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Volume 9
Issue 11
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Drones, Volume 9, Issue 11 (November 2025) – 18 articles

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22 pages, 8072 KB  
Article
Enhanced Dynamic Obstacle Avoidance for UAVs Using Event Camera and Ego-Motion Compensation
by Bahar Ahmadi and Guangjun Liu
Drones 2025, 9(11), 745; https://doi.org/10.3390/drones9110745 (registering DOI) - 25 Oct 2025
Abstract
To navigate dynamic environments safely, UAVs require accurate, real time onboard perception, which relies on ego motion compensation to separate self-induced motion from external dynamics and enable reliable obstacle detection. Traditional ego-motion compensation techniques are mainly based on optimization processes and may be [...] Read more.
To navigate dynamic environments safely, UAVs require accurate, real time onboard perception, which relies on ego motion compensation to separate self-induced motion from external dynamics and enable reliable obstacle detection. Traditional ego-motion compensation techniques are mainly based on optimization processes and may be computationally expensive for real-time applications or lack the precision needed to handle both rotational and translational movements, leading to issues such as misidentifying static elements as dynamic obstacles and generating false positives. In this paper, we propose a novel approach that integrates an event camera-based perception pipeline with an ego-motion compensation algorithm to accurately compensate for both rotational and translational UAV motion. An enhanced warping function, integrating IMU and depth data, is constructed to compensate camera motion based on real-time IMU data to remove ego motion from the asynchronous event stream, enhancing detection accuracy by reducing false positives and missed detections. On the compensated event stream, dynamic obstacles are detected by applying a motion aware adaptive threshold to the normalized mean timestamp image, with the threshold derived from the image’s spatial mean and standard deviation and adjusted by the UAV’s angular and linear velocities. Furthermore, in conjunction with a 3D Artificial Potential Field (APF) for obstacle avoidance, the proposed approach generates smooth, collision-free paths, addressing local minima issues through a rotational force component to ensure efficient UAV navigation in dynamic environments. The effectiveness of the proposed approach is validated through simulations, and its application for UAV navigation, safety, and efficiency in environments such as warehouses is demonstrated, where real-time response and precise obstacle avoidance are essential. Full article
(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones: 2nd Edition)
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21 pages, 816 KB  
Article
Urban Dimension of U-Space: Local Planning Considerations for Drone Integration
by Tobias Biehle
Drones 2025, 9(11), 744; https://doi.org/10.3390/drones9110744 (registering DOI) - 25 Oct 2025
Abstract
U-Space, the European Union’s legal framework for enabling drone traffic in low altitude, has implications extending beyond airspace management, particularly on the sustainable development of urban areas. This article presents a case study involving regional and local level representatives, examining anticipated concerns and [...] Read more.
U-Space, the European Union’s legal framework for enabling drone traffic in low altitude, has implications extending beyond airspace management, particularly on the sustainable development of urban areas. This article presents a case study involving regional and local level representatives, examining anticipated concerns and strategic interests, as well as managing requirements in urban U-Space planning. Following a three-stage capacity building process conducted in the German federal state of Hamburg, the results specify ambitions for enhancing economic attractiveness coupled with locally embedded visions for improved public service provision. Instruments that have shown apposite in the given setting to address concerns surrounding public order and security, as well as the impairment of area functions, are presented. The challenges of implementing U-Space in alignment with societal expectations are outlined. Based on the discussion of these findings, recommendations for local-level capacity-building policy and the multi-level governance of U-Space are derived. Full article
(This article belongs to the Special Issue Urban Air Mobility Solutions: UAVs for Smarter Cities)
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25 pages, 17236 KB  
Article
Hierarchical Deep Learning Model for Identifying Similar Targets in UAV Imagery
by Dmytro Borovyk, Oleksander Barmak, Pavlo Radiuk and Iurii Krak
Drones 2025, 9(11), 743; https://doi.org/10.3390/drones9110743 (registering DOI) - 25 Oct 2025
Abstract
Accurate object detection in UAV imagery is critical for situational awareness, yet conventional deep learning models often struggle to distinguish between visually similar targets. To address this challenge, this study introduces a hierarchical deep learning architecture that decomposes the multi-class detection task into [...] Read more.
