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Volume 9
Issue 4
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Drones, Volume 9, Issue 4 (April 2025) – 93 articles

Cover Story (view full-size image): Traffic congestion and carbon emissions remain pressing challenges in urban mobility. This study explores the integration of UAV (drone)-based monitoring systems and IoT sensors with Large Language Models (LLMs) to optimize traffic flow. Using the SUMO simulator, we conducted experiments in three urban scenarios: Pacific Beach and Coronado in San Diego, and Argüelles in Madrid. A Gemini-2.0-Flash experimental LLM interfaced with the simulation to dynamically adjust vehicle speeds based on real-time traffic conditions. The comparative results indicate that the AI-assisted approach significantly reduces congestion and CO2 emissions compared to a baseline simulation without AI intervention. This research highlights the potential of UAV-enhanced IoT frameworks for adaptive traffic management. View this paper
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27 pages, 9868 KiB  
Article
Attention-Enhanced Contrastive BiLSTM for UAV Intention Recognition Under Information Uncertainty
by Qianru Niu, Luyuan Zhang, Shuangyin Ren, Wei Gao and Chunjiang Wang
Drones 2025, 9(4), 319; https://doi.org/10.3390/drones9040319 - 21 Apr 2025
Cited by 1 | Viewed by 562
Abstract
The widespread deployment of unmanned aerial vehicles (UAVs) in modern warfare has profoundly increased the complexity and dynamic nature of aerial combat. To address the limitations of traditional UAV combat intention recognition methods, which rely on the “complete information” assumption and struggle to [...] Read more.
The widespread deployment of unmanned aerial vehicles (UAVs) in modern warfare has profoundly increased the complexity and dynamic nature of aerial combat. To address the limitations of traditional UAV combat intention recognition methods, which rely on the “complete information” assumption and struggle to adapt effectively to dynamic adversarial environments, this paper proposes a deep learning-based UAV air combat intention recognition model (BLAC). The BLAC model establishes dynamic temporal feature mappings through a bidirectional long short-term memory network (BL) and innovatively incorporates a cross-attention mechanism (A) paired with contrastive learning (C) to improve model performance. To mitigate battlefield information uncertainty, the BLAC model implements cubic spline interpolation for numerical features and proximity-based imputation for non-numerical features, effectively resolving data loss challenges. The experimental results demonstrate that the BLAC model achieves superior intention recognition accuracy compared to mainstream models, maintaining over 91% accuracy even under 30% data loss conditions. These outcomes confirm the robustness and adaptability of the model in dynamic combat environments. This research not only provides an efficient framework for UAV combat intention recognition under information uncertainty but also offers valuable theoretical and practical insights for advancing intelligent command and control systems. Full article
(This article belongs to the Collection Drones for Security and Defense Applications)
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21 pages, 57588 KiB  
Article
WCANet: An Efficient and Lightweight Weight Coordinated Adaptive Detection Network for UAV Inspection of Transmission Line Accessories
by Jiawei Chen, Pengfei Shi, Mengyao Xu, Yuanxue Xin, Xinnan Fan and Jinbo Zhang
Drones 2025, 9(4), 318; https://doi.org/10.3390/drones9040318 - 21 Apr 2025
Viewed by 364
Abstract
Accurate detection and timely management of high-voltage transmission accessories are crucial for ensuring the safe operation of power transmission. Existing network models suffer from issues like low precision in accessory detection, elevated model complexity, and a narrow range of category detection, especially in [...] Read more.
