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Volume 9
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Drones, Volume 9, Issue 3 (March 2025) – 71 articles

Cover Story (view full-size image): E-commerce growth strains traditional urban logistics, prompting drone networks for last-mile delivery. Current planning methods face route conflicts and inefficiency. This study proposes a stratified airspace solution: upper-layer transshipment routes connect essential nodes for fast cargo transfers, while lower-layer delivery routes facilitate door-to-door services. A double-layer model is designed to optimize transshipment node placement and route planning. A case study validated the model, deploying 17 transshipment nodes with 26 transshipment routes and 56 delivery routes, showing smaller network size, higher operational efficiency, and fewer intersections versus single-layer networks, confirming double-layer networks' superiority in safety and scalability for urban logistics. View this paper
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30 pages, 8862 KiB  
Article
PISCFF-LNet: A Method for Autonomous Flight of UAVs Based on Lightweight Road Extraction
by Yuanxu Zhu, Tianze Zhang, Aiying Wu and Gang Shi
Drones 2025, 9(3), 226; https://doi.org/10.3390/drones9030226 - 20 Mar 2025
Viewed by 325
Abstract
Currently, autonomous flight control for unmanned aerial vehicles (UAVs) has become increasingly critical in remote-sensing applications, such as high-resolution data acquisition and road disease detection. However, this task also faces significant challenges, particularly the weak GNSS signals in flight areas and the complex [...] Read more.
Currently, autonomous flight control for unmanned aerial vehicles (UAVs) has become increasingly critical in remote-sensing applications, such as high-resolution data acquisition and road disease detection. However, this task also faces significant challenges, particularly the weak GNSS signals in flight areas and the complex flight environment. Furthermore, many existing autonomous-flight-control algorithms for UAVs are computationally demanding, which limits their deployment on embedded devices with constrained memory and processing power, thereby affecting both operational efficiency and the safety of UAV missions. To address these issues, we propose PISCFF-LNet, a lightweight road-extraction network that integrates prior knowledge and spatial contextual features. The network employs a dual-branch encoder architecture to separately extract spatial and contextual features, thus obtaining multi-dimensional feature representations. In addition, to enhance the integration of different features and improve the overall feature representation, we also introduce a feature-fusion module. To further enhance UAV performance, we introduce an improved ray-based eight neighborhood algorithm (RENA), which efficiently extracts road-edge information with a remarkably low latency of just 7 ms, providing accurate flight guidance and reducing misidentification. To provide a comprehensive evaluation of the model’s performance, we have developed a new drone remote-sensing road-semantic-segmentation dataset, DRS Road, which includes approximately 2600 ultra-high-resolution remote-sensing images across six scene categories. The experimental results demonstrate that PISCFF-LNet achieves improvements of 1.06% in Intersection over Union (IoU) and 0.83% in F1-Score on the DeepGlobe Road dataset, and 1.03% in IoU and 0.57% in F1-Score on the DRS Road dataset, compared to existing methods. Finally, we applied the algorithm to a UAV, using a PID-based flight-control algorithm. The results show that drones employing our algorithm exhibit superior flight performance in both simulated and real-world environments. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)
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38 pages, 3832 KiB  
Review
An Integrated Approach for Earth Infrastructure Monitoring Using UAV and ERI: A Systematic Review
by Udochukwu ThankGod Ikechukwu Igwenagu, Rahul Debnath, Ahmed Abdelmoamen Ahmed and Md Jobair Bin Alam
Drones 2025, 9(3), 225; https://doi.org/10.3390/drones9030225 - 20 Mar 2025
Viewed by 698
Abstract
The integrity of earth infrastructure, encompassing slopes, dams, pavements, and embankments, is fundamental to the functioning of transportation networks, energy systems, and urban development. However, these infrastructures are increasingly threatened by a range of natural and anthropogenic factors. Conventional monitoring techniques, including inclinometers [...] Read more.
The integrity of earth infrastructure, encompassing slopes, dams, pavements, and embankments, is fundamental to the functioning of transportation networks, energy systems, and urban development. However, these infrastructures are increasingly threatened by a range of natural and anthropogenic factors. Conventional monitoring techniques, including inclinometers and handheld instruments, often exhibit limitations in spatial coverage and operational efficiency, rendering them insufficient for comprehensive evaluation. In response, Uncrewed Aerial Vehicles (UAVs) and Electrical Resistivity Imaging (ERI) have emerged as pivotal technological advancements, offering high-resolution surface characterization and critical subsurface diagnostics, respectively. UAVs facilitate the detection of deformations and geomorphological dynamics, while ERI is instrumental in identifying zones of water saturation and geological structures, detecting groundwater, characterizing vadose zone hydrology, and assessing subsurface soil and rock properties and potential slip surfaces, among others. The integration of these technologies enables multidimensional monitoring capabilities, enhancing the ability to predict and mitigate infrastructure instabilities. This article focuses on recent advancements in the integration of UAVs and ERI through data fusion frameworks, which synthesize surface and subsurface data to support proactive monitoring and predictive analytics. Drawing on a synthesis of contemporary research, this study underscores the potential of these integrative approaches to advance early-warning systems and risk mitigation strategies for critical infrastructure. Furthermore, it identifies existing research gaps and proposes future directions for the development of robust, integrated monitoring methodologies. Full article
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16 pages, 8851 KiB  
Article
MDDFA-Net: Multi-Scale Dynamic Feature Extraction from Drone-Acquired Thermal Infrared Imagery
by Zaixing Wang, Chao Dang, Rui Zhang, Linchang Wang, Yonghuan He and Rong Wu
Drones 2025, 9(3), 224; https://doi.org/10.3390/drones9030224 - 20 Mar 2025
Viewed by 359
Abstract
UAV infrared sensor technology plays an irreplaceable role in various fields. High-altitude infrared images present significant challenges for feature extraction due to their uniform texture and color, fragile and variable edge information, numerous background interference factors, and low pixel occupancy of small targets [...] Read more.
UAV infrared sensor technology plays an irreplaceable role in various fields. High-altitude infrared images present significant challenges for feature extraction due to their uniform texture and color, fragile and variable edge information, numerous background interference factors, and low pixel occupancy of small targets such as humans, bicycles, and diverse vehicles. In this paper, we propose a Multi-scale Dual-Branch Dynamic Feature Aggregation Network (MDDFA-Net) specifically designed to address these challenges in UAV infrared image processing. Firstly, a multi-scale dual-branch structure is employed to extract multi-level and edge feature information, which is crucial for detecting small targets in complex backgrounds. Subsequently, features at three different scales are fed into an Adaptive Feature Fusion Module for feature attention-weighted fusion, effectively filtering out background interference. Finally, the Multi-Scale Feature Enhancement and Fusion Module integrates high-level and low-level features across three scales to eliminate redundant information and enhance target detection accuracy. We conducted comprehensive experiments using the HIT-UAV dataset, which is characterized by its diversity and complexity, particularly in capturing small targets in high-altitude infrared images. Our method outperforms various state-of-the-art (SOTA) models across multiple evaluation metrics and also demonstrates strong inference speed capabilities across different devices, thereby proving the advantages of this approach in UAV infrared sensor image processing, especially for multi-scale small target detection. Full article
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19 pages, 3334 KiB  
Article
A Robust Control Method for the Trajectory Tracking of Hypersonic Unmanned Flight Vehicles Based on Model Predictive Control
by Haixia Ding, Bowen Xu, Weiqi Yang, Yunfan Zhou and Xianyu Wu
Drones 2025, 9(3), 223; https://doi.org/10.3390/drones9030223 - 20 Mar 2025
Viewed by 334
Abstract
Hypersonic unmanned flight vehicles have complex dynamic characteristics, such as nonlinearity, strong coupling, multiple constraints, and uncertainty. Operating in highly complex flight environments, hypersonic unmanned flight vehicles must not only contend with uncertainties and disturbances such as parameter perturbations and noise but also [...] Read more.
