for effective evaluation of cover crop biomass. Vegetation indices are widely used to quantify crop growth and biomass metrics. The objective of this study was to evaluate the accuracy of biomass estimation using a machine learning approach leveraging spectral and canopy height data acquired from unmanned aerial vehicles (UAVs), comparing different neural network architectures, optimizers, and activation functions. Field trials were carried out at two sites in Louisiana involving winter cover crops. The canopy height was estimated by subtracting the digital surface model taken at the time of peak growth of the cover crop from the data captured during a bare ground condition. When evaluated against the validation dataset, the neural network model facilitated with a Keras TensorFlow library with Adam optimizers and a sigmoid activation function performed the best, predicting cover crop biomass with an average of 96 g m−2 root mean squared error (RMSE). Other statistical metrics including the Pearson correlation and R2 also showed satisfactory conditions with this combination of hyperparameters. The observed cover crop biomass ranged from 290 to 1217 g m−2. The present study findings highlight the merit of comprehensive analysis of cover crop traits using UAV remote sensing and machine learning involving realistic underpinning biophysical mechanisms, as our approach captured both horizontal (vegetation indices) and vertical (canopy height) aspects of plant growth. Full article
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23 pages, 32565 KiB  
Article
Distributed Cognitive Positioning System Based on Nearest Neighbor Association and Multi-Point Filter Initiation for UAVs Using DTMB and INS
by Li Zha, Hai Zhang, Na Wang, Cancan Tao, Kunfeng Lv and Ruirui Zhang
Drones 2025, 9(2), 130; https://doi.org/10.3390/drones9020130 - 11 Feb 2025
Abstract
Location is critical for the safe and effective completion of Unmanned Aerial Vehicle (UAV) missions. Since positioning errors tend to accumulate over time, uncorrected measurements from Inertial Navigation Systems (INSs) are unreliable. Aiming for UAV self-positioning under the challenges of a Global Navigation [...] Read more.
Location is critical for the safe and effective completion of Unmanned Aerial Vehicle (UAV) missions. Since positioning errors tend to accumulate over time, uncorrected measurements from Inertial Navigation Systems (INSs) are unreliable. Aiming for UAV self-positioning under the challenges of a Global Navigation Satellite System (GNSS), this article integrates Digital Terrestrial Multimedia Broadcast (DTMB) signals and assisted INS components as external radiation sources for system design. The trigonometric geometry algorithm is proposed to estimate the pseudo-measurement, and the impact factors of the positioning error are analyzed. After filtering the pseudo-measurement by multi-point initiation, we designed a model for cross-regional positioning scenarios using the nearest-neighbor navigation association and scalar weighted distributed fusion. The simulation results demonstrate that the model can effectively track the target. Finally, the effectiveness of the positioning at a constant altitude is evaluated through different vehicle-mounted scenarios with a speed of 60 km/h. The experimental results show that the minimum positioning error can reach 18.95 m over a 525 m trajectory, thus meeting actual UAV requirements and having practical value. Full article
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17 pages, 2815 KiB  
Article
Unmanned Aerial Vehicle-Based Hyperspectral Imaging and Soil Texture Mapping with Robust AI Algorithms
by Pablo Flores Peña, Mohammad Sadeq Ale Isaac, Daniela Gîfu, Eleftheria Maria Pechlivani and Ahmed Refaat Ragab
Drones 2025, 9(2), 129; https://doi.org/10.3390/drones9020129 - 11 Feb 2025
Abstract
This paper explores the integration of UAV-based hyperspectral imaging and advanced AI algorithms for soil texture mapping and stress detection in agricultural settings. The primary focus lies on leveraging multi-modal sensor data, including hyperspectral imaging, thermal imaging, and gamma-ray spectroscopy, to enable precise [...] Read more.