Accurate detection and timely management of high-voltage transmission accessories are crucial for ensuring the safe operation of power transmission. Existing network models suffer from issues like low precision in accessory detection, elevated model complexity, and a narrow range of category detection, especially in UAV-based inspection scenarios. To alleviate the above problems, we propose an innovative Weight Coordinated Adaptive Network (WCANet) in this paper, aiming to improve the efficiency and accuracy of high-voltage transmission accessories detection. The network is designed with a plug-and-play WCA module that can effectively identify dense small targets, retain information in each channel, and reduce computational overheads, while incorporating Sim-AFPN with a skip-connection structure into the network aggregate feature information layer by layer, enhancing the ability to capture key features, and achieving a lightweight network structure. The WIoU loss of bounding box regression (BBR) is to reduce the competitiveness of high-quality anchor boxes and mask the effects of the low-quality examples, thus improving the accuracy of the model. The experimental results show that WCANet has achieved remarkable results in the HVTA, VisDrone2019, and VOC2007 datasets. Compared with other methods, our WCANet achieves highly accurate prediction of high-voltage transmission accessories with fewer parameters and model sizes, availably balancing model performance and complexity. Full article
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17 pages, 2221 KiB  
Article
Event-Triggered-Based Neuroadaptive Bipartite Containment Tracking for Networked Unmanned Aerial Vehicles
by Bowen Chen, Boxian Lin, Meng Li, Zhiqiang Li, Xinyu Zhang, Mengji Shi and Kaiyu Qin
Drones 2025, 9(4), 317; https://doi.org/10.3390/drones9040317 - 21 Apr 2025
Viewed by 555
Abstract
This paper addresses the event-triggered neuroadaptive bipartite containment tracking problem for networked unmanned aerial vehicles (UAVs) subject to resource constraints and actuator failures. A fully distributed event-triggered mechanism is innovatively developed to eliminate dependency on global information while rigorously excluding the Zeno phenomenon [...] Read more.
This paper addresses the event-triggered neuroadaptive bipartite containment tracking problem for networked unmanned aerial vehicles (UAVs) subject to resource constraints and actuator failures. A fully distributed event-triggered mechanism is innovatively developed to eliminate dependency on global information while rigorously excluding the Zeno phenomenon through nonperiodic threshold verification. The proposed mechanism enables neighboring UAVs to exchange information and update control signals exclusively at triggering instants, significantly reducing communication burdens and energy consumption. To handle unknown nonlinear dynamics under resource-limited scenarios, a novel event-triggered neural network (NN) approximation scheme is established where weight updating occurs only during event triggers, effectively decreasing computational resource occupation. Simultaneously, an adaptive robust compensation mechanism is constructed to counteract composite disturbances induced by actuator failures and approximation residuals. Based on the Lyapunov stability analysis, we theoretically prove that all closed-loop signals remain uniformly ultimately bounded while achieving prescribed bipartite containment objectives, where follower UAVs ultimately converge to the dynamic convex hull formed by multiple leaders with cooperative-competitive interactions. Finally, numerical simulations are conducted to validate the effectiveness of the theoretical results. Comparative simulation results show that the proposed event-triggered control scheme reduces the utilization of resources by 95% and 67% compared with the traditional time-triggered and static-triggered mechanisms, respectively. Full article
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20 pages, 544 KiB  
Article
A Quantitative Legal Support System for Transnational Autonomous Vehicle Design
by Zhe Yu, Yiwei Lu, Hao Zhan, Yang Yu and Zongshun Wang
Drones 2025, 9(4), 316; https://doi.org/10.3390/drones9040316 - 20 Apr 2025
Viewed by 380
Abstract
One of the key expectations of AI product manufacturers for their products is the ability to scale to larger markets, especially across legal systems, with fewer prototypes and lower adaptation costs. This paper focuses on the increasingly dynamic legal compliance challenges faced by [...] Read more.