Hypersonic unmanned flight vehicles have complex dynamic characteristics, such as nonlinearity, strong coupling, multiple constraints, and uncertainty. Operating in highly complex flight environments, hypersonic unmanned flight vehicles must not only contend with uncertainties and disturbances such as parameter perturbations and noise but also deal with complex task scenarios such as interception and no-fly zone avoidance. These factors collectively pose great challenges on the control performance of the vehicle. To address the challenges of trajectory tracking for the vehicles under complex constraints, this paper proposes a trajectory tracking control method based on model predictive control (MPC). Firstly, a nonlinear dynamic model for hypersonic unmanned flight vehicles is established. Then, a robust model predictive controller is designed and the optimal control law is derived to address the trajectory tracking control problem under complex constraints such as parameter perturbations. Finally, simulation experiments are designed under the conditions of aerodynamic parameter changes in the longitudinal plane and lateral no-fly zone avoidance. The simulation results demonstrate that the vehicle is capable of accurately and rapidly tracking the reference despite aerodynamic parameter perturbations and large-scale lateral maneuvers, thereby validating the effectiveness of the controller. Full article
(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology)
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20 pages, 5266 KiB  
Article
Adaptive Observer-Based Neural Network Control for Multi-UAV Systems with Predefined-Time Stability
by Yunli Zhang, Hongsheng Sha, Runlong Peng, Nan Li, Zhonghua Miao, Chuangxin He and Jin Zhou
Drones 2025, 9(3), 222; https://doi.org/10.3390/drones9030222 - 19 Mar 2025
Viewed by 307
Abstract
This article proposes an observer-based predefined-time robust formation controller for uncertain multi-UAV systems with external disturbances by integrating the sliding-mode technique with neural networks. The predefined-time strategy is developed to enhance formation tracking performance, including faster convergence speed, higher accuracy, and better robustness, [...] Read more.
This article proposes an observer-based predefined-time robust formation controller for uncertain multi-UAV systems with external disturbances by integrating the sliding-mode technique with neural networks. The predefined-time strategy is developed to enhance formation tracking performance, including faster convergence speed, higher accuracy, and better robustness, while the sliding-mode scheme, integrated with the neural network, is effectively utilized to handle uncertain dynamics and external disturbances, ensuring adaptivity, availability, and robustness. Furthermore, the stability of the closed-loop control system is analyzed using Lyapunov’s method applied to the formulation of the quadrotor Newton–Euler model. This analysis fully guarantees that the desired formation position tracking and attitude stabilization goals for multi-UAV (quadrotor) systems can be achieved. Finally, the effectiveness of the theoretical results is validated through comprehensive simulations. Full article
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40 pages, 11010 KiB  
Review
PRISMA Review: Drones and AI in Inventory Creation of Signage
by Geovanny Satama-Bermeo, Jose Manuel Lopez-Guede, Javad Rahebi, Daniel Teso-Fz-Betoño, Ana Boyano and Ortzi Akizu-Gardoki
Drones 2025, 9(3), 221; https://doi.org/10.3390/drones9030221 - 19 Mar 2025
Viewed by 524
Abstract
This systematic review explores the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) in automating road signage inventory creation, employing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology to analyze recent advancements. The study evaluates cutting-edge technologies, including [...] Read more.
This systematic review explores the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) in automating road signage inventory creation, employing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology to analyze recent advancements. The study evaluates cutting-edge technologies, including UAVs equipped with deep learning algorithms and advanced sensors like light detection and ranging (LiDAR) and multispectral cameras, highlighting their roles in enhancing traffic sign detection and classification. Key challenges include detecting minor or partially obscured signs and adapting to diverse environmental conditions. The findings reveal significant progress in automation, with notable improvements in accuracy, efficiency, and real-time processing capabilities. However, limitations such as computational demands and environmental variability persist. By providing a comprehensive synthesis of current methodologies and performance metrics, this review establishes a robust foundation for future research to advance automated road infrastructure management to improve safety and operational efficiency in urban and rural settings. Full article
(This article belongs to the Special Issue Advances in Deep Learning for Drones and Its Applications: 2nd Edition)
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25 pages, 8232 KiB  
Article
Estimation of Amorphophallus Konjac Above-Ground Biomass by Integrating Spectral and Texture Information from Unmanned Aerial Vehicle-Based RGB Images
by Ziyi Yang, Hongjuan Qi, Kunrong Hu, Weili Kou, Weiheng Xu, Huan Wang and Ning Lu
Drones 2025, 9(3), 220; https://doi.org/10.3390/drones9030220 - 19 Mar 2025
Viewed by 235
Abstract
The estimation of Above-Ground Biomass (AGB) in Amorphophallus konjac (Konjac) is essential for field management and yield prediction. While previous research has demonstrated the efficacy of Unmanned Aerial Vehicle (UAV) RGB imagery in estimating AGB for monoculture crops, the applicability of these methods [...] Read more.
The estimation of Above-Ground Biomass (AGB) in Amorphophallus konjac (Konjac) is essential for field management and yield prediction. While previous research has demonstrated the efficacy of Unmanned Aerial Vehicle (UAV) RGB imagery in estimating AGB for monoculture crops, the applicability of these methods to AGB estimation in Konjac remains uncertain due to its distinct morphological traits and prevalent intercropping practices with maize. Additionally, the Vegetation Indices (VIs) and Texture Features (TFs) obtained from UAV-based RGB imagery exhibit significant redundancy, raising concerns about whether the selected optimal variables can maintain estimation accuracy. Therefore, this study assessed the effectiveness of Variable Selection Using Random Forests (VSURF) and Principal Component Analysis (PCA) in variable selection and compared the performance of Stepwise Multiple Linear Regression (SMLR) with four Machine Learning (ML) regression techniques: Random Forest Regression (RFR), Extreme Gradient Boosting Regression (XGBR), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), as well as Deep Learning (DL), in estimating the AGB of Konjac based on the selected features. The results indicate that the integration (PCA_(PCA_VIs+PCA_TFs)) of PCA-based VIs and PCA-based TFs using PCA achieved the best prediction accuracy (R2 = 0.96, RMSE = 0.08 t/hm2, MAE = 0.06 t/hm2) with SVR. In contrast, the DL model derived from AlexNet, combined with RGB imagery, yielded moderate predictive accuracy (R2 = 0.72, RMSE = 0.21 t/hm2, MAE = 0.17 t/hm2) compared with the optimal ML model. Our findings suggest that ML regression techniques, combined with appropriate variable-selected approaches, outperformed DL techniques in estimating the AGB of Konjac. This study not only provides new insights into AGB estimation in Konjac but also offers valuable guidance for estimating AGB in other crops, thereby advancing the application of UAV technology in crop biomass estimation. Full article
(This article belongs to the Special Issue Advances in the Use of UAVs for Monitoring and Analysis of Forage and Agricultural Crops: Technologies, Methods and Applications)
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39 pages, 5668 KiB  
Article
A Self-Adaptive Improved Slime Mold Algorithm for Multi-UAV Path Planning
by Yuelin Ma, Zeren Zhang, Meng Yao and Guoliang Fan
Drones 2025, 9(3), 219; https://doi.org/10.3390/drones9030219 - 18 Mar 2025
Viewed by 399
Abstract
Multi-UAV path planning presents a critical challenge in Unmanned Aerial Vehicle (UAV) applications, particularly in environments with various obstacles and restrictions. These conditions transform multi-UAV path planning into a complex optimization problem with multiple constraints, significantly reducing the number of feasible solutions and [...] Read more.