This paper explores the integration of UAV-based hyperspectral imaging and advanced AI algorithms for soil texture mapping and stress detection in agricultural settings. The primary focus lies on leveraging multi-modal sensor data, including hyperspectral imaging, thermal imaging, and gamma-ray spectroscopy, to enable precise monitoring of abiotic and biotic stressors in crops. An innovative algorithm combining vegetation indices, path planning, and machine learning methods is introduced to enhance the efficiency of data collection and analysis. Experimental results demonstrate significant improvements in accuracy and operational efficiency, paving the way for real-time, data-driven decision-making in precision agriculture. Full article
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52 pages, 13117 KiB  
Review
UAV Path Planning Trends from 2000 to 2024: A Bibliometric Analysis and Visualization
by Qiwu Wu, Yunchen Su, Weicong Tan, Renjun Zhan, Jiaqi Liu and Lingzhi Jiang
Drones 2025, 9(2), 128; https://doi.org/10.3390/drones9020128 - 10 Feb 2025
Abstract
UAV path planning, as a key technology in the field of automatic control and intelligent systems, has demonstrated significant potential in various applications, including logistics and distribution, environmental monitoring, and emergency rescue. A comprehensive reassessment of the existing representative literature reveals that most [...] Read more.
UAV path planning, as a key technology in the field of automatic control and intelligent systems, has demonstrated significant potential in various applications, including logistics and distribution, environmental monitoring, and emergency rescue. A comprehensive reassessment of the existing representative literature reveals that most reviews in this field focus on specific aspects and are largely confined to methodological investigations, primarily qualitative analyses that lack empirical data to support their conclusions. To address this gap, this study employs the mapping knowledge domain (MKD) method of bibliometrics, utilizing CiteSpace, VOSviewer, and Bibliometrix R package to analyze a total of 4416 documents from the Web of Science Core Collection (WOSCC) spanning from 2000 to 2024. Through retrospective analysis and scientific knowledge mapping, we first review the development of UAV path planning and categorize it into four distinct stages. Secondly, we identify key external features of the field. Using techniques such as co-citation analysis and keyword clustering, we then identify research trends, burst papers, and hotspots. Finally, we highlight five typical application scenarios of UAV path planning. The results of the study indicate that the field of UAV path planning has made significant advancements over the past two decades, particularly since 2018. These studies encompass various disciplinary areas, underscoring the increasing necessity for the integration of multidisciplinary approaches to UAV path planning in recent years. The aim of this study is to provide researchers with a comprehensive reference and new research perspectives while offering technical guidelines for professionals working in related applications. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)
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22 pages, 1322 KiB  
Article
A Consensus-Driven Distributed Moving Horizon Estimation Approach for Target Detection Within Unmanned Aerial Vehicle Formations in Rescue Operations
by Salvatore Rosario Bassolillo, Egidio D’Amato and Immacolata Notaro
Drones 2025, 9(2), 127; https://doi.org/10.3390/drones9020127 - 9 Feb 2025
Abstract
In the last decades, the increasing employment of unmanned aerial vehicles (UAVs) in civil applications has highlighted the potential of coordinated multi-aircraft missions. Such an approach offers advantages in terms of cost-effectiveness, operational flexibility, and mission success rates, particularly in complex scenarios such [...] Read more.
In the last decades, the increasing employment of unmanned aerial vehicles (UAVs) in civil applications has highlighted the potential of coordinated multi-aircraft missions. Such an approach offers advantages in terms of cost-effectiveness, operational flexibility, and mission success rates, particularly in complex scenarios such as search and rescue operations, environmental monitoring, and surveillance. However, achieving global situational awareness, although essential, represents a significant challenge, due to computational and communication constraints. This paper proposes a Distributed Moving Horizon Estimation (DMHE) technique that integrates consensus theory and Moving Horizon Estimation to optimize computational efficiency, minimize communication requirements, and enhance system robustness. The proposed DMHE framework is applied to a formation of UAVs performing target detection and tracking in challenging environments. It provides a fully distributed architecture that enables UAVs to estimate the position and velocity of other fleet members while simultaneously detecting static and dynamic targets. The effectiveness of the technique is proved by several numerical simulation, including an in-depth sensitivity analysis of key algorithm parameters, such as fleet network topology and consensus iterations and the evaluation of the robustness against node faults and information losses. Full article
(This article belongs to the Special Issue Resilient Networking and Task Allocation for Drone Swarms)
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28 pages, 10105 KiB  
Article
Research on Risk Avoidance Path Planning for Unmanned Vehicle Based on Genetic Algorithm and Bezier Curve
by Gaoyang Xie, Liqing Fang, Xujun Su, Deqing Guo, Ziyuan Qi, Yanan Li and Jinli Che
Drones 2025, 9(2), 126; https://doi.org/10.3390/drones9020126 - 9 Feb 2025
Abstract
In the process of autonomous driving, the identification and avoidance of risk points is of great significance for the safe and efficient navigation of unmanned vehicles. To solve this problem, a new strategy combining a Bezier curve and the genetic algorithm is proposed [...] Read more.