One of the key expectations of AI product manufacturers for their products is the ability to scale to larger markets, especially across legal systems, with fewer prototypes and lower adaptation costs. This paper focuses on the increasingly dynamic legal compliance challenges faced by designers of AI products in achieving this goal. Based on non-monotonic reasoning, we design an automated reasoning tool to help them better understand the legal implications of their designs in a transnational context and, ultimately, adjust the design of AI products more flexibly. This tool supports the quantitative representation of the strength of legal significance to help designers better understand the reasons for their decisions from their own perspective. To illustrate this functionality, a case study on traffic regulations across the UK, France, and Japan demonstrates the system’s ability to resolve legal conflicts—such as driving-side mandates and speed radar detector prohibitions—through quantitative evaluation. Full article
(This article belongs to the Special Issue Unmanned Traffic Management Systems)
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26 pages, 9389 KiB  
Article
Unravelling the Characteristics of Microhabitat Alterations in Floodplain Inundated Areas Based on High-Resolution UAV Imagery and Remote Sensing: A Case Study in Jingjiang, Yangtze River
by Yichen Zheng, Dongshuo Lu, Zongrui Yang and Jianbo Chang
Drones 2025, 9(4), 315; https://doi.org/10.3390/drones9040315 - 18 Apr 2025
Viewed by 529
Abstract
The floodplain of a large river plays a crucial role in the river’s ecosystem and serves as an essential microhabitat for river fish to complete their life history events. Over the past four decades, the floodplain represented by the Jingjiang section in the [...] Read more.
The floodplain of a large river plays a crucial role in the river’s ecosystem and serves as an essential microhabitat for river fish to complete their life history events. Over the past four decades, the floodplain represented by the Jingjiang section in the middle reaches of the Yangtze River has experienced a significant reduction in area, complexity, and diversity of fish microhabitats. This study quantitatively analyzed the dynamic changes and geomorphological structure of the floodplain in the Jingjiang reach (JJR) of the Yangtze River using satellite remote sensing images and high-resolution unmanned aerial vehicle (UAV) optical images. We built an enhanced U-Net model incorporating both the CBAM and SE parallel attention mechanisms to classify these images and identify environmental structural units. The accuracy of the enhanced model was 16.39% higher compared to original U-Net model. At the same time, the improved normalized difference water index (mNDWI), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI) were utilized to extract the flood frequency of the floodplain and analyze the area changes of the floodplain in the JJR. The trend of the flood area in the JJR during the flood season was consistent with the overall trend of flood areas in the flood season, which generally exhibits a downward tendency. In 2022, the floodplain of the JJR underwent substantial anthropogenic disturbances, with 40% of its area comprising anthropogenic environmental units. Compared to historical periods, the impervious surface within the floodplain has increased annually, while ecological units such as riparian forests and trees have gradually diminished or even disappeared, leading to a simplification of structural complexity. These findings provide a critical background and robust data foundation for the protection and restoration of fish habitats and the formulation of strategies for fish population reconstruction in the Yangtze River. Full article
(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))
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21 pages, 2722 KiB  
Article
Coordinated Heterogeneous UAVs for Trajectory Tracking and Irregular Payload Transportation Using Sliding Mode Control
by Umar Farid, Bilal Khan, C. Arshad Mehmood, Muhammad Ali and Yifang Shi
Drones 2025, 9(4), 314; https://doi.org/10.3390/drones9040314 - 17 Apr 2025
Cited by 1 | Viewed by 499
Abstract
Heterogeneous UAVs offer unique advantages in multi-agent systems due to their varying capabilities including (a) different payload capacities, (b) maneuverability, and (c) flight endurance. These properties made them particularly well suited for complex operations such as lifting and transporting irregularly shaped payloads with [...] Read more.