Multi-UAV path planning presents a critical challenge in Unmanned Aerial Vehicle (UAV) applications, particularly in environments with various obstacles and restrictions. These conditions transform multi-UAV path planning into a complex optimization problem with multiple constraints, significantly reducing the number of feasible solutions and complicating the generation of optimal flight trajectories. Although the slime mold algorithm (SMA) has proven effective in optimization missions, it still suffers from limitations such as inadequate exploration capacity, premature convergence, and a propensity to become stuck in local optima. These drawbacks degrade its performance in intricate multi-UAV scenarios. This study proposes a self-adaptive improved slime mold algorithm called AI-SMA to address these issues. Firstly, AI-SMA incorporates a novel search mechanism to balance exploration and exploitation by integrating ranking-based differential evolution (rank-DE). Then, a self-adaptive switch operator is introduced to increase population diversity in later iterations and avoid premature convergence. Finally, a self-adaptive perturbation strategy is implemented to provide an effective escape mechanism, facilitating faster convergence. Extensive experiments were conducted on the CEC 2017 benchmark test suite and multi-UAV path-planning scenarios. The results show that AI-SMA improves the quality of optimal fitness by approximately 7.83% over the original SMA while demonstrating superior robustness and effectiveness in generating collision-free trajectories. Full article
(This article belongs to the Special Issue Swarm Intelligence-Inspired Planning and Control for Drones)
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25 pages, 13143 KiB  
Article
Swarm Maneuver Decision Method Based on Learning-Aided Evolutionary Pigeon-Inspired Optimization for UAV Swarm Air Combat
by Yongbin Sun, Yu Chen, Chen Wei, Bin Li and Yanming Fan
Drones 2025, 9(3), 218; https://doi.org/10.3390/drones9030218 - 18 Mar 2025
Viewed by 279
Abstract
Unmanned aerial vehicle (UAV) swarm dynamic combat poses significant challenges due to its complexity and dynamism. This study introduces a novel approach that addresses these challenges through the development of a swarm maneuver decision method based on the Learning-Aided Evolutionary Pigeon-Inspired Optimization (LAEPIO) [...] Read more.
Unmanned aerial vehicle (UAV) swarm dynamic combat poses significant challenges due to its complexity and dynamism. This study introduces a novel approach that addresses these challenges through the development of a swarm maneuver decision method based on the Learning-Aided Evolutionary Pigeon-Inspired Optimization (LAEPIO) algorithm. This research proceeds systematically as follows: First, a nonlinear model of fixed-wing UAVs and a decision-making system for swarm air combat are established. Next, a situation function is applied to characterize the battlefield environment and quantify the strategic advantages of each side during the engagement. The LAEPIO algorithm is then advanced to tackle sub-tasks in swarm air combat by incorporating a learning-aided evolutionary mechanism. Building upon this foundation, a swarm maneuver decision method is designed, enabling UAV swarms to select optimal strategies from a library of maneuvers after thoroughly assessing the battlefield scenario. Finally, the efficacy and superiority of the proposed method are demonstrated through comprehensive simulations across diverse air combat scenarios. The results show that the average win rate of the proposed algorithm is 36.7% higher than that of similar algorithms. Full article
(This article belongs to the Special Issue Biological UAV Swarm Control)
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19 pages, 4832 KiB  
Review
SAMFA: A Flame Segmentation Algorithm for Infrared and Visible Aerial Images in the Same Scene
by Jianye Yuan, Min Yang, Haofei Wang, Xinwang Ding, Song Li and Wei Gong
Drones 2025, 9(3), 217; https://doi.org/10.3390/drones9030217 - 18 Mar 2025
Viewed by 385
Abstract
Existing aerial forest fire monitoring data primarily consist of infrared or visible light images. However, there is a lack of in-depth research on the ability of models to perceive fire regions across different spectral images. To address this, we first constructed a dataset [...] Read more.
Existing aerial forest fire monitoring data primarily consist of infrared or visible light images. However, there is a lack of in-depth research on the ability of models to perceive fire regions across different spectral images. To address this, we first constructed a dataset of infrared and visible light images captured in the same scene, from the same perspective, and at the same time, with pixel-level segmentation annotations of the flame regions in the images. In response to the issues of poor flame segmentation performance in the current fire images and the large number of learnable parameters in large models, we propose an improved large model algorithm, SAMFA (Segmentation Anything Model, Fire, Adapter). Firstly, while freezing the original parameters of the large model, only the additionally incorporated Adapter module is fine-tuned to better adapt the network to the specificities of the flame segmentation task. Secondly, to enhance the network’s perception of flame edges, a U-shaped mask decoder is designed. Lastly, to reduce the training difficulty, a progressive strategy combining self-supervised and fully supervised learning is employed to optimize the entire model. We compared SAMFA with five state-of-the-art image segmentation algorithms on a labeled public dataset, and the experimental results demonstrate that SAMFA performs the best. Compared to SAM, SAMFA improves the IoU by 11.94% and 6.45% on infrared and visible light images, respectively, while reducing the number of learnable parameters to 11.58 M. Full article
(This article belongs to the Collection Editorial Board Members’ Collection Series: Drones in Emergencies Operations)
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23 pages, 12360 KiB  
Article
Distributed Decision Making for Electromagnetic Radiation Source Localization Using Multi-Agent Deep Reinforcement Learning
by Jiteng Chen, Zehui Zhang, Dan Fan, Chaoqun Hou, Yue Zhang, Teng Hou, Xiangni Zou and Jun Zhao
Drones 2025, 9(3), 216; https://doi.org/10.3390/drones9030216 - 18 Mar 2025
Viewed by 347
Abstract
The detection and localization of radiation sources in urban areas present significant challenges in electromagnetic spectrum operations, particularly with the proliferation of small UAVs. To address these challenges, we propose the Multi-UAV Reconnaissance Proximal Policy Optimization (MURPPO) algorithm based on a distributed reinforcement [...] Read more.