Accurate object detection in UAV imagery is critical for situational awareness, yet conventional deep learning models often struggle to distinguish between visually similar targets. To address this challenge, this study introduces a hierarchical deep learning architecture that decomposes the multi-class detection task into a structured, multi-level classification cascade. Our approach combines a high-recall Faster R-CNN for initial object proposal, specialized YOLO models for granular feature extraction, and a dedicated FT-Transformer for fine-grained classification. Experimental evaluation on a complex dataset demonstrated the effectiveness of this strategy. The hierarchical model achieved an aggregate F1-score of 93.9%, representing a 1.41% improvement over the 92.46% F1-score from a traditional, non-hierarchical baseline model. These results indicate that a modular, coarse-to-fine cascade can effectively reduce inter-class ambiguity, offering a scalable approach to improving object recognition in complex UAV-based monitoring environments. This work contributes a promising approach to developing more accurate and reliable situational awareness systems. Full article
(This article belongs to the Special Issue Advances in Deep Learning for Drones and Its Applications: 2nd Edition)
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43 pages, 8950 KB  
Article
Development of a Virtual Drone System for Exploring Natural Landscapes and Enhancing Junior High School Students’ Learning of Indigenous Settlement Site Selection
by Pei-Qing Wu, Tsu-Jen Ding, Yu-Jung Wu and Wernhuar Tarng
Drones 2025, 9(11), 742; https://doi.org/10.3390/drones9110742 (registering DOI) - 24 Oct 2025
Abstract
This study combined virtual reality technology with drone aerial imagery of Smangus, a remote Atayal tribe situated 1500 m above sea level in Hsinchu County, Taiwan, to develop a virtual drone system. This study aims to investigate the learning effectiveness and operational experience [...] Read more.
This study combined virtual reality technology with drone aerial imagery of Smangus, a remote Atayal tribe situated 1500 m above sea level in Hsinchu County, Taiwan, to develop a virtual drone system. This study aims to investigate the learning effectiveness and operational experience associated with the application of the virtual drone system for exploring tribal natural landscapes and enhancing junior high school students’ learning of Indigenous settlement site selection. A quasi-experimental design was conducted with two seventh-grade classes from a junior high school in Hsinchu County, Taiwan. The experimental group (n = 43) engaged with the virtual drone system to perform settlement site selection tasks, while the control group (n = 42) learned using traditional materials such as PowerPoint slides and maps. The intervention consisted of two instructional sessions, with data collected via achievement tests, questionnaires, and open-ended feedback. The results indicated that students in the experimental group significantly outperformed the control group in learning outcomes. Positive responses were also observed in learning motivation, cognitive load, and system satisfaction. Students reported that the virtual drone system improved students’ understanding of terrain and enhanced their skills in selecting appropriate sites while increasing their interest and motivation in learning. Moreover, the course incorporated the Atayal people’s migration history and field interview data, enriching its cultural authenticity and contextual relevance. Full article
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26 pages, 6312 KB  
Article
A Novel Telescopic Aerial Manipulator for Installing and Grasping the Insulator Inspection Robot on Power Lines: Design, Control, and Experiment
by Peng Yang, Hao Wang, Xiuwei Huang, Jiawei Gu, Tao Deng and Zonghui Yuan
Drones 2025, 9(11), 741; https://doi.org/10.3390/drones9110741 (registering DOI) - 24 Oct 2025
Abstract
Insulators on power lines require regular maintenance by operators in high-altitude hazardous environments, and the emergence of aerial manipulators provides an efficient and safe support for this scenario. In this study, a lightweight telescopic aerial manipulator system is developed, which can realize the [...] Read more.
Insulators on power lines require regular maintenance by operators in high-altitude hazardous environments, and the emergence of aerial manipulators provides an efficient and safe support for this scenario. In this study, a lightweight telescopic aerial manipulator system is developed, which can realize the installation and retrieval of insulator inspection robots on power lines. The aerial manipulator has three degrees of freedom, including two telescopic scissor mechanisms and one pitch rotation mechanism. Multiple types of cameras and sensors are specifically configured in the structure, and the total mass of the structure is 2.2 kg. Next, the kinematic model, dynamic model, and instantaneous contact force model of the designed aerial manipulator are derived. Then, the hybrid position/force control strategy of the aerial manipulator and the visual detection and estimation algorithm are designed to complete the operation or complete the task. Finally, the lifting external load test, grasp and installation operation test, as well as outdoor flight operation test are carried out. The test results not only quantitatively evaluate the effectiveness of the structural design and control design of the system but also verify that the aerial manipulator can complete the accurate automatic grasp and installation operation of the 3.6 kg target device in outdoor flight. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) Applications in Critical Industrial Sectors)
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21 pages, 1876 KB  
Article
Adaptive Minimum Error Entropy Cubature Kalman Filter in UAV-Integrated Navigation Systems
by Xuhang Liu, Hongli Zhao, Yicheng Liu, Suxing Ling, Xinhanyang Chen, Chenyu Yang and Pei Cao
Drones 2025, 9(11), 740; https://doi.org/10.3390/drones9110740 (registering DOI) - 24 Oct 2025
Abstract
Small unmanned aerial vehicles are now commonly equipped with integrated navigation systems to obtain high-precision navigation parameters. However, affected by the dual impacts of multipath effects and dynamic environmental changes, their state estimation process is vulnerable to interference from measurement outliers, which in [...] Read more.
