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Optimising Construction Site Auditing: A Novel Methodology Integrating Ground Drones and Building Information Modelling (BIM) Analysis
Remote Sensing Using Unmanned Aerial Vehicles for Water Stress Detection: A Review Focusing on Specialty Crops
An Integrated Approach for Earth Infrastructure Monitoring Using UAV and ERI: A Systematic Review
A Survey on Unauthorized UAV Threats to Smart Farming

Journal Description

Drones

Drones is an international, peer-reviewed, open access journal published monthly online by MDPI. The journal focuses on the design and applications of drones, including unmanned aerial vehicles (UAVs), Unmanned Aircraft Systems (UASs), and Remotely Piloted Aircraft Systems (RPASs), etc. Likewise, contributions based on unmanned marine/water/underwater drones, unmanned ground vehicles, fully autonomous driving, and space drones are also welcome.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High visibility: indexed within Scopus, SCIE (Web of Science), Inspec, Ei Compendex and other databases.
Journal Rank: JCR - Q1 (Remote Sensing) / CiteScore - Q1 (Aerospace Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.1 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 4.8 (2024); 5-Year Impact Factor: 5.0 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2504-446X

Latest Articles

33 pages, 15773 KiB

Open AccessArticle

Surface Change and Stability Analysis in Open-Pit Mines Using UAV Photogrammetric Data and Geospatial Analysis

by Abdurahman Yasin Yiğit and Halil İbrahim Şenol

Drones 2025, 9(7), 472; https://doi.org/10.3390/drones9070472 - 2 Jul 2025

Significant morphological transformations resulting from open-pit mining activities always present major problems with site safety and slope stability. This study investigates an active marble quarry in Dinar, Türkiye by combining geospatial analysis and photogrammetry based on unmanned aerial vehicles (UAV). Acquired in 2024 [...] Read more.

Significant morphological transformations resulting from open-pit mining activities always present major problems with site safety and slope stability. This study investigates an active marble quarry in Dinar, Türkiye by combining geospatial analysis and photogrammetry based on unmanned aerial vehicles (UAV). Acquired in 2024 and 2025, high-resolution images were combined with dense point clouds produced by Structure from Motion (SfM) methods. Iterative Closest Point (ICP) registration (RMSE = 2.09 cm) and Multiscale Model-to-Model Cloud Comparison (M3C2) analysis was used to quantify the surface changes. The study found a volumetric increase of 7744.04 m³ in the dump zones accompanied by an excavation loss of 8359.72 m³, so producing a net difference of almost 615.68 m³. Surface risk factors were evaluated holistically using a variety of morphometric criteria. These measures covered surface variation in several respects: their degree of homogeneity, presence of any unevenness or texture, verticality, planarity, and linearity. Surface variation > 0.20, roughness > 0.15, and verticality > 0.25 help one to identify zones of increased instability. Point cloud modeling derived from UAVs and GIS-based spatial analysis were integrated to show that morphological anomalies are spatially correlated with possible failure zones. Full article

(This article belongs to the Special Issue UAVs for Photogrammetry, 3D Modeling, Obtrusive Light and Sky Glow Measurements)

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26 pages, 26505 KiB

Open AccessArticle

Multi-UAV Trajectory Planning Based on a Two-Layer Algorithm Under Four-Dimensional Constraints

by Yong Yang, Yujie Fu, Runpeng Xin, Weiqi Feng and Kaijun Xu

Drones 2025, 9(7), 471; https://doi.org/10.3390/drones9070471 - 1 Jul 2025

With the rapid development of the low-altitude economy and smart logistics, unmanned aerial vehicles (UAVs), as core low-altitude platforms, have been widely applied in urban delivery, emergency rescue, and other fields. Although path planning in complex environments has become a research hotspot, optimization [...] Read more.

With the rapid development of the low-altitude economy and smart logistics, unmanned aerial vehicles (UAVs), as core low-altitude platforms, have been widely applied in urban delivery, emergency rescue, and other fields. Although path planning in complex environments has become a research hotspot, optimization and scheduling of UAVs under time window constraints and task assignments remain insufficiently studied. To address this issue, this paper proposes an improved algorithmic framework based on a two-layer structure to enhance the intelligence and coordination efficiency of multi-UAV path planning. In the lower layer path planning stage, considering the limitations of the whale optimization algorithm (WOA), such as slow convergence, low precision, and susceptibility to local optima, this study integrates a backward learning mechanism, nonlinear convergence factor, random number generation strategy, and genetic algorithm principle to construct an improved IWOA. These enhancements significantly strengthen the global search capability and convergence performance of the algorithm. For upper layer task assignment, the improved ALNS (IALNS) addresses local optima issues in complex constraints. It integrates K-means clustering for initialization and a simulated annealing mechanism, improving scheduling rationality and solution efficiency. Through the coordination between the upper and lower layers, the overall solution flexibility is improved. Experimental results demonstrate that the proposed IALNS-IWOA two-layer method outperforms the conventional IALNS-WOA approach by 7.30% in solution quality and 7.36% in environmental adaptability, effectively improving the overall performance of UAV trajectory planning. Full article