Heterogeneous UAVs offer unique advantages in multi-agent systems due to their varying capabilities including (a) different payload capacities, (b) maneuverability, and (c) flight endurance. These properties made them particularly well suited for complex operations such as lifting and transporting irregularly shaped payloads with even mass distribution. Homogeneous UAV systems may face limitations. By utilizing these capabilities, heterogeneous UAVs enable efficient resource utilization, adaptability to dynamic conditions, and precise coordination for challenging missions. This paper presents a distributed sliding mode control (DSMC) strategy, designed to achieve stable trajectory tracking and synchronized irregular-shaped payload lifting by heterogeneous UAVs. The proposed approach ensures maintaining stability throughout the operation. The framework dynamically adjusts roll, pitch, and yaw angles to achieve precise payload lifting, while maintaining stability during transportation. Additionally, we conduct a comparative analysis between DSMC and PID controller, evaluating their performance in terms of trajectory tracking accuracy, payload stability, and safety distance between the drones. Simulation results demonstrate the effectiveness of the proposed method in minimizing trajectory tracking errors, achieving smooth payload transportation, and ensuring robust performance. The findings highlight the potential of DSMC as a reliable control strategy for multi-UAV coordination in complex payload transportation scenarios. Full article
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26 pages, 37822 KiB  
Article
Drone-Based VNIR–SWIR Hyperspectral Imaging for Environmental Monitoring of a Uranium Legacy Mine Site
by Victor Tolentino, Andres Ortega Lucero, Friederike Koerting, Ekaterina Savinova, Justus Constantin Hildebrand and Steven Micklethwaite
Drones 2025, 9(4), 313; https://doi.org/10.3390/drones9040313 - 17 Apr 2025
Viewed by 1321
Abstract
Growing awareness of the environmental cost of mining operations has led to increased research on monitoring and restoring legacy mine sites. Hyperspectral imaging (HSI) has emerged as a valuable tool in the mining life cycle, including post-mining environment. By detecting variations in crystal [...] Read more.
Growing awareness of the environmental cost of mining operations has led to increased research on monitoring and restoring legacy mine sites. Hyperspectral imaging (HSI) has emerged as a valuable tool in the mining life cycle, including post-mining environment. By detecting variations in crystal structure and physicochemical attributes on the surface of materials, HSI provides insights into site environmental and ecological conditions. Here, we explore the capabilities of drone-based HSI for mapping surface patterns related to contamination dispersal in a legacy uranium-rare earth element mine site. Hyperspectral data across the visible to near-infrared (VNIR) and short-wave infrared (SWIR) wavelength ranges (400–2500 nm) were collected over selected areas of the former Mary Kathleen mine site in Queensland, Australia. Analyses were performed using data-driven (Spectral Angle Mapper—SAM) and knowledge-based (Band Ratios—BRs) spectral processing techniques. SAM identifies contamination patterns and differentiates mineral compositions within visually similar areas. However, its accuracy is limited when mapping specific minerals, as most endmembers represent mineral groups or mixtures. BR highlights reactive surfaces and clay mixtures, reinforcing key patterns identified by SAM. The results indicate that drone-based HSI can capture and distinguish complex surface trends, demonstrating the technology’s potential to enhance the assessment and monitoring of environmental conditions at a mine site. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)
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29 pages, 3403 KiB  
Review
A Review of Physical Layer Security in Aerial–Terrestrial Integrated Internet of Things: Emerging Techniques, Potential Applications, and Future Trends
by Yixin He, Jingwen Wu, Lijun Zhu, Fanghui Huang, Baolei Wang, Deshan Yang and Dawei Wang
Drones 2025, 9(4), 312; https://doi.org/10.3390/drones9040312 - 16 Apr 2025
Viewed by 937
Abstract
The aerial–terrestrial integrated Internet of Things (ATI-IoT) utilizes both aerial platforms (e.g., drones and high-altitude platform stations) and terrestrial networks to establish comprehensive and seamless connectivity across diverse geographical regions. The integration offers significant advantages, including expanded coverage in remote and underserved areas, [...] Read more.