Small unmanned aerial vehicles are now commonly equipped with integrated navigation systems to obtain high-precision navigation parameters. However, affected by the dual impacts of multipath effects and dynamic environmental changes, their state estimation process is vulnerable to interference from measurement outliers, which in turn leads to the degradation of navigation accuracy and poses a threat to flight safety. To address this issue, this research presents an adaptive minimum error entropy cubature Kalman filter. Firstly, the cubature Kalman filter is introduced to solve the problem of model nonlinear errors; secondly, the cubature Kalman filter based on minimum error entropy is derived to effectively curb the interference that measurement outliers impose on filtering results; finally, a kernel bandwidth adjustment factor is designed, and the kernel bandwidth is estimated adaptively to further improve navigation accuracy. Through numerical simulation experiments, the robustness of the proposed method with respect to measurement outliers is validated; further flight experiment results show that compared with existing related filters, this proposed filter can achieve more accurate navigation and positioning. Full article
(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)
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19 pages, 6326 KB  
Article
MCD-Net: Robust Multi-UAV Cooperative Detection via Secondary Matching and Hybrid Fusion for Occluded Objects
by Huijie Zhou, Zijun Yang, Aitong Ma, Wei Zhang, Hong Zhang and Yifeng Niu
Drones 2025, 9(11), 739; https://doi.org/10.3390/drones9110739 (registering DOI) - 24 Oct 2025
Abstract
Multi-Unmanned Aerial Vehicle (UAV) cooperative detection systems enhance perception by sharing object information from well-perceived UAVs to perception-limited UAVs via cross-view projection. However, such projections often suffer from misalignment due to environmental complexities and dynamic conditions, while existing fusion methods lack the robustness [...] Read more.
Multi-Unmanned Aerial Vehicle (UAV) cooperative detection systems enhance perception by sharing object information from well-perceived UAVs to perception-limited UAVs via cross-view projection. However, such projections often suffer from misalignment due to environmental complexities and dynamic conditions, while existing fusion methods lack the robustness to handle these inaccuracies effectively. To address this issue, we propose a novel Multi-UAV Cooperative Detection Network (MCD-Net), which introduces a secondary matching method and a hybrid fusion strategy to mitigate the adverse effects of projection misalignment. The secondary matching method integrates both background and object features to refine the inter-view projection transformation matrix, improving the reliability of cross-view information supplementation. The hybrid fusion strategy combines (1) Confidence-Based Decision Fusion for initial screening; (2) a Region Consistency Measurement module to evaluate similarity before and after projection, eliminating inconsistent results; and (3) a Vehicle Parts Perception module to detect occluded objects in potential regions, reducing false detections. Additionally, we contribute a dedicated vehicle parts dataset to train the classifier within the perception module. Experimental results demonstrate that MCD-Net achieves significant improvements over single-UAV detection, with higher recall and F-score metrics. Specifically, the recall for occluded objects improves by an average of 9.88%, highlighting the robustness and effectiveness of our approach in challenging scenarios. Full article
(This article belongs to the Section Artificial Intelligence in Drones (AID))
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29 pages, 9276 KB  
Article
A High-Precision Polar Flight Guidance Algorithm for Fixed-Wing UAVs via Heading Prediction
by Junmin Cheng, Guangwen Li, Shaobo Zhai, Jialin Mu and Yiyan Hou
Drones 2025, 9(11), 738; https://doi.org/10.3390/drones9110738 - 23 Oct 2025
Viewed by 119
Abstract
Heading is a crucial navigation parameter for high-precision flight guidance. Since the heading changes rapidly while unmanned aerial vehicles (UAVs) track great ellipse routes in polar regions, it is necessary to implement special guidance algorithms. This article presents a high-precision polar flight guidance [...] Read more.