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20 pages, 110802 KiB

Open AccessArticle

Toward High-Resolution UAV Imagery Open-Vocabulary Semantic Segmentation

by Zimo Chen, Yuxiang Xie and Yingmei Wei

Drones 2025, 9(7), 470; https://doi.org/10.3390/drones9070470 - 1 Jul 2025

Unmanned Aerial Vehicle (UAV) image semantic segmentation faces challenges in recognizing novel categories due to closed-set training paradigms and the high cost of annotation. While open-vocabulary semantic segmentation (OVSS) leverages vision-language models like CLIP to enable flexible class recognition, existing methods are limited [...] Read more.

Unmanned Aerial Vehicle (UAV) image semantic segmentation faces challenges in recognizing novel categories due to closed-set training paradigms and the high cost of annotation. While open-vocabulary semantic segmentation (OVSS) leverages vision-language models like CLIP to enable flexible class recognition, existing methods are limited to low-resolution images, hindering their applicability to high-resolution UAV data. Current adaptations—downsampling, cropping, or modifying CLIP—compromise either detail preservation, global context, or computational efficiency. To address these limitations, we propose HR-Seg, the first high-resolution OVSS framework for UAV imagery, which effectively integrates global context from downsampled images with local details from cropped sub-images through a novel cost-volume architecture. We introduce a detail-enhanced encoder with multi-scale embedding and a detail-aware decoder for progressive mask refinement, specifically designed to handle objects of varying sizes in aerial imagery. We evaluated existing OVSS methods alongside HR-Seg, training on the VDD dataset and testing across three benchmarks: VDD, UDD, and UAVid. HR-Seg achieved superior performance with mIoU scores of 89.38, 73.67, and 55.23, respectively, outperforming all compared state-of-the-art OVSS approaches. These results demonstrate HR-Seg’s exceptional capability in processing high-resolution UAV imagery. Full article

(This article belongs to the Special Issue Visual Language Models and Large Language Models for Unmanned Aerial Vehicles)

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24 pages, 8079 KiB

Open AccessArticle

Enhancing the Scale Adaptation of Global Trackers for Infrared UAV Tracking

by Zicheng Feng, Wenlong Zhang, Erting Pan, Donghui Liu and Qifeng Yu

Drones 2025, 9(7), 469; https://doi.org/10.3390/drones9070469 - 1 Jul 2025

Tracking unmanned aerial vehicles (UAVs) in infrared video is an essential technology for the anti-UAV task. Given frequent UAV target disappearances caused by occlusion or moving out of view, global trackers, which have the unique ability to recapture targets, are widely used in [...] Read more.

Tracking unmanned aerial vehicles (UAVs) in infrared video is an essential technology for the anti-UAV task. Given frequent UAV target disappearances caused by occlusion or moving out of view, global trackers, which have the unique ability to recapture targets, are widely used in infrared UAV tracking. However, global trackers perform poorly when dealing with large target scale variation because they cannot maintain approximate consistency between target sizes in the template and the search region. To enhance the scale adaptation of global trackers, we propose a plug-and-play scale adaptation enhancement module (SAEM). This can generate a scale adaptation enhancement kernel according to the target size in the previous frame, and then perform implicit scale adaptation enhancement on the extracted target template features. To optimize training, we introduce an auxiliary branch to supervise the learning of SAEM and add Gaussian noise to the input size to improve its robustness. In addition, we propose a one-stage anchor-free global tracker (OSGT), which has a more concise structure than other global trackers to meet the real-time requirement. Extensive experiments on three Anti-UAV Challenge datasets and the Anti-UAV410 dataset demonstrate the superior performance of our method and verify that our proposed SAEM can effectively enhance the scale adaptation of existing global trackers. Full article

(This article belongs to the Special Issue UAV Detection, Classification, and Tracking)

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35 pages, 5917 KiB

Open AccessReview

Trajectory Planning of Unmanned Aerial Vehicles in Complex Environments Based on Intelligent Algorithm

by Zhekun Cheng, Jueying Yang, Jinfeng Sun and Liangyu Zhao

Drones 2025, 9(7), 468; https://doi.org/10.3390/drones9070468 - 1 Jul 2025

In recent years, effective trajectory planning has been developed to promote the extensive application of unmanned aerial vehicles (UAVs) in various domains. However, the actual operation of UAVs in complex environments presents significant challenges to their trajectory planning, particularly in maintaining task reliability [...] Read more.