The aerial–terrestrial integrated Internet of Things (ATI-IoT) utilizes both aerial platforms (e.g., drones and high-altitude platform stations) and terrestrial networks to establish comprehensive and seamless connectivity across diverse geographical regions. The integration offers significant advantages, including expanded coverage in remote and underserved areas, enhanced reliability of data transmission, and support for various applications such as emergency communications, vehicular ad hoc networks, and intelligent agriculture. However, due to the inherent openness of wireless channels, ATI-IoT faces potential network threats and attacks, and its security issues cannot be ignored. In this regard, incorporating physical layer security techniques into ATI-IoT is essential to ensure data integrity and confidentiality. Motivated by the aforementioned factors, this review presents the latest advancements in ATI-IoT that facilitate physical layer security. Specifically, we elucidate the endogenous safety and security of wireless communications, upon which we illustrate the current status of aerial–terrestrial integrated architectures along with the functions of their components. Subsequently, various emerging techniques (e.g., intelligent reflective surfaces-assisted networks, device-to-device communications, covert communications, and cooperative transmissions) for ATI-IoT enabling physical layer security are demonstrated and categorized based on their technical principles. Furthermore, given that aerial platforms offer flexible deployment and high re-positioning capabilities, comprehensive discussions on practical applications of ATI-IoT are provided. Finally, several significant unresolved issues pertaining to technical challenges as well as security and sustainability concerns in ATI-IoT enabling physical layer security are outlined. Full article
(This article belongs to the Special Issue Physical-Layer Security in Drone Communications—2nd Edition)
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26 pages, 4680 KiB  
Review
Impact of Drone Disturbances on Wildlife: A Review
by Saadia Afridi, Lucie Laporte-Devylder, Guy Maalouf, Jenna M. Kline, Samuel G. Penny, Kasper Hlebowicz, Dylan Cawthorne and Ulrik Pagh Schultz Lundquist
Drones 2025, 9(4), 311; https://doi.org/10.3390/drones9040311 - 16 Apr 2025
Viewed by 2499
Abstract
Drones are becoming increasingly valuable tools in wildlife studies due to their ability to access remote areas and offer high-resolution information with minimal human interference. Their application is, however, causing concern regarding wildlife disturbance. This review synthesizes the existing literature on how animals [...] Read more.
Drones are becoming increasingly valuable tools in wildlife studies due to their ability to access remote areas and offer high-resolution information with minimal human interference. Their application is, however, causing concern regarding wildlife disturbance. This review synthesizes the existing literature on how animals within terrestrial, aerial, and aquatic environments are impacted by drone disturbance in relation to operational variables, sensory stimulation, species-specific sensitivity, and physiological and behavioral responses. We found that drone altitude, speed, approach distance, and noise levels significantly influence wildlife responses, with some species exhibiting increased vigilance, flight responses, or physiological stress. Environmental context and visual cues are also involved in species detection of drones and disturbance thresholds. Although the short-term response to behavior change has been well documented, long-term consequences of repeated drone exposure remain poorly known. This paper identifies the necessity for continued research into drone–wildlife interactions, with an emphasis on the requirement to minimize disturbance by means of improved flight parameters and technology. Full article
(This article belongs to the Special Issue Drone-Based Wildlife Protection, Monitoring, and Conservation Management：2nd Edition)
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17 pages, 2085 KiB  
Article
Agricultural Drone-Based Variable-Rate N Application for Regulating Wheat Protein Content
by Senlin Guan, Yumi Shimazaki, Kimiyasu Takahashi, Hitoshi Kato, Koichiro Fukami and Shuichi Watanabe
Drones 2025, 9(4), 310; https://doi.org/10.3390/drones9040310 - 16 Apr 2025
Viewed by 1142
Abstract
Implementing a variable-rate application (VRA) of fertilization based on real-time crop growth status reduces costs and enhances work efficiency. However, the technical challenges associated with obtaining accurate growth-distribution maps and applying VRA, particularly with agricultural drones, remain underexplored. In this study, we specifically [...] Read more.