Heading is a crucial navigation parameter for high-precision flight guidance. Since the heading changes rapidly while unmanned aerial vehicles (UAVs) track great ellipse routes in polar regions, it is necessary to implement special guidance algorithms. This article presents a high-precision polar flight guidance algorithm for fixed-wing UAVs along great ellipse routes based on heading prediction. Specifically, a globally applicable definition of polar grid frame was proposed. On this basis, a novel flight guidance algorithm based on heading prediction was developed. Therein, the calculation method for grid azimuth on great ellipse routes based on the WGS-84 ellipse model was derived in detail, realizing accurate heading estimation and prediction. Subsequently, the predicted grid heading was utilized to tackle the difficulty of heading changes, enabling the UAV to predict and adjust its heading in advance. Moreover, an adaptive predicted lead-time adjustment strategy based on fuzzy decision-making was introduced to improve the prediction accuracy under challenging situations, and an enhanced particle swarm optimization algorithm was employed to determine the hyperparameters in fuzzy rules. To verify the effectiveness of the proposed algorithm, extensive simulations were operated using the Monte Carlo method, and the proposed algorithm demonstrated 3–4 times higher guidance accuracy compared to conventional algorithms. Full article
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24 pages, 41039 KB  
Article
A Novel Design of a Sliding Mode Controller Based on Modified ERL for Enhanced Quadcopter Trajectory Tracking
by Ahmed Abduljabbar Mahmood, Fernando García and Abdulla Al-Kaff
Drones 2025, 9(11), 737; https://doi.org/10.3390/drones9110737 (registering DOI) - 23 Oct 2025
Viewed by 88
Abstract
This paper introduces a new approach to obtain robust tracking performance, disturbance resistance, and input variation resistance, and eliminate chattering phenomena in the control signal and output responses of an unmanned aerial vehicle (UAV) quadcopter with parametric uncertainty. This method involves a modified [...] Read more.
This paper introduces a new approach to obtain robust tracking performance, disturbance resistance, and input variation resistance, and eliminate chattering phenomena in the control signal and output responses of an unmanned aerial vehicle (UAV) quadcopter with parametric uncertainty. This method involves a modified exponential reaching law (ERL) of the sliding mode control (SMC) based on a Gaussian kernel function with a continuous nonlinear Smoother Signum Function (SSF). The smooth continuous signum function is proposed as a substitute for the signum function to prevent the chattering effect caused by the switching sliding surface. The closed-loop system’s stability is ensured according to Lyapunov’s stability theory. Optimal trajectory tracking is attained based on particle swarm optimization (PSO) to select the controller parameters. A comparative analysis with a classical hierarchical SMC based on different ERLs (sign function, saturation function, and SSF) is presented to further substantiate the superior performance of the proposed controller. The outcomes of the simulation prove that the suggested controller has much better effectiveness, unknown disturbance resistance, input variation resistance, and parametric uncertainty than the other controllers, which produce chattering and make the control signal range fall within unrealistic values. Furthermore, the suggested controller outperforms the classical SMC by reducing the tracking integral mean squared errors by 96.154% for roll, 98.535% for pitch, 44.81% for yaw, and 22.8% for altitude under normal flight conditions. It also reduces the tracking mean squared errors by 99.05% for roll, 99.26% for pitch, 40.18% for yaw, and 99.998% for altitude under trajectory tracking flight conditions in the presence of external disturbances. Therefore, the proposed controller can efficiently follow paths in the presence of parameter uncertainties, input variation, and external disturbances. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)
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22 pages, 2224 KB  
Article
Modelling, Design, and Control of a Central Motor Driving Reconfigurable Quadcopter
by Zhuhuan Wu, Ke Huang and Jiaying Zhang
Drones 2025, 9(11), 736; https://doi.org/10.3390/drones9110736 - 23 Oct 2025
Viewed by 121
Abstract
Constrained by fixed frame dimensions, conventional drones usually demonstrate insufficient capabilities to accommodate complex environments. However, the reconfigurable drone can address this limitation through its deformable frame equipped with actuators or passive interaction mechanisms. Nevertheless, these additional components may introduce an excessive weight [...] Read more.