In recent years, effective trajectory planning has been developed to promote the extensive application of unmanned aerial vehicles (UAVs) in various domains. However, the actual operation of UAVs in complex environments presents significant challenges to their trajectory planning, particularly in maintaining task reliability and ensuring safety. To overcome these challenges, this review presents a comprehensive summary of various trajectory planning techniques currently applied to UAVs based on the emergence of intelligent algorithms, which enhance the adaptability and learning ability of UAVs and offer innovative solutions for their application in complex environments. Firstly, the characteristics of different UAV types, including fixed-wing, multi-rotor UAV, single-rotor UAV, and tilt-rotor UAV, are introduced. Secondly, the key constraints of trajectory planning in complex environments are summarized. Thirdly, the research trend from 2010 to 2024, together with the implementation, advantages, and existing problems of machine learning, evolutionary algorithms, and swarm intelligence, are compared. Based on these algorithms, the related applications of UAVs in complex environments, including transportation, inspection, and other tasks, are summarized. Ultimately, this review provides practical guidance for developing intelligent trajectory planning methods for UAVs to achieve the minimal amount of time spent on computation, efficient dynamic collision avoidance, and superior task completion ability. Full article

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19 pages, 2267 KiB

Open AccessArticle

Closed-Loop Aerial Tracking with Dynamic Detection-Tracking Coordination

by Yang Wang, Heqing Huang, Jiahao He, Dongting Han and Zhiwei Zhao

Drones 2025, 9(7), 467; https://doi.org/10.3390/drones9070467 - 30 Jun 2025

Aerial tracking is an important service for many Unmanned Aerial Vehicle (UAV) applications. Existing work has failed to provide robust solutions when handling target disappearance, viewpoint changes, and tracking drifts in practical scenarios with limited UAV resources. In this paper, we propose a [...] Read more.

Aerial tracking is an important service for many Unmanned Aerial Vehicle (UAV) applications. Existing work has failed to provide robust solutions when handling target disappearance, viewpoint changes, and tracking drifts in practical scenarios with limited UAV resources. In this paper, we propose a closed-loop framework integrating three key components: (1) a lightweight adaptive detection with multi-scale feature extraction, (2) spatiotemporal motion modeling through Kalman-filter-based trajectory prediction, and (3) autonomous decision-making through composite scoring of detection confidence, appearance similarity, and motion consistency. By implementing dynamic detection-tracking coordination with quality-aware feature preservation, our system enables real-time operation through performance-adaptive frequency modulation. Evaluated on VOT-ST2019 and OTB100 benchmarks, the proposed method yields marked improvements over baseline trackers, achieving a 27.94% increase in Expected Average Overlap (EAO) and a 10.39% reduction in failure rates, while sustaining a frame rate of 23–95 FPS on edge hardware. The framework achieves rapid target reacquisition during prolonged occlusion scenarios through optimized protocols, outperforming conventional methods in sustained aerial surveillance tasks. Full article

(This article belongs to the Section Drone Design and Development)

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8 pages, 1164 KiB

Open AccessCommunication

UAVs’ Flight Dynamics Is All You Need for Wind Speed and Direction Measurement in Air

by Sihong Zhu, Tonghui Zhao, Huanji Zhang, Yichao Chen, Dongxu Yang, Yi Liu and Junji Cao

Drones 2025, 9(7), 466; https://doi.org/10.3390/drones9070466 - 30 Jun 2025

The aerial measurement of wind speed and direction is important for the development of the low-altitude economy, meteorology, climate research, and renewable energy systems. Existing UAV-based wind measurements, whether instrument-based or flight-dynamic-based, consistently exhibit bias and significant errors, limiting their reliability for precise [...] Read more.

The aerial measurement of wind speed and direction is important for the development of the low-altitude economy, meteorology, climate research, and renewable energy systems. Existing UAV-based wind measurements, whether instrument-based or flight-dynamic-based, consistently exhibit bias and significant errors, limiting their reliability for precise wind estimation. This study introduces a machine learning (ML) approach to improve the accuracy of the wind speed and direction estimation using UAVs. The proposed method leverages data from sensors onboard UAV platforms, combined with advanced ML algorithms trained on ground-truth measurements obtained through high-resolution LiDAR systems. The experiments reveal that incorporating a 10 s smoothing window yields a root mean square error (RMSE) value of 0.39 m/s for the wind speed (horizontal) and an even lower bias (≤0.069 m/s) when using a 60 s smoothing window, representing a marked improvement over traditional techniques. These results are particularly promising at longer smoothing windows (>50 s), where the ML-based approach achieves superior accuracy compared to LiDAR measurements. The findings underscore the potential of integrating machine learning with UAV-based wind measurement systems to achieve higher precision and reliability in wind characterization. Full article

(This article belongs to the Section Drone Design and Development)

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27 pages, 12374 KiB

Open AccessArticle

A Novel Neural Network-Based Adaptive Formation Control for Cooperative Transportation of an Underwater Payload Using a Fleet of UUVs

by Wen Pang, Daqi Zhu, Mingzhi Chen, Wentao Xu and Bin Wang

Drones 2025, 9(7), 465; https://doi.org/10.3390/drones9070465 - 30 Jun 2025

This article studies the cooperative underwater payload transportation problem for multiple unmanned underwater vehicles (UUVs) operating in a constrained workspace with both static and dynamic obstacles. A novel cooperative formation control algorithm has been presented in this paper for the transportation of a [...] Read more.