The detection and localization of radiation sources in urban areas present significant challenges in electromagnetic spectrum operations, particularly with the proliferation of small UAVs. To address these challenges, we propose the Multi-UAV Reconnaissance Proximal Policy Optimization (MURPPO) algorithm based on a distributed reinforcement learning framework, which utilizes an independent decision making mechanism and collaborative positioning method with multiple UAVs to achieve high-precision detection and localization of radiation sources. We adopt a dual-branch actor structure for independent decisions in UAV control, which reduces decision complexity and improves learning efficiency. The algorithm incorporates task-specific knowledge into the reward function design to guide UAVs in exploring abnormal radiation sources. Furthermore, we employ a geometry-based three-point localization algorithm that leverages multiple UAVs’ spatial distribution for precise abnormal radiation source positioning. Simulations in urban environments demonstrate the effectiveness of the MURPPO algorithm, with the proportion of successfully localized target radiation sources converging to 56.5% in the later stages of training, approaching a 38.5% improvement over a traditional multi-agent proximal policy optimization algorithm. The results indicate that MURPPO effectively addresses the challenges of the intelligent sensing and localization of UAVs in complex urban electromagnetic spectrum operations. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)
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18 pages, 6835 KiB  
Article
Research on the Method for Pairing Drone Images with BIM Models Based on Revit
by Shaojin Hao, Xinghong Huang, Zhen Duan, Jia Hou, Wei Chen and Lixiong Cai
Drones 2025, 9(3), 215; https://doi.org/10.3390/drones9030215 - 17 Mar 2025
Viewed by 495
Abstract
With the development of drone and image recognition technologies, using drones to capture images for engineering structural damage detection can replace inefficient and unsafe manual maintenance inspections. This paper focuses on the pairing method between drone devices and the BIM components of large [...] Read more.
With the development of drone and image recognition technologies, using drones to capture images for engineering structural damage detection can replace inefficient and unsafe manual maintenance inspections. This paper focuses on the pairing method between drone devices and the BIM components of large buildings, with Revit’s secondary development serving as the technical approach. A plugin for pairing drone images with BIM components is developed. The research first establishes the technical scheme for pairing drone images with BIM models. Then, the positional and directional information of the drone images are extracted, and a reference coordinate system for the drone’s position and image capture orientation is introduced. The transformation method and path from the real-world coordinate system to the Revit 2023 software coordinate system are explored. To validate the interactive logic of the transformation path, a pairing plugin is developed in Revit. By employing coordinate conversion and Revit family loading procedures, the relative position and capture orientation of the drone are visualized in the 3D BIM model. The plugin uses techniques such as family element filtering and ray tracing to automatically identify and verify BIM components, ensuring the precise matching of drone images and BIM components. Finally, the plugin’s functionality is verified using a high-rise building in Wuhan as a case study. The results demonstrate that this technological approach not only improves the efficiency of pairing drone images with models in building smart maintenance but also provides a fast and reliable method for pairing drones with BIM systems in building management and operations. This contributes to the intelligent and automated development of building maintenance. Full article
(This article belongs to the Special Issue Digital Twins and Extended Reality: Opportunities and Challenges of Integrated Applications (2nd Edition))
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24 pages, 7728 KiB  
Article
UVPose: A Real-Time Key-Point-Based Skeleton Detection Network for a Drone Countermeasure System
by Bodan Yao, Weijiao Wang, Zhaojie Wang and Qi Song
Drones 2025, 9(3), 214; https://doi.org/10.3390/drones9030214 - 17 Mar 2025
Viewed by 373
Abstract
In drone countermeasure systems, drone tracking is commonly conducted using object detection methods, which are typically limited to identifying the presence of a drone. To enhance the performance of such systems and improve the accuracy of drone flight posture prediction—while precisely capturing critical [...] Read more.
In drone countermeasure systems, drone tracking is commonly conducted using object detection methods, which are typically limited to identifying the presence of a drone. To enhance the performance of such systems and improve the accuracy of drone flight posture prediction—while precisely capturing critical components such as rotors, mainboards, and flight trajectories—this paper introduces a novel drone key point detection model, UVPose, built upon the MMpose framework. First, we design an innovative backbone network, MDA-Net, based on the CSPNet architecture. This network improves multi-scale feature extraction and strengthens connections between low- and high-level features. To further enhance key point perception and pose estimation accuracy, a parallel attention mechanism, combining channel and spatial attention, is integrated. Next, we propose an advanced neck structure, RFN, which combines high-level semantic features from the backbone with rich contextual information from the neck. For the head, we adopt the SimCC method, optimized for lightweight, efficient, and accurate key point localization. Experimental results demonstrate that UVPose outperforms existing models, achieving a PCK of 79.2%, an AP of 67.2%, and an AR of 73.5%, with only 15.8 million parameters and 3.3 G of computation. This balance between accuracy and resource efficiency makes UVPose well suited for deployment on edge devices. Full article
(This article belongs to the Special Issue Advances in Deep Learning for Drones and Its Applications: 2nd Edition)
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21 pages, 2153 KiB  
Article
Scenario-Driven Evaluation of Autonomous Agents: Integrating Large Language Model for UAV Mission Reliability
by Anıl Sezgin
Drones 2025, 9(3), 213; https://doi.org/10.3390/drones9030213 - 17 Mar 2025
Cited by 1 | Viewed by 1218
Abstract
The Internet of Drones (IoD) integrates autonomous aerial platforms with security, logistics, agriculture, and disaster relief. Decision-making in IoD suffers in real-time adaptability, platform interoperability, and scalability. Conventional decision frameworks with heuristic algorithms and narrow Artificial Intelligence (AI) falter in complex environments. To [...] Read more.
The Internet of Drones (IoD) integrates autonomous aerial platforms with security, logistics, agriculture, and disaster relief. Decision-making in IoD suffers in real-time adaptability, platform interoperability, and scalability. Conventional decision frameworks with heuristic algorithms and narrow Artificial Intelligence (AI) falter in complex environments. To mitigate these, in this study, an augmented decision model is proposed, combining large language models (LLMs) and retrieval-augmented generation (RAG) for enhancing IoD intelligence. Centralized intelligence is achieved by processing environment factors, mission logs, and telemetry, with real-time adaptability. Efficient retrieval of contextual information through RAG is merged with LLMs for timely, correct decision-making. Contextualized decision-making vastly improves adaptability in uncertain environments for a drone network. With LLMs and RAG, the model introduces a scalable, adaptable IoD operations solution. It enables the development of autonomous aerial platforms in industries, with future work in computational efficiency, ethics, and extending operational environments. In-depth analysis with the collection of drone telemetry logs and operational factors was conducted. Decision accuracy, response time, and contextual relevance were measured to gauge system effectiveness. The model’s performance increased remarkably, with a BLEU of 0.82 and a cosine similarity of 0.87, proving its effectiveness for operational commands. Decision latency averaged 120 milliseconds, proving its suitability for real-time IoD use cases. Full article
(This article belongs to the Section Drone Communications)
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19 pages, 2535 KiB  
Article
A Novel HGW Optimizer with Enhanced Differential Perturbation for Efficient 3D UAV Path Planning
by Lei Lv, Hongjuan Liu, Ruofei He, Wei Jia and Wei Sun
Drones 2025, 9(3), 212; https://doi.org/10.3390/drones9030212 - 16 Mar 2025
Viewed by 317
Abstract
In general, path planning for unmanned aerial vehicles (UAVs) is modeled as a challenging optimization problem that is critical to ensuring efficient UAV mission execution. The challenge lies in the complexity and uncertainty of flight scenarios, particularly in three-dimensional scenarios. In this study, [...] Read more.