In the process of autonomous driving, the identification and avoidance of risk points is of great significance for the safe and efficient navigation of unmanned vehicles. To solve this problem, a new strategy combining a Bezier curve and the genetic algorithm is proposed in this paper. Firstly, in order to make the curvature of the path continuous, the design uses two symmetric Bezier curves as the path curves. Then, in order to describe the influence range of risk points more accurately, the artificial potential field model is used to describe the risk points, and the integral of the curve path in the potential field is calculated. Finally, an improved genetic algorithm is designed. The limit of the path and the risk value of the path are added to the fitness function, and the selection operator and the mutation operator are improved. It can be seen from the results of simulation and real vehicle experiments that this new strategy can provide an effective path planning method to avoid risk points. Full article
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31 pages, 18303 KiB  
Article
A Novel Approach for Maize Straw Type Recognition Based on UAV Imagery Integrating Height, Shape, and Spectral Information
by Xin Liu, Huili Gong, Lin Guo, Xiaohe Gu and Jingping Zhou
Drones 2025, 9(2), 125; https://doi.org/10.3390/drones9020125 - 9 Feb 2025
Abstract
Accurately determining the distribution and quantity of maize straw types is of great significance for evaluating the effectiveness of conservation tillage, precisely estimating straw resources, and predicting the risk of straw burning. The widespread adoption of conservation tillage technology has greatly increased the [...] Read more.
Accurately determining the distribution and quantity of maize straw types is of great significance for evaluating the effectiveness of conservation tillage, precisely estimating straw resources, and predicting the risk of straw burning. The widespread adoption of conservation tillage technology has greatly increased the diversity and complexity of maize straw coverage in fields after harvest. To improve the precision and effectiveness of remote sensing recognition for maize straw types, a novel method was proposed. This method utilized unmanned aerial vehicle (UAV) multispectral imagery, integrated the Stacking Enhanced Straw Index (SESI) introduced in this study, and combined height, shape, and spectral characteristics to improve recognition accuracy. Using the original five-band multispectral imagery, a new nine-band image of the study area was constructed by integrating the calculated SESI, Canopy Height Model (CHM), Product Near-Infrared Straw Index (PNISI), and Normalized Difference Vegetation Index (NDVI) through band combination. An object-oriented classification method, utilizing a “two-step segmentation with multiple algorithms” strategy, was employed to integrate height, shape, and spectral features, enabling rapid and accurate mapping of maize straw types. The results showed that height information obtained from the CHM and spectral information derived from SESI were essential for accurately classifying maize straw types. Compared to traditional methods that relied solely on spectral information for recognition of maize straw types, the proposed approach achieved a significant improvement in overall classification accuracy, increasing it by 8.95% to reach 95.46%, with a kappa coefficient of 0.94. The remote sensing recognition methods and findings for maize straw types presented in this study can offer valuable information and technical support to agricultural departments, environmental protection agencies, and related enterprises. Full article
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15 pages, 11775 KiB  
Article
Drone Path Planning for Bridge Substructure Inspection Considering GNSS Signal Shadowing
by Phillip Kim and Junhee Youn
Drones 2025, 9(2), 124; https://doi.org/10.3390/drones9020124 - 9 Feb 2025
Abstract
Drones are useful tools for performing tasks that are difficult for humans. Thus, they are being increasingly utilized in various fields. In smart construction, a range of methods, including robots and drones, has been proposed to inspect facilities and other similar structures. Global [...] Read more.