This article studies the cooperative underwater payload transportation problem for multiple unmanned underwater vehicles (UUVs) operating in a constrained workspace with both static and dynamic obstacles. A novel cooperative formation control algorithm has been presented in this paper for the transportation of a large payload in underwater scenarios. More precisely, by using the advantages of multi-UUV formation cooperation, based on rigidity graph theory and backstepping technology, the distance between each UUV, as well as the UUV and the transport payload, is controlled to form a three-dimensional rigid structure so that the load remains balanced and stable, to coordinate the transport of objects within the feasible area of the workspace. Moreover, a neural network (NN) is utilized to maintain system stability despite unknown nonlinearities and disturbances in the system dynamics. In addition, based on the interfered fluid flow algorithm, a collision-free motion trajectory was planned for formation systems. The control scheme also performs real-time formation reconfiguration according to the size and position of obstacles in space, thereby enhancing the flexibility of cooperative handling. The uniform ultimate boundedness of the formation distance errors is comprehensively demonstrated by utilizing the Lyapunov stability theory. Finally, the simulation results show that the UUVs can quickly form and maintain the desired formation, transport the payload along the planned trajectory to shuttle in multi-obstacle environments, verify the feasibility of the method proposed in this paper, and achieve the purpose of the collaborative transportation of large underwater payload by multiple UUVs and their targeted delivery. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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18 pages, 1028 KiB

Open AccessArticle

Cooperative Drone and Water Supply Truck Scheduling for Wildfire Fighting Using Deep Reinforcement Learning

by Lin-Yuan Bai, Xin-Ya Chen, Hai-Feng Ling and Yu-Jun Zheng

Drones 2025, 9(7), 464; https://doi.org/10.3390/drones9070464 - 30 Jun 2025

Wildfires often spread rapidly and cause significant casualties and economic losses. Firefighting drones carrying water capsules provide an efficient way for wildfire extinguishing, but their operational capabilities are limited by their payloads. This weakness can be compensated by using ground vehicles to provide [...] Read more.

Wildfires often spread rapidly and cause significant casualties and economic losses. Firefighting drones carrying water capsules provide an efficient way for wildfire extinguishing, but their operational capabilities are limited by their payloads. This weakness can be compensated by using ground vehicles to provide mobile water supply. To this end, this paper presents an optimization problem of scheduling multiple drones and water supply trucks for wildfire fighting, which allocates burning subareas to drones, routes drones to perform fire-extinguishing operations in burning subareas and reload water between every two consecutive operations, and routes trucks to provide timely water supply for drones. To solve the problem within the limited emergency response time, we propose a deep reinforcement learning method, which consists of an encoder for embedding the input instance features and a decoder for generating a solution by iteratively predicting the subarea selection decision through attention. Computational results on test instances constructed upon real-world wilderness areas demonstrate the performance advantages of the proposed method over a collection of heuristic and metaheuristic optimization methods. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)

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20 pages, 935 KiB

Open AccessArticle

MASP: Scalable Graph-Based Planning Towards Multi-UAV Navigation

by Xinyi Yang, Xinting Yang, Chao Yu, Jiayu Chen, Wenbo Ding, Huazhong Yang and Yu Wang

Drones 2025, 9(7), 463; https://doi.org/10.3390/drones9070463 - 28 Jun 2025

This work investigates multi-UAV navigation tasks where multiple drones need to reach initially unassigned goals in a limited time. Reinforcement learning (RL) has recently become a popular approach for such tasks. However, RL struggles with low sample efficiency when directly exploring (nearly) optimal [...] Read more.

This work investigates multi-UAV navigation tasks where multiple drones need to reach initially unassigned goals in a limited time. Reinforcement learning (RL) has recently become a popular approach for such tasks. However, RL struggles with low sample efficiency when directly exploring (nearly) optimal policies in a large exploration space, especially with an increased number of drones (e.g., 10+ drones) or in complex environments (e.g., a 3D quadrotor simulator). To address these challenges, this paper proposes Multi-UAV Scalable Graph-based Planner (MASP), a goal-conditioned hierarchical planner that reduces space complexity by decomposing the large exploration space into multiple goal-conditioned subspaces. MASP consists of a high-level policy that optimizes goal assignment and a low-level policy that promotes goal navigation. MASP uses a graph-based representation and introduces an attention-based mechanism as well as a group division mechanism to enhance cooperation between drones and adaptability to varying team sizes. The results demonstrate that MASP outperforms RL and planning-based baselines in task and execution efficiency. Compared to planning-based competitors, MASP improves task efficiency by over 27.92% in a 3D continuous quadrotor environment with 20 drones. Full article