In general, path planning for unmanned aerial vehicles (UAVs) is modeled as a challenging optimization problem that is critical to ensuring efficient UAV mission execution. The challenge lies in the complexity and uncertainty of flight scenarios, particularly in three-dimensional scenarios. In this study, one introduces a framework for UAV path planning in a 3D environment. To tackle this challenge, we develop an innovative hybrid gray wolf optimizer (GWO) algorithm, named SDPGWO. The proposed algorithm simplifies the position update mechanism of GWO and incorporates a differential perturbation strategy into the search process, enhancing the optimization ability and avoiding local minima. Simulations conducted in various scenarios reveal that the SDPGWO algorithm excels in rapidly generating superior-quality paths for UAVs. In addition, it demonstrates enhanced robustness in handling complex 3D environments and outperforms other related algorithms in both performance and reliability. 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, 23754 KiB  
Article
A Low-Latency Dynamic Object Detection Algorithm Fusing Depth and Events
by Duowen Chen, Liqi Zhou and Chi Guo
Drones 2025, 9(3), 211; https://doi.org/10.3390/drones9030211 - 15 Mar 2025
Viewed by 407
Abstract
Existing RGB image-based object detection methods achieve high accuracy when objects are static or in quasi-static conditions but demonstrate degraded performance with fast-moving objects due to motion blur artifacts. Moreover, state-of-the-art deep learning methods, which rely on RGB images as input, necessitate training [...] Read more.
Existing RGB image-based object detection methods achieve high accuracy when objects are static or in quasi-static conditions but demonstrate degraded performance with fast-moving objects due to motion blur artifacts. Moreover, state-of-the-art deep learning methods, which rely on RGB images as input, necessitate training and inference on high-performance graphics cards. These cards are not only bulky and power-hungry but also challenging to deploy on compact robotic platforms. Fortunately, the emergence of event cameras, inspired by biological vision, provides a promising solution to these limitations. These cameras offer low latency, minimal motion blur, and non-redundant outputs, making them well suited for dynamic obstacle detection. Building on these advantages, a novel methodology was developed through the fusion of events with depth to address the challenge of dynamic object detection. Initially, an adaptive temporal sampling window was implemented to selectively acquire event data and supplementary information, contingent upon the presence of objects within the visual field. Subsequently, a warping transformation was applied to the event data, effectively eliminating artifacts induced by ego-motion while preserving signals originating from moving objects. Following this preprocessing stage, the transformed event data were converted into an event queue representation, upon which denoising operations were performed. Ultimately, object detection was achieved through the application of image moment analysis to the processed event queue representation. The experimental results show that, compared with the current state-of-the-art methods, the proposed method has improved the detection speed by approximately 20% and the accuracy by approximately 5%. To substantiate real-world applicability, the authors implemented a complete obstacle avoidance pipeline, integrating our detector with planning modules and successfully deploying it on a custom-built quadrotor platform. Field tests confirm reliable avoidance of an obstacle approaching at approximately 8 m/s, thereby validating practical deployment potential. Full article
(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)
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19 pages, 2858 KiB  
Article
Fast Entry Trajectory Planning Method for Wide-Speed Range UASs
by Weihao Feng, Dongzhu Feng, Pei Dai, Shaopeng Li, Chenkai Zhang and Jiadi Ma
Drones 2025, 9(3), 210; https://doi.org/10.3390/drones9030210 - 15 Mar 2025
Viewed by 397
Abstract
Convex optimization has gained increasing popularity in trajectory planning methods for wide-speed range unmanned aerial systems (UASs) with multiple no-fly zones (NFZs) in the entry phase. To address the issues of slow or even infeasible solutions, a modified fast trajectory planning method using [...] Read more.
Convex optimization has gained increasing popularity in trajectory planning methods for wide-speed range unmanned aerial systems (UASs) with multiple no-fly zones (NFZs) in the entry phase. To address the issues of slow or even infeasible solutions, a modified fast trajectory planning method using the approaches of variable trust regions and adaptive generated initial values is proposed in this paper. A dimensionless energy-based dynamics model detailing the constraints of the entry phase is utilized to formulate the original entry trajectory planning problem. This problem is then transformed into a finite-dimensional convex programming problem, using techniques such as successive linearization and interval trapezoidal discretization. Finally, a variable trust region strategy and an adaptive initial value generation strategy are adopted to accelerate the solving process in complex flight environments. The experimental results imply that the strategy proposed in this paper can significantly reduce the solution time of trajectory planning for wide-speed range UASs in complex environments. Full article
(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology)
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23 pages, 3354 KiB  
Article
Simultaneous Learning Knowledge Distillation for Image Restoration: Efficient Model Compression for Drones
by Yongheng Zhang
Drones 2025, 9(3), 209; https://doi.org/10.3390/drones9030209 - 14 Mar 2025
Viewed by 734
Abstract
Deploying high-performance image restoration models on drones is critical for applications like autonomous navigation, surveillance, and environmental monitoring. However, the computational and memory limitations of drones pose significant challenges to utilizing complex image restoration models in real-world scenarios. To address this issue, we [...] Read more.
Deploying high-performance image restoration models on drones is critical for applications like autonomous navigation, surveillance, and environmental monitoring. However, the computational and memory limitations of drones pose significant challenges to utilizing complex image restoration models in real-world scenarios. To address this issue, we propose the Simultaneous Learning Knowledge Distillation (SLKD) framework, specifically designed to compress image restoration models for resource-constrained drones. SLKD introduces a dual-teacher, single-student architecture that integrates two complementary learning strategies: Degradation Removal Learning (DRL) and Image Reconstruction Learning (IRL). In DRL, the student encoder learns to eliminate degradation factors by mimicking Teacher A, which processes degraded images utilizing a BRISQUE-based extractor to capture degradation-sensitive natural scene statistics. Concurrently, in IRL, the student decoder reconstructs clean images by learning from Teacher B, which processes clean images, guided by a PIQE-based extractor that emphasizes the preservation of edge and texture features essential for high-quality reconstruction. This dual-teacher approach enables the student model to learn from both degraded and clean images simultaneously, achieving robust image restoration while significantly reducing computational complexity. Experimental evaluations across five benchmark datasets and three restoration tasks—deraining, deblurring, and dehazing—demonstrate that, compared to the teacher models, the SLKD student models achieve an average reduction of 85.4% in FLOPs and 85.8% in model parameters, with only a slight average decrease of 2.6% in PSNR and 0.9% in SSIM. These results highlight the practicality of integrating SLKD-compressed models into autonomous systems, offering efficient and real-time image restoration for aerial platforms operating in challenging environments. Full article
(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones, 2nd Edition)
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22 pages, 491 KiB  
Article
Enhancing Physical-Layer Security in UAV-Assisted Communications: A UAV-Mounted Reconfigurable Intelligent Surface Scheme for Secrecy Rate Optimization
by Mengqiu Chai, Yuan Liu, Shengjie Zhao and Hao Deng
Drones 2025, 9(3), 208; https://doi.org/10.3390/drones9030208 - 14 Mar 2025
Viewed by 650
Abstract
With the wide application of unmanned aerial vehicles (UAVs) in the military and civilian fields, the physical layer security of UAV-assisted communication has attracted more and more attention in recent years. Reconfigurable intelligent surface (RIS) is a revolutionizing and promising technology that can [...] Read more.