Constrained by fixed frame dimensions, conventional drones usually demonstrate insufficient capabilities to accommodate complex environments. However, the reconfigurable drone can address this limitation through its deformable frame equipped with actuators or passive interaction mechanisms. Nevertheless, these additional components may introduce an excessive weight burden, which conflicts with the lightweight objective in aircraft design. In this work, we propose a novel reconfigurable quadrotor inspired by the swimming morphology of jellyfish, with only one actuator placed at the centre of the frame to achieve significant morphological reconfiguration. In the design of the morphing mechanism, three telescopic sleeves are driven by the actuator, enabling arms’ rotation to achieve a maximum projected area reduction of 55%. The nested design of sleeves ensures a sufficient morphing range while maintaining structural compactness in the fully deployed mode. Furthermore, key structural dimensions are optimized, reducing the central motor load by up to 65% across configurations. After deriving parameter variations during morphing, Proportion–Integration–Differentiation (PID) controllers are implemented and flight simulations are conducted in MATLAB. Results confirm the drone’s sustained controllability during and after reconfiguration, with an “8”-shaped trajectory tracking root mean square error (RMSE) of 0.109 m and successful traversal through long narrow slits, reducing mission duration under certain conditions. Full article
(This article belongs to the Topic Innovation and Inventions in Aerospace and UAV Applications)
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23 pages, 4964 KB  
Article
Online Multi-AUV Trajectory Planning for Underwater Sweep Video Sensing in Unknown and Uneven Seafloor Environments
by Talal S. Almuzaini and Andrey V. Savkin
Drones 2025, 9(11), 735; https://doi.org/10.3390/drones9110735 - 23 Oct 2025
Viewed by 66
Abstract
Autonomous underwater vehicles (AUVs) play a critical role in underwater remote sensing and monitoring applications. This paper addresses the problem of navigating multiple AUVs to perform sweep video sensing of unknown underwater regions over uneven seafloors, where visibility is limited by the conical [...] Read more.
Autonomous underwater vehicles (AUVs) play a critical role in underwater remote sensing and monitoring applications. This paper addresses the problem of navigating multiple AUVs to perform sweep video sensing of unknown underwater regions over uneven seafloors, where visibility is limited by the conical field of view (FoV) of the onboard cameras and by occlusions caused by terrain. Coverage is formulated as a feasibility objective of achieving a prescribed target fraction while respecting vehicle kinematics, actuation limits, terrain clearance, and inter-vehicle spacing constraints. We propose an online, occlusion-aware trajectory planning algorithm that integrates frontier-based goal selection, safe viewing depth estimation with clearance constraints, and model predictive control (MPC) for trajectory tracking. The algorithm adaptively guides a team of AUVs to preserve line of sight (LoS) visibility, maintain safe separation, and ensure sufficient clearance while progressively expanding coverage. The approach is validated through MATLAB simulations on randomly generated 2.5D seafloor surfaces with varying elevation characteristics. Benchmarking against classical lawnmower baselines demonstrates the effectiveness of the proposed method in achieving occlusion-aware coverage in scenarios where fixed-pattern strategies are insufficient. Full article
(This article belongs to the Topic Target Tracking, Guidance, and Navigation for Autonomous Systems, 2nd Edition)
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37 pages, 12943 KB  
Article
Natural Disaster Information System (NDIS) for RPAS Mission Planning
by Robiah Al Wardah and Alexander Braun
Drones 2025, 9(11), 734; https://doi.org/10.3390/drones9110734 - 23 Oct 2025
Viewed by 240
Abstract
Today’s rapidly increasing number and performance of Remotely Piloted Aircraft Systems (RPASs) and sensors allows for an innovative approach in monitoring, mitigating, and responding to natural disasters and risks. At present, there are 100s of different RPAS platforms and smaller and more affordable [...] Read more.