Drones are useful tools for performing tasks that are difficult for humans. Thus, they are being increasingly utilized in various fields. In smart construction, a range of methods, including robots and drones, has been proposed to inspect facilities and other similar structures. Global navigation satellite system (GNSS) shadowing can occur when large bridge substructures, which are difficult for humans to access, are inspected using drones because GNSS is a major component in drone operation. This study develops a path planning algorithm to address areas with GNSS shadowing. The operation mode of the drone is classified into waypoint selection based on the photography point algorithm (WPS-PPA) and GNSS non-shadowing area algorithm (WPS-GNSA). Both algorithms are experimentally compared for flight performance in the GNSS shadowing area. A field experiment was conducted by varying the distance between the drone and the bridge substructure and by comparing the success of the flights. In successful flights, the GNSS reception of WPS-GNSA reached 1.4 times that of WPS-PPA. Furthermore, even in failed flights, compared to the WPS-PPA algorithm, the WPS-GNSA algorithm continued flight until the GNSS signal further deteriorated. Accordingly, WPS-GNSA is more favorable than WPS-PPA for inspecting bridge substructures under GNSS signal shadowing. Full article
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23 pages, 2471 KiB  
Article
Underwater Acoustic MAC Protocol for Multi-Objective Optimization Based on Multi-Agent Reinforcement Learning
by Jinfang Jiang, Yiling Dong, Guangjie Han and Gang Su
Drones 2025, 9(2), 123; https://doi.org/10.3390/drones9020123 - 7 Feb 2025
Abstract
In underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel allocation fairness and energy efficiency, [...] Read more.
In underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel allocation fairness and energy efficiency, this paper proposes a multi-objective optimization MAC protocol (MOMA-MAC) based on multi-agent reinforcement learning. MOMA-MAC utilizes a delay reward mechanism combined with the Multi-agent Proximal Policy Optimization Algorithm (MAPPO) to design a dual reward mechanism, which enables agents to adaptively collaborate and compete to optimize the use of network resources. According to experimental results, MOMA-MAC performs noticeably better than traditional MAC protocols and deep reinforcement learning-based methods in terms of throughput, energy efficiency, and fairness in multi-agent scenarios, showing great potential for improving communication efficiency and energy utilization. Full article
(This article belongs to the Topic Advances in Wireless and Mobile Networking)
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19 pages, 3212 KiB  
Article
A Continuous Space Path Planning Method for Unmanned Aerial Vehicle Based on Particle Swarm Optimization-Enhanced Deep Q-Network
by Le Han, Hui Zhang and Nan An
Drones 2025, 9(2), 122; https://doi.org/10.3390/drones9020122 - 7 Feb 2025
Abstract
In the field of unmanned aerial vehicle (UAV) path planning, the conventional deep Q-network (DQN) algorithm encounters the issue of action space discretization, which results in the generation of unsmooth and inefficient planned paths. To address this issue, we introduce the particle swarm [...] Read more.
In the field of unmanned aerial vehicle (UAV) path planning, the conventional deep Q-network (DQN) algorithm encounters the issue of action space discretization, which results in the generation of unsmooth and inefficient planned paths. To address this issue, we introduce the particle swarm optimization (PSO) algorithm into DQN to convert the discrete action space into a continuous one. This method divides the agent’s surrounding space into discrete and continuous action spaces. The PSO algorithm performs a global search in the continuous space to obtain a continuous candidate solution, while DQN learns a policy in the discrete space to obtain a discrete candidate solution. Then, the two candidate solutions are combined using a weighted vector method to determine a direction that balances global search and policy learning. Additionally, we introduce a novel feature matrix as the state space for DQN, providing more accurate environmental and positional representations. Furthermore, we incorporate a mechanism into the base prioritized experience replay (PER) and N-step updates, which combines the current temporal difference error (TD-error) with historical priorities and includes a policy entropy penalty term, thereby enhancing DQN’s ability to learn long-term dependencies. The performance of the PSO-DQN model is further improved through an enhanced ε-greedy policy and learning rate decay strategy. Simulation results and experiments using the Flightmare simulator demonstrate that the proposed method generates smoother and more efficient paths for drones, exhibiting strong robustness in complex environments. Full article
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23 pages, 1150 KiB  
Article
Joint Optimization of Data Collection for Multi-UAV-and-IRS-Assisted IoT in Urban Scenarios
by Yuhang Yang, Yi Hong, Xin Fan, Deying Li and Zhibo Chen
Drones 2025, 9(2), 121; https://doi.org/10.3390/drones9020121 - 7 Feb 2025
Abstract
Due to their distinct economic efficiency and adaptability advantages, Unmanned Aerial Vehicles (UAVs) can serve as mobile data collectors, collecting data from Internet of Things Devices (IoTDs). As a promising emerging technology, the Intelligent Reflecting Surface (IRS) holds the potential to overcome architectural [...] Read more.