With the wide application of unmanned aerial vehicles (UAVs) in the military and civilian fields, the physical layer security of UAV-assisted communication has attracted more and more attention in recent years. Reconfigurable intelligent surface (RIS) is a revolutionizing and promising technology that can improve spectrum efficiency through intelligent reconfiguration of the propagation environment. In this paper, we investigate the physical layer security of RIS and UAV-assisted communication systems. Specifically, we consider the scenario of multiple eavesdroppers wiretapping the communication between the base station and the legitimate user and propose a secure mechanism that deploys the RIS on a dynamic UAV for security assistance. In order to maximize the average secrecy rate of the system, we propose a joint optimization problem of joint UAV flight trajectory, RIS transmit phase shift, and base station transmit power. Since the proposed problem is non-convex, it is difficult to solve it directly, so we propose a joint optimization algorithm based on block coordinate descent and successive convex optimization techniques. Simulation results verify the effectiveness of our proposed design in improving the secrecy performance of the considered system. Full article
(This article belongs to the Special Issue Physical-Layer Security in Drone Communications)
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26 pages, 3355 KiB  
Article
Online Resource Allocation and Trajectory Optimization of STAR–RIS–Assisted UAV–MEC System
by Xi Hu, Hongchao Zhao, Wujie Zhang and Dongyang He
Drones 2025, 9(3), 207; https://doi.org/10.3390/drones9030207 - 14 Mar 2025
Viewed by 542
Abstract
In urban environments, the highly complex communication environment often leads to blockages in the link between ground users (GUs) and unmanned aerial vehicles (UAVs), resulting in poor communication quality. Although traditional reconfigurable intelligent surfaces (RISs) can improve wireless channel quality, they can only [...] Read more.
In urban environments, the highly complex communication environment often leads to blockages in the link between ground users (GUs) and unmanned aerial vehicles (UAVs), resulting in poor communication quality. Although traditional reconfigurable intelligent surfaces (RISs) can improve wireless channel quality, they can only provide reflection services and have limited coverage. For this reason, we study a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR–RIS)–assisted UAV–mobile edge computing (UAV–MEC) network, which can serve multiple users residing in the transmission area and reflection area, and switch between reflection and transmission modes according to the relative positions of the UAV, GUs, and STAR–RIS, providing users with more flexible and efficient services. The system comprehensively considers user transmit power, time slot allocation, UAV flight trajectory, STAR–RIS mode selection, and phase angle matrix, achieving long–term energy consumpution minimization while ensuring stable task backlog queue. Since the proposed problem is a long–term stochastic optimization problem, we use the Lyapunov method to transform it into three deterministic online optimization subproblems and iteratively solve them alternately. Specifically, we firstly use the Lambert function to solve for the closed-form solution of the transmit power; then, use Lagrange duality and the Karush–Kuhn–Tucker conditions to solve time slot allocation; finally, successive convex approximation is used to obtain trajectory planning for UAVs with lower complexity, and triangular inequalities are used to solve the STAR–RIS phase shift. The simulation results show that the proposed scheme has better performance than other benchmark schemes in maintaining queue stability and reducing energy consumption. Full article
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26 pages, 4783 KiB  
Article
A Hybrid Decision-Making Framework for UAV-Assisted MEC Systems: Integrating a Dynamic Adaptive Genetic Optimization Algorithm and Soft Actor–Critic Algorithm with Hierarchical Action Decomposition and Uncertainty-Quantified Critic Ensemble
by Yu Yang, Yanjun Shi, Xing Cui, Jiajian Li and Xijun Zhao
Drones 2025, 9(3), 206; https://doi.org/10.3390/drones9030206 - 13 Mar 2025
Viewed by 626
Abstract
With the continuous progress of UAV technology and the rapid development of mobile edge computing (MEC), the UAV-assisted MEC system has shown great application potential in special fields such as disaster rescue and emergency response. However, traditional deep reinforcement learning (DRL) decision-making methods [...] Read more.
With the continuous progress of UAV technology and the rapid development of mobile edge computing (MEC), the UAV-assisted MEC system has shown great application potential in special fields such as disaster rescue and emergency response. However, traditional deep reinforcement learning (DRL) decision-making methods suffer from limitations such as difficulty in balancing multiple objectives and training convergence when making mixed action space decisions for UAV path planning and task offloading. This article innovatively proposes a hybrid decision framework based on the improved Dynamic Adaptive Genetic Optimization Algorithm (DAGOA) and soft actor–critic with hierarchical action decomposition, an uncertainty-quantified critic ensemble, and adaptive entropy temperature, where DAGOA performs an effective search and optimization in discrete action space, while SAC can perform fine control and adjustment in continuous action space. By combining the above algorithms, the joint optimization of drone path planning and task offloading can be achieved, improving the overall performance of the system. The experimental results show that the framework offers significant advantages in improving system performance, reducing energy consumption, and enhancing task completion efficiency. When the system adopts a hybrid decision framework, the reward score increases by a maximum of 153.53% compared to pure deep reinforcement learning algorithms for decision-making. Moreover, it can achieve an average improvement of 61.09% on the basis of various reinforcement learning algorithms such as proposed SAC, proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), and twin delayed deep deterministic policy gradient (TD3). Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)
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20 pages, 20397 KiB  
Article
Assessing Seasonal and Diurnal Thermal Dynamics of Water Channel and Highway Bridges Using Unmanned Aerial Vehicle Thermography
by Abdulkadir Memduhoğlu and Nizar Polat
Drones 2025, 9(3), 205; https://doi.org/10.3390/drones9030205 - 13 Mar 2025
Viewed by 532
Abstract
Bridges are critical components of modern infrastructure, yet their long-term performance is often compromised by thermal stresses induced by environmental and material factors. Despite advances in remote sensing, characterizing the complex thermal dynamics of bridge structures remains challenging. In this study, we investigate [...] Read more.