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21 pages, 7193 KiB

Open AccessArticle

Optimization of Trailing-Edge Unloading for Lambda-Wing UAV Using B-Spline Trailing-Edge Twist Method

by Chengen Yuan, Dongli Ma, Yuhong Jia and Liang Zhang

Drones 2025, 9(7), 462; https://doi.org/10.3390/drones9070462 - 28 Jun 2025

As a commonly used configuration for advanced unmanned aerial vehicles (UAVs), the flying-wing configuration suffers from pitching moment trimming issues due to the lack of horizontal tail. The UAV either needs to unload lift at the trailing edge or needs to increase the [...] Read more.

As a commonly used configuration for advanced unmanned aerial vehicles (UAVs), the flying-wing configuration suffers from pitching moment trimming issues due to the lack of horizontal tail. The UAV either needs to unload lift at the trailing edge or needs to increase the wingtip twist angle at the cost of losing the lift-to-drag ratio. The commonly used methods for solving pitching moment trimming issues are compared and analyzed in this paper, and it is found that the method of trailing-edge twist has advantages under cruising lift coefficient. Furthermore, a trailing-edge twist deformation parameterized model that can deform multiple critical sections is designed with relevant grids. The multi-objective genetic algorithm is used to optimize the parameterized model and obtain the optimized results. Through comparative analysis, it is found that the optimized trailing-edge twist model has an advantage in distributing the pitching moment. By optimizing the distribution of aerodynamic forces and moments, cruise trim is achieved with only a 1.43% cost to the cruise lift-to-drag ratio compared to the initial model. Full article

(This article belongs to the Section Drone Design and Development)

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18 pages, 1803 KiB

Open AccessArticle

Flight Parameters for Spray Deposition Efficiency of Unmanned Aerial Application Systems (UAASs)

by Thiago Caputti, Luan Pereira de Oliveira, Camila Rodrigues, Paulo Cremonez, Wheeler Foshee, Alvin M. Simmons and Andre Luiz Biscaia Ribeiro da Silva

Drones 2025, 9(7), 461; https://doi.org/10.3390/drones9070461 - 27 Jun 2025

The use of unmanned aerial application systems (UAASs) for precision pesticide applications has increased alongside the demand for sustainable agricultural practices. However, limited studies have standardized the necessary flight parameters ensuring the optimal use of UAASs in specialty crops (e.g., fruits and vegetables). [...] Read more.

The use of unmanned aerial application systems (UAASs) for precision pesticide applications has increased alongside the demand for sustainable agricultural practices. However, limited studies have standardized the necessary flight parameters ensuring the optimal use of UAASs in specialty crops (e.g., fruits and vegetables). Thus, the objective of this study was to evaluate the effects of flight speed, droplet size, and application volume on the spray deposition of UAASs, creating guidelines to facilitate their use in specialty crops. Field experiments were conducted in a three-factorial experimental design of three flight speeds (i.e., 4, 7, and 10 m/s), three droplet sizes (i.e., 150, 250, and 350 µm), and two application volumes (i.e., 18.75 and 28.10 L/ha). Spraying droplet parameters (i.e., coverage, droplet density, and droplet spectra, and application uniformity), measured through the effective swath width, were recorded to assess spray deposition efficiency. Flight speed, droplet size, and application volume significantly influenced spray deposition. Treatments with slower flight speeds (4 m/s) and higher application volumes (28.10 L/ha) increased spray coverage, while droplet density was maximized at 4 m/s with the finest droplet size (150 µm), which are desirable characteristics for pesticide applications in specialty crops. Ultimately, the effective swath width and spray uniformity were maximized at a flight speed of 7.93 m/s with a droplet size of 350 µm. These results help optimize UAAS-based pesticide application, increasing efficiency and reducing environmental impact; however, understanding pesticide translocation dynamics (i.e., systemic or contact) on plants is key for growers to determine flight parameters. Full article

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28 pages, 7066 KiB

Open AccessArticle

A Deep Learning-Based Trajectory and Collision Prediction Framework for Safe Urban Air Mobility

by Junghoon Kim, Hyewon Yoon, Seungwon Yoon, Yongmin Kwon and Kyuchul Lee

Drones 2025, 9(7), 460; https://doi.org/10.3390/drones9070460 - 26 Jun 2025

As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and [...] Read more.