Today’s rapidly increasing number and performance of Remotely Piloted Aircraft Systems (RPASs) and sensors allows for an innovative approach in monitoring, mitigating, and responding to natural disasters and risks. At present, there are 100s of different RPAS platforms and smaller and more affordable payload sensors. As natural disasters pose ever increasing risks to society and the environment, it is imperative that these RPASs are utilized effectively. In order to exploit these advances, this study presents the development and validation of a Natural Disaster Information System (NDIS), a geospatial decision-support framework for RPAS-based natural hazard missions. The system integrates a global geohazard database with specifications of geophysical sensors and RPAS platforms to automate mission planning in a generalized form. NDIS v1.0 uses decision tree algorithms to select suitable sensors and platforms based on hazard type, distance to infrastructure, and survey feasibility. NDIS v2.0 introduces a Random Forest method and a Critical Path Method (CPM) to further optimize task sequencing and mission timing. The latest version, NDIS v3.8.3, implements a staggered decision workflow that sequentially maps hazard type and disaster stage to appropriate survey methods, sensor payloads, and compatible RPAS using rule-based and threshold-based filtering. RPAS selection considers payload capacity and range thresholds, adjusted dynamically by proximity, and ranks candidate platforms using hazard- and sensor-specific endurance criteria. The system is implemented using ArcGIS Pro 3.4.0, ArcGIS Experience Builder (2025 cloud release), and Azure Web App Services (Python 3.10 runtime). NDIS supports both batch processing and interactive real-time queries through a web-based user interface. Additional features include a statistical overview dashboard to help users interpret dataset distribution, and a crowdsourced input module that enables community-contributed hazard data via ArcGIS Survey123. NDIS is presented and validated in, for example, applications related to volcanic hazards in Indonesia. These capabilities make NDIS a scalable, adaptable, and operationally meaningful tool for multi-hazard monitoring and remote sensing mission planning. Full article
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25 pages, 739 KB  
Article
Cooperative Task Allocation for Unmanned Aerial Vehicle Swarm Using Multi-Objective Multi-Population Self-Adaptive Ant Lion Optimizer
by Chengze Li, Gengsong Li, Yi Liu, Qibin Zheng, Guoli Yang, Kun Liu and Xingchun Diao
Drones 2025, 9(11), 733; https://doi.org/10.3390/drones9110733 - 23 Oct 2025
Viewed by 149
Abstract
The rational allocation of tasks is a critical issue in enhancing the mission execution capability of unmanned aerial vehicle (UAV) swarms, which is difficult to solve exactly in polynomial time. Evolutionary-algorithm-based approaches are among the popular methods for addressing this problem. However, existing [...] Read more.
The rational allocation of tasks is a critical issue in enhancing the mission execution capability of unmanned aerial vehicle (UAV) swarms, which is difficult to solve exactly in polynomial time. Evolutionary-algorithm-based approaches are among the popular methods for addressing this problem. However, existing methods often suffer from insufficiently rigorous constraint settings and a focus on single-objective optimization. To address these limitations, this paper considers multiple types of constraints—including temporal constraints, time window constraints, and task integrity constraints—and establishes a model with optimization objectives comprising task reward, task execution cost, and task execution time. A multi-objective multi-population self-adaptive ant lion optimizer (MMSALO) is proposed to solve the problem. In MMSALO, a sparsity-based selection mechanism replaces roulette wheel selection, effectively enhancing the global search capability. A random boundary strategy is adopted to increase the randomness and diversity of ant movement around antlions, thereby improving population diversity. An adaptive position update strategy is employed to strengthen exploration in the early stages and exploitation in the later stages of the algorithm. Additionally, a preference-based elite selection mechanism is introduced to enhance optimization performance and improve the distribution of solutions. Finally, to handle complex multiple constraints, a double-layer encoding mechanism and an adaptive penalty strategy are implemented. Simulation experiments were conducted to validate the proposed algorithm. The results demonstrate that MMSALO exhibits superior performance in solving multi-task, multi-constraint task-allocation problems for UAV swarms. Full article
(This article belongs to the Section Artificial Intelligence in Drones (AID))
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24 pages, 3366 KB  
Article
Study of the Optimal YOLO Visual Detector Model for Enhancing UAV Detection and Classification in Optoelectronic Channels of Sensor Fusion Systems
by Ildar Kurmashev, Vladislav Semenyuk, Alberto Lupidi, Dmitriy Alyoshin, Liliya Kurmasheva and Alessandro Cantelli-Forti
Drones 2025, 9(11), 732; https://doi.org/10.3390/drones9110732 - 23 Oct 2025
Viewed by 342
Abstract
The rapid spread of unmanned aerial vehicles (UAVs) has created new challenges for airspace security, as drones are increasingly used for surveillance, smuggling, and potentially for attacks near critical infrastructure. A key difficulty lies in reliably distinguishing UAVs from visually similar birds in [...] Read more.