Bridges are critical components of modern infrastructure, yet their long-term performance is often compromised by thermal stresses induced by environmental and material factors. Despite advances in remote sensing, characterizing the complex thermal dynamics of bridge structures remains challenging. In this study, we investigate the seasonal and diurnal thermal behavior of two common bridge types—a water channel bridge with paving stone surfacing and a highway bridge with asphalt surfacing—using high-resolution UAV thermography. A pre-designed photogrammetric flight plan (yielding a ground sampling distance of <5 cm) was implemented to acquire thermal and visual imagery during four distinct temporal windows (winter morning, winter evening, summer morning, and summer evening). The methodology involved generating thermal orthophotos via structure-from-motion techniques, extracting systematic temperature measurements (n=150 per bridge), and analyzing these using independent-samples and paired t-tests to quantify material-specific thermal responses and environmental coupling effects. The results reveal that the water channel bridge exhibited significantly lower thermal variability (1.54–3.48 °C) compared to the highway bridge (3.27–5.66 °C), with pronounced differences during winter mornings (Cohen’s d=2.03, p<0.001). Furthermore, material properties strongly modulated thermal dynamics, as evidenced by the significant temperature differentials between the paving stone and asphalt surfaces, while ambient conditions further influence surface–ambient coupling (r=0.961 vs. 0.975). The results provide UAV-based quantitative metrics for bridge thermal assessment and empirical evidence to support the temporal monitoring of bridges with varying materials and environmental conditions for future studies. Full article
(This article belongs to the Special Issue Unconventional Drone-Based Surveying 2nd Edition)
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24 pages, 4006 KiB  
Article
BiLSTM-Attention-PFTBD: Robust Long-Baseline Acoustic Localization for Autonomous Underwater Vehicles in Adversarial Environments
by Yizhuo Jia, Yi Lou, Yunjiang Zhao, Sibo Sun and Julian Cheng
Drones 2025, 9(3), 204; https://doi.org/10.3390/drones9030204 - 12 Mar 2025
Viewed by 408
Abstract
The accurate and reliable localization and tracking of Autonomous Underwater Vehicles (AUVs) are essential for the success of various underwater missions, such as environmental monitoring, subsea resource exploration, and military operations. long-baseline acoustic localization (LBL) is a fundamental technique for underwater positioning, but [...] Read more.
The accurate and reliable localization and tracking of Autonomous Underwater Vehicles (AUVs) are essential for the success of various underwater missions, such as environmental monitoring, subsea resource exploration, and military operations. long-baseline acoustic localization (LBL) is a fundamental technique for underwater positioning, but it faces significant challenges in adversarial environments. These challenges include abrupt target maneuvers and intentional signal interference, both of which degrade the performance of traditional localization algorithms. Although particle filter-based Track-Before-Detect (PFTBD) algorithms are effective under normal submarine conditions, they struggle to maintain accuracy in adversarial environments due to their dependence on conventional likelihood calculations. To address this, we propose the BiLSTM-Attention-PFTBD algorithm, which enhances the traditional PFTBD framework by integrating bidirectional Long Short-Term Memory (BiLSTM) networks with multi-head attention mechanisms. This combination enables better feature extraction and adaptation for localizing AUVs in adversarial underwater environments. Simulation results demonstrate that the proposed method outperforms traditional PFTBD algorithms, significantly reducing localization errors and maintaining robust tracking accuracy in adversarial settings. Full article
(This article belongs to the Special Issue Advances in Autonomy of Underwater Vehicles (AUVs))
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31 pages, 4011 KiB  
Review
A Survey on Obstacle Detection and Avoidance Methods for UAVs
by Ahmad Merei, Hamid Mcheick, Alia Ghaddar and Djamal Rebaine
Drones 2025, 9(3), 203; https://doi.org/10.3390/drones9030203 - 12 Mar 2025
Viewed by 2492
Abstract
Obstacle avoidance is crucial for the successful completion of UAV missions. Static and dynamic obstacles, such as trees, buildings, flying birds, or other UAVs, can threaten these missions. As a result, safe path planning is essential, particularly for missions involving multiple UAVs. Collision-free [...] Read more.
Obstacle avoidance is crucial for the successful completion of UAV missions. Static and dynamic obstacles, such as trees, buildings, flying birds, or other UAVs, can threaten these missions. As a result, safe path planning is essential, particularly for missions involving multiple UAVs. Collision-free paths can be designed in either 2D or 3D environments, depending on the scenario. This study provides an overview of recent advancements in obstacle avoidance and path planning for UAVs. These methods are compared based on various criteria, including avoidance techniques, obstacle types, the environment explored, sensor equipment, map types, and path statuses. Additionally, this paper includes a process addressing obstacle detection and avoidance and reviews the evolution of obstacle detection and avoidance (ODA) techniques in UAVs over the past decade. Full article
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46 pages, 5374 KiB  
Article
Exploring the Feasibility of Airfoil Integration on a Multirotor Frame for Enhanced Aerodynamic Performance
by António André C. Freitas, Victor Wilson G. Azevedo, Vitor Hugo A. Aguiar, Jorge Miguel A. Lopes and Rui Miguel A. Caldeira
Drones 2025, 9(3), 202; https://doi.org/10.3390/drones9030202 - 12 Mar 2025
Viewed by 607
Abstract
Unmanned Aerial Vehicles (UAVs) have become indispensable across various industries, but their efficiency, particularly in multirotor designs, remains constrained by aerodynamic limitations. This study investigates the integration of airfoil shapes into the arms of multirotor UAV frames to enhance aerodynamic performance, thereby improving [...] Read more.
Unmanned Aerial Vehicles (UAVs) have become indispensable across various industries, but their efficiency, particularly in multirotor designs, remains constrained by aerodynamic limitations. This study investigates the integration of airfoil shapes into the arms of multirotor UAV frames to enhance aerodynamic performance, thereby improving energy efficiency and extending flight times. By employing Computational Fluid Dynamics (CFD) simulations, this research compares the aerodynamic characteristics of a standard quadrotor frame against an airfoil-integrated design. The results reveal that while airfoil-shaped arms marginally increase drag in cruise flight, they significantly reduce downforce across all flight conditions, optimizing thrust utilization and lowering overall energy consumption. The findings suggest potential applications in military reconnaissance, agriculture, and other fields requiring longer UAV flight durations and improved efficiency. This work advances UAV design by demonstrating a feasible method for enhancing the performance of multirotor systems while maintaining structural simplicity and cost-effectiveness. Full article
(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)
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24 pages, 4444 KiB  
Article
Model-Based Offline Reinforcement Learning for AUV Path-Following Under Unknown Ocean Currents with Limited Data
by Xinmao Li, Lingbo Geng, Kaizhou Liu and Yifeng Zhao
Drones 2025, 9(3), 201; https://doi.org/10.3390/drones9030201 - 12 Mar 2025
Viewed by 433
Abstract
Minimizing experimental data while maintaining good AUV path-following performance is essential to reduce controller design costs and ensure AUV safety, particularly in complex and dynamic underwater environments with unknown ocean currents. To address this, we propose a conservative offline model-based Q-learning (CMQL) algorithm. [...] Read more.
Minimizing experimental data while maintaining good AUV path-following performance is essential to reduce controller design costs and ensure AUV safety, particularly in complex and dynamic underwater environments with unknown ocean currents. To address this, we propose a conservative offline model-based Q-learning (CMQL) algorithm. This algorithm is robust to unknown disturbance and efficient in data utilization. The CMQL-based controller is trained offline with dynamics and kinematics models constructed from limited AUV motion data and requires no additional fine-tuning for deployment. These models, constructed by improved conditional neural processes, enable accurate long-term motion state predictions within the data distribution. Additionally, the carefully designed state space, action space, reward function, and domain randomization ensure strong generalization and disturbance rejection without extra compensation. Simulation results demonstrate that CMQL achieves effective path-following under unknown ocean currents with a limited dataset of only 1000 data points. This method also achieves zero-shot transfer, demonstrating its generalization and potential for real-world applications. Full article
(This article belongs to the Section Drone Design and Development)
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16 pages, 857 KiB  
Article
E²VRP-CPP: An Energy-Efficient Approach for Multi-UAV Multi-Region Coverage Path Planning Optimization in the Enhanced Vehicle Routing Problem
by Yuechao Zang, Xueqin Huang, Min Lu, Qianzhen Zhang and Xianqiang Zhu
Drones 2025, 9(3), 200; https://doi.org/10.3390/drones9030200 - 11 Mar 2025
Viewed by 553
Abstract
Unmanned Aerial Vehicles (UAVs) are widely used in applications such as land assessment, surveillance, and rescue operations, where they are often required to cover multiple disjoint regions. Coverage Path Planning (CPP) aims to determine optimal paths for UAVs to cover these areas. While [...] Read more.