Implementing a variable-rate application (VRA) of fertilization based on real-time crop growth status reduces costs and enhances work efficiency. However, the technical challenges associated with obtaining accurate growth-distribution maps and applying VRA, particularly with agricultural drones, remain underexplored. In this study, we specifically focused on agricultural drone-based VRA fertilization for regulating wheat protein content. First, normalized difference vegetation index (NDVI) distribution maps were obtained using multispectral images captured using a small unmanned aerial vehicle. Subsequently, a prescription map based on the NDVI values was generated to facilitate the implementation of VRA for fertilization. Continuous monitoring of changes in related vegetation indices was conducted from post-topdressing to harvest. Experimental results indicated that selecting targeted experimental survey areas based on different growth conditions can result in accurate predictions of the final yield. However, it is sill ineffective for predicting protein content or protein yield. Additionally, VRA fertilization with less fertilizer in high-NDVI areas and more fertilizer in low-NDVI areas showed no significant difference in final protein content or protein yield compared to conventional uniform fertilization. These findings provide reference data for advancing precision agriculture by addressing field-scale variability for high-quality and uniform production while presenting further research challenges. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture—2nd Edition)
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23 pages, 5887 KiB  
Article
Construction and Application of an Agent-Based Intelligent Operation and Maintenance System for UAV
by Qi Li, Lijie Cui, Qiang Wang, Anxin Guo and Hu Yuan
Drones 2025, 9(4), 309; https://doi.org/10.3390/drones9040309 - 16 Apr 2025
Viewed by 669
Abstract
As a crucial component in the evolution of modern warfare toward digitization and intelligentization, unmanned aerial vehicle (UAV) equipment requires a more precise and efficient operation and maintenance (O&M) system. Based on the Department of Defense Architecture Framework (DoDAF) 2.0, the integration of [...] Read more.
As a crucial component in the evolution of modern warfare toward digitization and intelligentization, unmanned aerial vehicle (UAV) equipment requires a more precise and efficient operation and maintenance (O&M) system. Based on the Department of Defense Architecture Framework (DoDAF) 2.0, the integration of Multi-Agent Systems (MAS) and military simulation technology provides a comprehensive, rational, and feasible theoretical foundation for the construction and validation of an intelligent O&M system for UAV equipment. Firstly, starting from the O&M tasks of UAV equipment in intelligent warfare, this study analyzes the capability requirements for intelligent UAV O&M by following the generation path of scenarios, activities, and capabilities. Three core capabilities are proposed: situational awareness, decision support, and mission execution. Secondly, various O&M tasks are decomposed into behaviors of multiple types of agents, and based on this, an intelligent O&M system for UAV equipment is designed using a “cloud-edge-terminal” distributed architecture. Finally, simulations are conducted to model and validate UAV equipment maintenance tasks. Experimental results demonstrate that the MAS-based UAV O&M system significantly enhances support efficiency, accuracy, and response speed, offering a novel solution for O&M in future UAV operations. Full article
(This article belongs to the Collection Drones for Security and Defense Applications)
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19 pages, 41668 KiB  
Article
Unmanned Aerial Vehicles for the Monitoring of Telecommunication Towers from the Engineering Approach
by Raimondas Pomarnacki, Domantas Bručas, Tomas Jačionis and Darius Plonis
Drones 2025, 9(4), 308; https://doi.org/10.3390/drones9040308 - 16 Apr 2025
Viewed by 524
Abstract
In this article, the authors conducted electromagnetic radiation research on telecommunication base stations using Unmanned Aerial Vehicles (UAVs). Until now, UAVs have only been capable of performing visual inspections, without investigating electrical parameters. The authors suggest a method that involves using a spectrum [...] Read more.
In this article, the authors conducted electromagnetic radiation research on telecommunication base stations using Unmanned Aerial Vehicles (UAVs). Until now, UAVs have only been capable of performing visual inspections, without investigating electrical parameters. The authors suggest a method that involves using a spectrum analyzer and an automated flight with a predefined trajectory around the base stations to conduct electromagnetic radiation research, which is used by telecommunication regulatory organizations. Two different types of UAVs were used in this work: a drone and a fixed-wing aircraft, each with distinct characteristics. The authors successfully designed and tested both types of UAVs under real conditions and performed measurements. A specialized algorithm with software was developed for processing measurement results, which accurately presents the data in a graphical format. Experiments were performed, and the results, at distances of 200 m or further from the telecommunication base tower by changing the altitude by 5 m, were collected. Full article
(This article belongs to the Section Drone Design and Development)
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20 pages, 10021 KiB  
Article
Drone-Enabled 3D Magnetometric Resistivity Imaging for Geological Hazard Detection: A Feasibility Study of Mapping Fracture Zones
by Zhongchang Chen and Dikun Yang
Drones 2025, 9(4), 307; https://doi.org/10.3390/drones9040307 - 15 Apr 2025
Viewed by 673
Abstract
This study proposes a novel drone-based semi-airborne total-field magnetometric resistivity (SA-TFMMR) system for high-resolution detection of conductive fracture zones in geologically hazardous terrains. The system integrates a high-power, low-frequency grounded-wire transmitter with a drone-mounted total-field magnetometer, achieving high survey efficiency and extensive data [...] Read more.