Due to their distinct economic efficiency and adaptability advantages, Unmanned Aerial Vehicles (UAVs) can serve as mobile data collectors, collecting data from Internet of Things Devices (IoTDs). As a promising emerging technology, the Intelligent Reflecting Surface (IRS) holds the potential to overcome architectural barriers and improve communication quality in urban environments. This study investigates the development of an IoT data collection system tailored for urban environments, leveraging the synergistic operation of multiple UAVs and IRSs. In light of the limited coverage capacity of an individual IRS, we deploy several IRSs, with multiple UAVs stationed at various base stations (BSs) to collect data from IoTDs. We propose a grouping genetic algorithm-independent double deep-Q network-alternating optimization (GGA-IDDQN-AO) approach, aiming to minimize the average mission completion time for a mission cycle. This approach optimizes both the deployment and mission allocation strategies of UAVs using the grouping genetic algorithm. Additionally, by integrating deep reinforcement learning with the alternating optimization algorithm, the flight trajectories of UAVs and IRSs’ phase shifts are fine-tuned. The effectiveness of the GGA-IDDQN-AO approach is validated through comprehensive simulations, which demonstrate that the integration of IRSs leads to a notable performance enhancement in the IoT data collection system. Full article
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18 pages, 8978 KiB  
Article
Drop Nozzle from a Remotely Piloted Aerial Application System Reduces Spray Displacement
by Ryan P. Gibson, Daniel E. Martin, Zachary S. Howard, Scott A. Nolte and Mohamed A. Latheef
Drones 2025, 9(2), 120; https://doi.org/10.3390/drones9020120 - 6 Feb 2025
Abstract
Weeds remain one of the major limiting factors affecting agricultural production, causin significant yield loss globally. Spot spraying of resistant weeds as an alternative to broadcast applications provides the delivery of chemicals closer to the plant canopy. Also, wind speed can cause spray [...] Read more.
Weeds remain one of the major limiting factors affecting agricultural production, causin significant yield loss globally. Spot spraying of resistant weeds as an alternative to broadcast applications provides the delivery of chemicals closer to the plant canopy. Also, wind speed can cause spray displacement and can lead to inefficient coverage and environmental contamination. To mitigate this issue, this study sought to evaluate drop nozzles configured to direct the spray closer to the target. A remotely piloted aerial application system was retrofitted with a 60 cm drop nozzle comprising a straight stream and a 30° full cone nozzle. A tracer spray solution was applied on 13 Kromekote cards placed in a grid configuration. The center of deposition for each spray application was determined using the Python (3.11) software. Regardless of nozzle angle, the drop nozzle produced ca. 76% lower spray displacement than the no drop nozzle. The no drop nozzles had a narrower relative span compared to the drop nozzles. This suggests that smaller, more driftable fractions of the spray did not deposit on the targets due to spray displacement. Additional research investigating in-field weed species under various meteorological conditions is required to move this technology forward. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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26 pages, 5271 KiB  
Article
Research on Swarm Control Based on Complementary Collaboration of Unmanned Aerial Vehicle Swarms Under Complex Conditions
by Longqian Zhao, Bing Chen and Feng Hu
Drones 2025, 9(2), 119; https://doi.org/10.3390/drones9020119 - 6 Feb 2025
Abstract
Under complex conditions, the collaborative control capability of UAV swarms is considered to be the key to ensuring the stability and safety of swarm flights. However, in complex environments such as forest firefighting, traditional swarm control methods struggle to meet the differentiated needs [...] Read more.