As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and long-range dependencies in trajectory data. The model is trained on fifty-six routes generated from a UAM planned commercialization network, sampled at 0.1 s intervals. To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. The trajectory prediction component achieves an RMSE of 0.2172, MAE of 0.1668, and MSE of 0.0524. The collision classification module built on the LSTM–Attention prediction backbone delivers an accuracy of 0.9881. Analysis of attention weight distributions reveals which temporal segments most influence model outputs, enhancing interpretability and guiding future refinements. Moreover, this model is embedded within the Short-Term Conflict Alert component of the Safety Nets module in the UAM traffic management system to provide continuous trajectory prediction and collision risk assessment, supporting proactive traffic control. The system exhibits robust generalizability on unseen scenarios and offers a scalable foundation for enhancing operational safety. Validation currently excludes environmental disturbances such as wind, physical obstacles, and real-world flight logs. Future work will incorporate atmospheric variability, sensor and communication uncertainties, and obstacle detection inputs to advance toward a fully integrated traffic management solution with comprehensive situational awareness. Full article

(This article belongs to the Special Issue Urban Air Mobility Solutions: UAVs for Smarter Cities)

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19 pages, 4258 KiB

Open AccessArticle

Detection and Geolocation of Peat Fires Using Thermal Infrared Cameras on Drones

by Temitope Sam-Odusina, Petrisly Perkasa, Carl Chalmers, Paul Fergus, Steven N. Longmore and Serge A. Wich

Drones 2025, 9(7), 459; https://doi.org/10.3390/drones9070459 - 25 Jun 2025

Peat fires are a major hazard to human and animal health and can negatively impact livelihoods. Once peat fires start to burn, they are difficult to extinguish and can continue to burn for months, destroying biomass and contributing to carbon emissions globally. In [...] Read more.

Peat fires are a major hazard to human and animal health and can negatively impact livelihoods. Once peat fires start to burn, they are difficult to extinguish and can continue to burn for months, destroying biomass and contributing to carbon emissions globally. In areas with limited accessibility and periods of thick haze and fog, these fires are difficult to detect, localize, and tackle. To address this problem, thermal infrared cameras mounted on drones can provide a potential solution since they allow large areas to be surveyed relatively quickly and can detect thermal radiation from fires above and below the peat surface. This paper describes a deep learning pipeline that detects and segments peat fires in thermal images. Controlled peat fires were constructed under varying environmental conditions and thermal images were taken to form a dataset for our pipeline. A semi-automated approach was adopted to label images using Otsu’s adaptive thresholding technique, which significantly reduces the required effort often needed to tag objects in images. The proposed method uses a pre-trained ResNet-50 model as a backbone (encoder) for feature extraction and is augmented with a set of up-sampling layers and skip connections, like the UNet architecture. The experimental results show that the model can achieve an IOU score of 87.6% on an unseen test set of thermal images containing peat fires. In comparison, a MobileNetV2 model trained under the same experimental conditions achieved an IOU score of 57.9%. In addition, the model is robust to false positives, which is indicated by a precision equal to 94.2%. To demonstrate its practical utility, the model was also tested on real peat wildfires, and the results are promising, as indicated by a high IOU score of 90%. Finally, a geolocation algorithm is presented to identify the GNSS location of these fires once they are detected in an image to aid fire-fighting responses. The proposed scheme was built using a web-based platform that performs offline detection and allows peat fires to be geolocated. Full article

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27 pages, 2359 KiB

Open AccessArticle

The Aerodynamically Driven Orientation Control of a Solar Panel on an Aircraft with Numerical Simulation

by Alina Fazylova, Kuanysh Alipbayev, Kenzhebek Myrzabekov, Alisher Aden and Teodor Iliev

Drones 2025, 9(7), 458; https://doi.org/10.3390/drones9070458 - 25 Jun 2025

For unmanned aerial vehicles with long-duration autonomous missions, efficient energy management is critically important. One of the most promising solutions is solar power, the implementation of which requires the continuous orientation tracking of the Sun’s position. This study presents a three-axis active solar [...] Read more.

For unmanned aerial vehicles with long-duration autonomous missions, efficient energy management is critically important. One of the most promising solutions is solar power, the implementation of which requires the continuous orientation tracking of the Sun’s position. This study presents a three-axis active solar tracking system based on a gimbal mount, providing full kinematic control of the panel in space. A mathematical model of orientation is developed using the Earth-Centered Inertial, local geographic frame, and unmanned aerial vehicle body coordinate systems. An aerodynamic analysis is conducted, including a quantitative assessment of drag, lift, and torque on the panel. Based on the obtained characteristics, limiting conditions for the safe operation of the tracker are formulated. An adaptive control algorithm is introduced, minimizing a generalized objective function that accounts for angular deviation, aerodynamic loads, and current energy balance. Numerical simulations are described, demonstrating system stability under various scenarios: turbulence, maneuvers, power limitations, and sensor errors. The results confirm the effectiveness of the proposed approach under real-world operating conditions. Full article

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19 pages, 11127 KiB

Open AccessArticle

Drone State Estimation Based on Frame-to-Frame Template Matching with Optimal Windows

by Seokwon Yeom

Drones 2025, 9(7), 457; https://doi.org/10.3390/drones9070457 - 24 Jun 2025

The flight capability of drones expands the surveillance area and allows drones to be mobile platforms. Therefore, it is important to estimate the kinematic state of drones. In this paper, the kinematic state of a mini drone in flight is estimated based on [...] Read more.