This study proposes a novel drone-based semi-airborne total-field magnetometric resistivity (SA-TFMMR) system for high-resolution detection of conductive fracture zones in geologically hazardous terrains. The system integrates a high-power, low-frequency grounded-wire transmitter with a drone-mounted total-field magnetometer, achieving high survey efficiency and extensive data coverage in mountainous areas. We develop a 3D inversion framework incorporating terrain-adaptive depth weighting, which successfully images a dipping water-saturated fracture zone model beneath a reservoir overburden at a tunnel water gushing accident site. Sensitivity analyses of SA-TFMMR reveal that the effectiveness of detection is controlled by the source-target coupling and the orientation of the target body with respect to the geomagnetic field. Optimal current injection along target strike directions amplifies magnetic anomalies, and orthogonal multi-source configurations can enhance imaging resolution. This UAV-geophysical integration provides a paradigm for pre-disaster monitoring of water-related geohazards. Full article
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22 pages, 1585 KiB  
Article
Distributed Formation Planning for Unmanned Aerial Vehicles
by Zeming Zhao, Xiaozhen Zhang, Hao Fang and Qingkai Yang
Drones 2025, 9(4), 306; https://doi.org/10.3390/drones9040306 - 14 Apr 2025
Cited by 1 | Viewed by 699
Abstract
Formation flying of multiple unmanned aerial vehicles (UAVs) has attracted much attention for its versatility in cooperative tasks. In this paper, a distributed formation planning method is proposed for UAVs. First, we design a path searching algorithm, swarm-A*, which can enhance the cohesion [...] Read more.
Formation flying of multiple unmanned aerial vehicles (UAVs) has attracted much attention for its versatility in cooperative tasks. In this paper, a distributed formation planning method is proposed for UAVs. First, we design a path searching algorithm, swarm-A*, which can enhance the cohesion of a swarm, i.e., preventing the disintegration of the swarm when it encounters an obstacle. Then, after waypoint reallocation, a formation trajectory optimization framework is formulated. Smooth formation trajectories for UAVs to travel safely in obstacle-laden environments can be obtained by solving the optimization problem. Next, a tracking controller based on sliding mode control is designed, ensuring that the UAVs follow the planned formation trajectories under dynamic constraints. Finally, numerical simulations and experiments are conducted to validate the effectiveness of the proposed method. Full article
(This article belongs to the Section Drone Communications)
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25 pages, 1980 KiB  
Review
UAV-Based Soil Water Erosion Monitoring: Current Status and Trends
by Beatriz Macêdo Medeiros, Bernardo Cândido, Paul Andres Jimenez Jimenez, Junior Cesar Avanzi and Marx Leandro Naves Silva
Drones 2025, 9(4), 305; https://doi.org/10.3390/drones9040305 - 14 Apr 2025
Viewed by 1712
Abstract
Soil erosion affects land productivity, water quality, and ecosystem resilience. Traditional monitoring methods are often time-consuming, labor-intensive, and resource-demanding, while unmanned aerial vehicles (UAVs) provide high-resolution, near-real-time data, improving accuracy. This study conducts a bibliometric analysis of UAV-based soil erosion research to explore [...] Read more.