Unmanned Aerial Vehicles (UAVs) are widely used in applications such as land assessment, surveillance, and rescue operations, where they are often required to cover multiple disjoint regions. Coverage Path Planning (CPP) aims to determine optimal paths for UAVs to cover these areas. While CPP for single regions has been extensively studied, multi-region CPP with multiple UAVs remains underexplored. Existing methods typically focus on minimizing path length, but often neglect the nonlinear variations in energy consumption during flight, limiting their practical applicability. This paper addresses the multi-UAV, multi-region CPP as a variant of the Vehicle Routing Problem (VRP) with energy estimation. We propose an approach that optimizes UAV flight speeds to minimize energy consumption, supported by an accurate energy estimation algorithm. In addition, a heuristic algorithm is developed to balance the distribution of tasks among UAVs, considering both the scanning and transit times. Experiments using real-world data from the Changsha urban area demonstrate that our approach outperforms state-of-the-art methods in computational efficiency and energy savings, highlighting its potential for practical UAV deployment. Full article
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18 pages, 426 KiB  
Article
Physical-Layer Security Enhancement for UAV Downlink Communication Using Joint Precoding and Artificial Noise Design in Generalized Spatial Directional Modulation
by Xianglu Li, Youyang Xiang, Jie Zhou, Ying Luo, Qilong Du, Dong Hou and Jie Tian
Drones 2025, 9(3), 199; https://doi.org/10.3390/drones9030199 - 11 Mar 2025
Viewed by 674
Abstract
This paper proposes a novel joint precoding and artificial noise design framework for generalized spatial directional modulation (AN-GSDM) in unmanned aerial vehicle (UAV) communications, aimed at enhancing the physical-layer security of downlink UAV communication systems. The key innovation lies in the dynamic co-optimization [...] Read more.
This paper proposes a novel joint precoding and artificial noise design framework for generalized spatial directional modulation (AN-GSDM) in unmanned aerial vehicle (UAV) communications, aimed at enhancing the physical-layer security of downlink UAV communication systems. The key innovation lies in the dynamic co-optimization of multi-beam control and artificial noise (AN) power allocation under mobility constraints, enabling real-time adaptation to varying channel conditions. This approach jointly optimizes the precoding matrix and power-control factor, facilitating the effective management of multi-beams and AN to maximize the secrecy rate. The secrecy rate expression is derived, and the corresponding joint optimization problem is formulated. Due to the non-convex nature of the problem and the lack of a closed-form solution, an alternating iterative algorithm is proposed. This algorithm alternates between optimizing the precoding matrix using gradient descent and deriving a suboptimal closed-form solution for the power-control factor. Simulation results confirm that the proposed algorithm significantly enhances security by maximizing the secrecy rate, reducing eavesdroppers’ achievable rate to near zero, while simultaneously maintaining legitimate user’s rate. The approach not only strengthens security but also preserves system effectiveness, demonstrating robust convergence properties. This makes it a practical and promising solution for secure UAV communication. Full article
(This article belongs to the Special Issue Physical-Layer Security in Drone Communications)
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24 pages, 2940 KiB  
Communication
Secure Transmission for RIS-Assisted Downlink Hybrid FSO/RF SAGIN: Sum Secrecy Rate Maximization
by Jiawei Li, Weichao Yang, Tong Liu, Li Li, Yi Jin, Yixin He and Dawei Wang
Drones 2025, 9(3), 198; https://doi.org/10.3390/drones9030198 - 10 Mar 2025
Viewed by 581
Abstract
This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted downlink hybrid free-space optics (FSO)/radio frequency (RF) space–air–ground integrated network (SAGIN) architecture, where the high altitude platform (HAP) converts the optical signal sent by the satellite into an electrical signal through optoelectronic conversion. The [...] Read more.
This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted downlink hybrid free-space optics (FSO)/radio frequency (RF) space–air–ground integrated network (SAGIN) architecture, where the high altitude platform (HAP) converts the optical signal sent by the satellite into an electrical signal through optoelectronic conversion. The drone equipped with RIS dynamically adjusts the signal path to serve ground users, thereby addressing communication challenges caused by RF link blockages from clouds or buildings. To improve the security performance of SAGIN, this paper maximizes the sum secrecy rate (SSR) by optimizing the power allocation, RIS phase shift, and drone trajectory. Then, an alternating iterative framework is proposed for a joint solution using the simulated annealing algorithm, semi-definite programming, and the designed deep deterministic policy gradient (DDPG) algorithm. The simulation results show that the proposed scheme can significantly enhance security performance. Specifically, compared with the NOMA and SDMA schemes, the SSR of the proposed scheme is increased by 39.7% and 286.7%, respectively. Full article
(This article belongs to the Special Issue Advances in UAV Networks Towards 6G)
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26 pages, 11344 KiB  
Article
A Robust Tool for 3D Rail Mapping Using UAV Data Photogrammetry, AI and CV: qAicedrone-Rail
by Innes Barbero-García, Diego Guerrero-Sevilla, David Sánchez-Jiménez and David Hernández-López
Drones 2025, 9(3), 197; https://doi.org/10.3390/drones9030197 - 10 Mar 2025
Viewed by 689
Abstract
Rail systems are essential for economic growth and regional connectivity, but aging infrastructures face challenges from increased demand and environmental factors. Traditional inspection methods, such as visual inspections, are inefficient and costly and pose safety risks. Unmanned Aerial Vehicles (UAVs) have become a [...] Read more.
Rail systems are essential for economic growth and regional connectivity, but aging infrastructures face challenges from increased demand and environmental factors. Traditional inspection methods, such as visual inspections, are inefficient and costly and pose safety risks. Unmanned Aerial Vehicles (UAVs) have become a viable alternative to rail mapping and monitoring. This study presents a robust method for the 3D extraction of rail tracks from UAV-based aerial imagery. The approach integrates YOLOv8 for initial detection and segmentation, photogrammetry for 3D data extraction and computer vision techniques with a Multiview approach to enhance accuracy. The tool was tested in a real-world complex scenario. Errors of 2 cm and 4 cm were obtained for planimetry and altimetry, respectively. The detection performance and metric results show a significant reduction in errors and increased precision compared to intermediate YOLO-based outputs. In comparison to most image-based methodologies, the tool has the advantage of generating both accurate altimetric and planimetric data. The generated data exceed the requirements for cartography at a scale of 1:500, as required by the Spanish regulations for photogrammetric works for rail infrastructures. The tool is integrated into the open-source QGIS platform; the tool is user-friendly and aims to improve rail system maintenance and safety. Full article
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