The flight capability of drones expands the surveillance area and allows drones to be mobile platforms. Therefore, it is important to estimate the kinematic state of drones. In this paper, the kinematic state of a mini drone in flight is estimated based on the video captured by its camera. A novel frame-to-frame template-matching technique is proposed. The instantaneous velocity of the drone is measured through image-to-position conversion and frame-to-frame template matching using optimal windows. Multiple templates are defined by their corresponding windows in a frame. The size and location of the windows are obtained by minimizing the sum of the least square errors between the piecewise linear regression model and the nonlinear image-to-position conversion function. The displacement between two consecutive frames is obtained via frame-to-frame template matching that minimizes the sum of normalized squared differences. The kinematic state of the drone is estimated by a Kalman filter based on the velocity computed from the displacement. The Kalman filter is augmented to simultaneously estimate the state and velocity bias of the drone. For faster processing, a zero-order hold scheme is adopted to reuse the measurement. In the experiments, two 150 m long roadways were tested; one road is in an urban environment and the other in a suburban environment. A mini drone starts from a hovering state, reaches top speed, and then continues to fly at a nearly constant speed. The drone captures video 10 times on each road from a height of 40 m at a 60-degree camera tilt angle. It will be shown that the proposed method achieves average distance errors at low meter levels after the flight. Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones, 2nd Edition)

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31 pages, 1576 KiB

Open AccessArticle

Joint Caching and Computation in UAV-Assisted Vehicle Networks via Multi-Agent Deep Reinforcement Learning

by Yuhua Wu, Yuchao Huang, Ziyou Wang and Changming Xu

Drones 2025, 9(7), 456; https://doi.org/10.3390/drones9070456 - 24 Jun 2025

Intelligent Connected Vehicles (ICVs) impose stringent requirements on real-time computational services. However, limited onboard resources and the high latency of remote cloud servers restrict traditional solutions. Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC), which deploys computing and storage resources at the network [...] Read more.

Intelligent Connected Vehicles (ICVs) impose stringent requirements on real-time computational services. However, limited onboard resources and the high latency of remote cloud servers restrict traditional solutions. Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC), which deploys computing and storage resources at the network edge, offers a promising solution. In UAV-assisted vehicular networks, jointly optimizing content and service caching, computation offloading, and UAV trajectories to maximize system performance is a critical challenge. This requires balancing system energy consumption and resource allocation fairness while maximizing cache hit rate and minimizing task latency. To this end, we introduce system efficiency as a unified metric, aiming to maximize overall system performance through joint optimization. This metric comprehensively considers cache hit rate, task computation latency, system energy consumption, and resource allocation fairness. The problem involves discrete decisions (caching, offloading) and continuous variables (UAV trajectories), exhibiting high dynamism and non-convexity, making it challenging for traditional optimization methods. Concurrently, existing multi-agent deep reinforcement learning (MADRL) methods often encounter training instability and convergence issues in such dynamic and non-stationary environments. To address these challenges, this paper proposes a MADRL-based joint optimization approach. We precisely model the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and adopt the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm, which follows the Centralized Training Decentralized Execution (CTDE) paradigm. Our method aims to maximize system efficiency by achieving a judicious balance among multiple performance metrics, such as cache hit rate, task delay, energy consumption, and fairness. Simulation results demonstrate that, compared to various representative baseline methods, the proposed MAPPO algorithm exhibits significant superiority in achieving higher cumulative rewards and an approximately 82% cache hit rate. Full article

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23 pages, 551 KiB

Open AccessReview

Drones and AI-Driven Solutions for Wildlife Monitoring

by Nourdine Aliane

Drones 2025, 9(7), 455; https://doi.org/10.3390/drones9070455 - 24 Jun 2025

Wildlife monitoring has entered a transformative era with the convergence of drone technology and artificial intelligence (AI). Drones provide access to remote and dangerous habitats, while AI unlocks the potential to process vast amounts of wildlife data. This synergy is reshaping wildlife monitoring, [...] Read more.