The rapid spread of unmanned aerial vehicles (UAVs) has created new challenges for airspace security, as drones are increasingly used for surveillance, smuggling, and potentially for attacks near critical infrastructure. A key difficulty lies in reliably distinguishing UAVs from visually similar birds in electro-optical surveillance channels, where complex backgrounds and visual noise often increase false alarms. To address this, we investigated recent YOLO architectures and developed an enhanced model named YOLOv12-ADBC, incorporating an adaptive hierarchical feature integration mechanism to strengthen multi-scale spatial fusion. This architectural refinement improves sensitivity to subtle inter-class differences between drones and birds. A dedicated dataset of 7291 images was used to train and evaluate five YOLO versions (v8–v12), together with the proposed YOLOv12-ADBC. Comparative experiments demonstrated that YOLOv12-ADBC achieved the best overall performance, with precision = 0.892, recall = 0.864, mAP50 = 0.881, mAP50–95 = 0.633, and per-class accuracy reaching 96.4% for drones and 80% for birds. In inference tests on three video sequences simulating realistic monitoring conditions, YOLOv12-ADBC consistently outperformed baselines, achieving a detection accuracy of 92.1–95.5% and confidence levels up to 88.6%, while maintaining real-time processing at 118–135 frames per second (FPS). These results demonstrate that YOLOv12-ADBC not only surpasses previous YOLO models but also offers strong potential as the optical module in multi-sensor fusion frameworks. Its integration with radar, RF, and acoustic channels is expected to further enhance system-level robustness, providing a practical pathway toward reliable UAV detection in modern airspace protection systems. Full article
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38 pages, 1093 KB  
Article
Neural-Guided Adaptive Clustering for UAV-Based User Grouping in 5G/6G Post-Disaster Networks
by Mohammed Sani Adam, Nor Fadzilah Abdullah, Asma Abu-Samah, Oluwatosin Ahmed Amodu and Rosdiadee Nordin
Drones 2025, 9(11), 731; https://doi.org/10.3390/drones9110731 - 22 Oct 2025
Viewed by 212
Abstract
In post-disaster scenarios, Unmanned Aerial Vehicles (UAVs) acting as Mobile Aerial Base Stations (MABSs) offer a flexible means of restoring communication for isolated user equipment (UE) when conventional infrastructure is unavailable. More broadly, clustering is a fundamental tool for organizing spatially distributed entities [...] Read more.
In post-disaster scenarios, Unmanned Aerial Vehicles (UAVs) acting as Mobile Aerial Base Stations (MABSs) offer a flexible means of restoring communication for isolated user equipment (UE) when conventional infrastructure is unavailable. More broadly, clustering is a fundamental tool for organizing spatially distributed entities in wireless, IoT, and sensor networks. However, static algorithms such as Affinity Propagation Clustering (APC) often fail to generalize across diverse environments and user densities. This study introduces a hybrid clustering framework that dynamically selects between APC and density-based clustering (DBSCAN), guided by a neural classifier trained on spatial distribution features. The chosen centroids then seed a Genetic Algorithm (GA) that evolves UAV trajectories under multiple performance indicators, including coverage, capacity, and path efficiency. Simulation results demonstrate that the hybrid clustering approach improves the adaptability and effectiveness of UAV deployments by learning context-aware clustering strategies. Beyond UAV-assisted disaster recovery, the proposed framework illustrates how intelligent clustering selection can enhance performance in heterogeneous, real-time applications such as IoT connectivity, smart city monitoring, and large-scale sensor coordination. Full article
(This article belongs to the Special Issue Advances in UAV Networks Towards 6G)
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19 pages, 15285 KB  
Article
Towards Safer UAV Operations in Urban Air Mobility: 3D Automated Modelling for CFD-Based Microweather Systems
by Enrique Aldao, Gonzalo Veiga-Piñeiro, Pablo Domínguez-Estévez, Elena Martín, Fernando Veiga-López, Gabriel Fontenla-Carrera and Higinio González-Jorge
Drones 2025, 9(11), 730; https://doi.org/10.3390/drones9110730 - 22 Oct 2025
Viewed by 131
Abstract
Turbulence and wind gusts pose significant risks to the safety and efficiency of UAVs (uncrewed aerial vehicles) in urban environments. In these settings, wind dynamics are strongly influenced by interactions with buildings and terrain, giving rise to small-scale phenomena such as vortex shedding [...] Read more.