Under complex conditions, the collaborative control capability of UAV swarms is considered to be the key to ensuring the stability and safety of swarm flights. However, in complex environments such as forest firefighting, traditional swarm control methods struggle to meet the differentiated needs of UAVs with differences in behavior characteristics and mutually coupled constraints, which gives rise to the problem that adjustments and feedback to the control policy during training are prone to erroneous judgments, leading to decision-making dissonance. This study proposed a swarm control method for complementary collaboration of UAVs under complex conditions. The method first generates training data through the interaction between UAV swarms and the environment; then it captures the potential patterns of UAV behaviors, extracts their differentiated behavior characteristics, and explores diversified behavior combination scenarios with complementary advantages; accordingly, dynamic behavior allocations are made according to the differences in perception accuracy and action capability to achieve collaborative cooperation; and finally, it optimizes the neural network parameters through behavior learning to improve the decision-making policy. According to the experimental results, the UAV swarm control method proposed in this study demonstrates high formation stability and integrity when dealing with the collaborative missions of multiple types of UAVs. Full article
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30 pages, 11390 KiB  
Article
A Multi-Objective Black-Winged Kite Algorithm for Multi-UAV Cooperative Path Planning
by Xiukang Liu, Fufu Wang, Yu Liu and Long Li
Drones 2025, 9(2), 118; https://doi.org/10.3390/drones9020118 - 5 Feb 2025
Abstract
In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning [...] Read more.
In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning issues that involve multiple machines. Here, we introduce an advanced mathematical model for cooperative path planning among multiple UAVs in urban logistics scenarios, employing the non-dominated sorting black-winged kite algorithm (NSBKA) to address this multi-objective optimization challenge. To evaluate the efficacy of NSBKA, it was benchmarked against other algorithms using the Zitzler, Deb, and Thiele (ZDT) test problems, Deb, Thiele, Laumanns, and Zitzler (DTLZ) test problems, and test functions from the conference on evolutionary computation 2009 (CEC2009) for three types of multi-objective problems. Comparative analyses and statistical results indicate that the proposed algorithm outperforms on all 22 test functions. To verify the capability of NSBKA in addressing the multi-UAV cooperative problem model, the algorithm is applied to solve the problem. Simulation experiments for three UAVs and five UAVs show that the proposed algorithm can obtain a more reasonable collaborative path solution set for UAVs. Moreover, path planning based on NSBKA is generally superior to other algorithms in terms of energy saving, safety, and computing efficiency during planning. This affirms the effectiveness of the meta-heuristic algorithm in dealing with multiple objective multi-UAV cooperation problems and further enhances the robustness and competitiveness of NSBKA. Full article
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20 pages, 13525 KiB  
Article
Fixed/Mobile Collaborative Traffic Flow Detection Study Based on Wireless Charging of UAVs
by Hao Wu, Mingbo Niu, Biao Wang, Kai Yan, Yuxuan Li and Hanyu Pang
Drones 2025, 9(2), 117; https://doi.org/10.3390/drones9020117 - 5 Feb 2025
Abstract
Accurate traffic flow detection plays a critical role in intelligent traffic control systems. However, conventional fixed video detection devices often face challenges such as occlusion and overlap in high-density traffic scenarios, which leads to distortions in vehicle detection. To address this issue, it [...] Read more.
Accurate traffic flow detection plays a critical role in intelligent traffic control systems. However, conventional fixed video detection devices often face challenges such as occlusion and overlap in high-density traffic scenarios, which leads to distortions in vehicle detection. To address this issue, it is essential to obtain precise vehicle data as a reliable reference for managing traffic flow during peak periods. In this paper, we propose an intelligent detection scheme using an improved YOLOv8n target recognition algorithm combined with a ByteTrack multi-target tracking algorithm. A collaborative unmanned aerial vehicle (UAV) collaborative detection framework is also established, integrating UAVs and fixed detection devices to work in tandem. Such a multi-UAV collaborative data acquiring system is designed for efficient, continuous, and uninterrupted operation, employing a three-drone rotational detection strategy. UAVs offer additional flexibility and coverage in obtaining vehicle data. However, limited power could be an essential challenge to the system’s wireless physical link stability and safety. To overcome power limitations during UAV collaboration, a wireless charging (WC) system is introduced, enabling automatic constant current–constant voltage (CC-CV) switching and preventing damage from accidental data link disabling. This collaborative traffic data acquiring and transmission system ensures a stable power supply for UAVs during high-density traffic periods, supporting their reliable UAV collaborative wireless data link. Experimental results show that the collaborative detection architecture combined with wireless charging can achieve high detection accuracy, with the recognition accuracy remaining between 0.95 and 0.99. Full article
(This article belongs to the Special Issue Urban Traffic Monitoring and Analysis Using UAVs)
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