Wildlife monitoring has entered a transformative era with the convergence of drone technology and artificial intelligence (AI). Drones provide access to remote and dangerous habitats, while AI unlocks the potential to process vast amounts of wildlife data. This synergy is reshaping wildlife monitoring, offering novel solutions to tackle challenges in species identification, animal tracking, anti-poaching, population estimation, and habitat analysis. This paper conducts a comprehensive literature review to examine the recent advancements in drone and AI systems for wildlife monitoring, focusing on two critical dimensions: (1) Methodologies, algorithms, and applications, analyzing the AI techniques employed in wildlife monitoring, including their operational frameworks and real-world implementations. (2) Challenges and opportunities, identifying current limitations, including technical hurdles and regulatory constraints, as well as exploring the untapped potential in drone and AI integration to enhance wildlife monitoring and conservation efforts. By synthesizing these insights, this paper will provide researchers with a structured framework for leveraging drone and AI systems in wildlife monitoring, identifying best practices and outlining actionable pathways for future innovation in the field. Full article

(This article belongs to the Special Issue Drones and Machine Learning as a Complementary Tools in Wildlife Biology Studies)

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25 pages, 6723 KiB

Open AccessArticle

Parametric Modeling and Evaluation of Departure and Arrival Air Routes for Urban Logistics UAVs

by Zhongming Li, Yifei Zhao and Xinhui Ren

Drones 2025, 9(7), 454; https://doi.org/10.3390/drones9070454 - 23 Jun 2025

With the rapid development of the low-altitude economy, the intensive take-offs and landings of Unmanned Aerial Vehicles (UAVs) performing logistics transport tasks in urban areas have introduced significant safety risks. To reduce the likelihood of collisions, logistics operators—such as Meituan, Antwork, and Fengyi—have [...] Read more.

With the rapid development of the low-altitude economy, the intensive take-offs and landings of Unmanned Aerial Vehicles (UAVs) performing logistics transport tasks in urban areas have introduced significant safety risks. To reduce the likelihood of collisions, logistics operators—such as Meituan, Antwork, and Fengyi—have established fixed departure and arrival air routes above vertiports and designed fixed flight air routes between vertiports to guide UAVs to fly along predefined paths. In the complex and constrained low-altitude urban environment, the design of safe and efficient air routes has undoubtedly become a key enabler for successful operations. This research, grounded in both current theoretical research and real-world logistics UAV operations, defines the concept of UAV logistics air routes and presents a comprehensive description of their structure. A parametric model for one-way round-trip logistics air routes is proposed, along with an air route evaluation model and optimization method. Based on this framework, the research identifies four basic configurations that are commonly adopted for one-way round-trip operations. These configurations can be further improved into two optimized configurations with more balanced performance across multiple metrics. Simulation results reveal that Configuration 1 is only suitable for small-scale transport; as the number of delivery tasks increases, delays grow linearly. When the task volume exceeds 100 operations per 30 min, Configurations 2, 3, and 4 reduce average delay by 88.9%, 89.2%, and 93.3%, respectively, compared with Configuration 1. The research also finds that flight speed along segments and the cruise segment capacity have the most significant influence on delays. Properly increasing these two parameters can lead to a 28.4% reduction in the average delay. The two optimized configurations, derived through further refinement, show better trade-offs between average delay and flight time than any of the fundamental configurations. This research not only provides practical guidance for the planning and design of UAV logistics air routes but also lays a methodological foundation for future developments in UAV scheduling and air route network design. Full article

(This article belongs to the Section Innovative Urban Mobility)

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14 pages, 3505 KiB

Open AccessArticle

Small Drone Detection Using Hybrid Beamforming 24 GHz Fully Integrated CMOS Radar

by Kangjie Jin, Seung-Soo Han, Donghyun Baek and Han Lim Lee

Drones 2025, 9(7), 453; https://doi.org/10.3390/drones9070453 - 23 Jun 2025

This paper presents a compact 24 GHz radar with a 4-transmit (4Tx) and 4-receive (4Rx) CMOS radar IC, integrated with a 4 × 4 Tx array and four 1 × 4 receive Rx array antennas, optimized for enhancing small drone detection. By employing [...] Read more.

This paper presents a compact 24 GHz radar with a 4-transmit (4Tx) and 4-receive (4Rx) CMOS radar IC, integrated with a 4 × 4 Tx array and four 1 × 4 receive Rx array antennas, optimized for enhancing small drone detection. By employing the hybrid beamforming technique based on analog beamforming on the transmit side and independent four-channel digital reception, the proposed radar achieves high spatial resolution and robust target tracking. The proposed radar features an elevation scan range of ±45° with an azimuth fan-beam half-power beamwidth (HPBW) of 80° for a comprehensive detection field. Tests with a small drone measuring 20.3 × 15.9 × 7 cm³, positioned at various elevation angles of up to 45° and azimuth angles of up to ±60° at a distance of 4 m from the radar, verified its detection capability and highlighted the radar’s effectiveness in tracking small aerial targets. This architecture emphasizes the advantages of analog beamforming on Tx and multi-channel Rx, addressing the increasing demands for precise drone detection and monitoring in both civilian and defense domains. Full article

(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology)

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