Turbulence and wind gusts pose significant risks to the safety and efficiency of UAVs (uncrewed aerial vehicles) in urban environments. In these settings, wind dynamics are strongly influenced by interactions with buildings and terrain, giving rise to small-scale phenomena such as vortex shedding and gusts. These wind speed oscillations generate unsteady forces that can destabilise UAV flight, particularly for small vehicles. Additionally, predicting their formation requires high-resolution Computational Fluid Dynamics (CFD) models, as current weather forecasting tools lack the resolution to capture these phenomena. However, such models require 3D representations of study areas with high geometric consistency and detail, which are not available for most cities. To address this issue, this work introduces an automated methodology for urban CFD mesh generation using open-source data. The proposed method generates error-free meshes compatible with OpenFOAM and includes tools for geometry modification, enhancing solver convergence and enabling adjustments to mesh complexity based on computational resources. Using this approach, CFD simulations are conducted for the city of Ourense, followed by an analysis of their impact on UAV operations and the integration of the system into a trajectory optimisation framework. The CFD model is also validated using experimental anemometer measurements. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)
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31 pages, 4749 KB  
Article
Full-Vehicle Experimental Investigation of Propeller Icing on a Hovering Quadcopter
by Hamdi Ercan and Ahmet Dalkın
Drones 2025, 9(11), 729; https://doi.org/10.3390/drones9110729 - 22 Oct 2025
Viewed by 190
Abstract
This study investigated the ice accretion process on unmanned aerial vehicle (UAV) propeller blades rotating under various conditions. The experimental tests were carried out in the cold chamber laboratory, and two typical icing scenarios were applied: rime ice and glaze ice. With high-resolution [...] Read more.
This study investigated the ice accretion process on unmanned aerial vehicle (UAV) propeller blades rotating under various conditions. The experimental tests were carried out in the cold chamber laboratory, and two typical icing scenarios were applied: rime ice and glaze ice. With high-resolution imaging and flight data analysis, the effects of ice formation patterns on UAV performance were studied in detail. The test results revealed different ice accretion characteristics for each condition. In rime ice conditions, the ice layer formed in perfect harmony with the airfoil of the propeller and was less affected by the rotational effects. Glaze ice conditions created complex needle-like ice formations due to the centrifugal force on unfrozen water with the non-dimensional water-loading parameter confirming substantially higher delivered water in glaze (~3:1 ratio relative to rime). The performance loss experienced in the UAV was determined by analysing the motor speed, motor input power and total battery capacity loss data. Averaged over the icing interval, the electrical input power of the affected motors increased by ≈26.4% (front-left) and ≈15.8% (rear-right) in glaze relative to rime. Glaze ice conditions resulted in more severe performance penalties compared to rime ice conditions, leading to greater power loss and the normalised battery state-of-charge fell to 69.85% under glaze and 74.10% under rime conditions. This study examined in detail the icing process occurring on rotating full vehicle UAV propellers and its impact on flight performance and safety. Full article
(This article belongs to the Special Issue Recent Development in Drones Icing)
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Article
Unmanned Aircraft for Emergency Deliveries Between Hospitals in Madrid: Estimating Time Savings and Predictability
by Emir Ganić, Cristina Barrado, Tatjana Krstić Simić, Jovana Kuljanin and Miguel Baena
Drones 2025, 9(11), 728; https://doi.org/10.3390/drones9110728 - 22 Oct 2025
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Abstract
Unmanned aircraft are increasingly recognized for their potential to enhance healthcare logistics, offering rapid and reliable transport solutions. Among the many envisioned use cases, emergency medical deliveries stand out as particularly promising due to their immediate societal value. This study investigates the potential [...] Read more.
Unmanned aircraft are increasingly recognized for their potential to enhance healthcare logistics, offering rapid and reliable transport solutions. Among the many envisioned use cases, emergency medical deliveries stand out as particularly promising due to their immediate societal value. This study investigates the potential of drones operating under U-space to support hospital-to-hospital emergency deliveries in Madrid. Using the GEMMA tool, we modeled and simulated operations with two drone types along direct routes between four hospitals, resulting in six hospital pairs. Drone travel times were estimated and compared against road transport times obtained from the Google Routes API, incorporating one week of traffic data to capture daily and weekend variability. The results show substantial advantages of aerial transport, with time savings ranging from 2 to 26 min, equivalent to 35–58% compared to road transport. Drones consistently ensured deliveries within 15 min, outperforming regular cars (39%) and ambulances or motorcycles in highly congested periods. Sensitivity analysis confirms their reliability in scenarios with strict time constraints, especially under 15 min. These findings demonstrate that drones reduce travel times and improve predictability, providing a robust evidence base for policymakers and regulators to advance U-space integration in healthcare logistics. Full article
(This article belongs to the Special Issue Urban Air Mobility Solutions: UAVs for Smarter Cities)
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