Soil erosion affects land productivity, water quality, and ecosystem resilience. Traditional monitoring methods are often time-consuming, labor-intensive, and resource-demanding, while unmanned aerial vehicles (UAVs) provide high-resolution, near-real-time data, improving accuracy. This study conducts a bibliometric analysis of UAV-based soil erosion research to explore trends, technologies, and challenges. A systematic review of Web of Science and Scopus articles identified 473 relevant studies after filtering for terms that refer to types of soil erosion. Analysis using R’s bibliometrix package shows research is concentrated in Asia, Europe, and the Americas, with 304 publications following a surge. Multi-rotor UAVs with RGB sensors are the most common. Gully erosion is the most studied form of the issue, followed by landslides, rills, and interrill and piping erosion. Significant gaps remain in rill and interrill erosion research. The integration of UAVs with satellite data, laser surveys, and soil properties is limited but crucial. While challenges such as data accuracy and integration persist, UAVs offer cost-effective, near-real-time monitoring capabilities, enabling rapid responses to erosion changes. Future work should focus on multi-source data fusion to enhance conservation strategies. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture—2nd Edition)
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19 pages, 9382 KiB  
Article
Minimum Snap Trajectory Planning and Augmented MPC for Morphing Quadrotor Navigation in Confined Spaces
by Chaojun Qin, Na Zhao, Qiuyu Wang, Yudong Luo and Yantao Shen
Drones 2025, 9(4), 304; https://doi.org/10.3390/drones9040304 - 12 Apr 2025
Viewed by 759
Abstract
Existing studies rarely investigate the dynamic morphology factor on motion planning and control, which is crucial for morphing quadrotors to achieve autonomous flight. Therefore, this paper studies the collaborative optimization of trajectory generation and flight control for the morphing quadrotor with real-time adjustable [...] Read more.
Existing studies rarely investigate the dynamic morphology factor on motion planning and control, which is crucial for morphing quadrotors to achieve autonomous flight. Therefore, this paper studies the collaborative optimization of trajectory generation and flight control for the morphing quadrotor with real-time adjustable arms. In the motion planning layer, an objective function that combines position and morphology is constructed by embedding variable arm length as a decision variable into the conventional minimum snap trajectory generation framework. The generated trajectory not only satisfies the speed and acceleration constraints, but also smoothly passes through the narrow spaces that are difficult for traditional quadrotors to traverse. In the control layer, a constrained augmented model predictive control based on the dynamics of the morphing quadrotors is proposed to follow the generated trajectory with an embedded integrator, which is added by exploiting the differential flat variables to improve the tracking performance. In the numerical studies, a scenario with a corridor was considered to demonstrate the effectiveness of the proposed control strategy to achieve optimal trajectory under multiple constraints. Full article
(This article belongs to the Topic Advanced Technologies and Applications for Unmanned Systems)
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29 pages, 13656 KiB  
Article
Robust FOSMC of a Quadrotor in the Presence of Parameter Uncertainty
by Fahad M. Al-Qahtani, Mujahed Aldhaifallah, Sami El Ferik and Abdul-Wahid A. Saif
Drones 2025, 9(4), 303; https://doi.org/10.3390/drones9040303 - 11 Apr 2025
Viewed by 541
Abstract
This study addresses the problem of attitude and altitude tracking for a quadrotor system in the presence of parameter uncertainties. The goal is to develop a robust control strategy that can handle the nonlinear, strongly coupled dynamics of the quadrotor. To achieve this, [...] Read more.
This study addresses the problem of attitude and altitude tracking for a quadrotor system in the presence of parameter uncertainties. The goal is to develop a robust control strategy that can handle the nonlinear, strongly coupled dynamics of the quadrotor. To achieve this, we propose a fractional-order sliding mode control (FOSMC) scheme, which is specifically designed to improve system performance under uncertain parameters. The FOSMC approach is combined with additional adaptive laws to further enhance the robustness of the control system. We derive the necessary control laws and apply them to the quadrotor’s state-space representation, ensuring that the system remains stable and performs accurately in the presence of uncertainties. Numerical simulations are conducted to evaluate the effectiveness of the proposed control strategy. The results show that the FOSMC-based controller successfully achieves precise tracking of both attitude and altitude, demonstrating significant robustness against parameter variations and disturbances. In conclusion, the proposed FOSMC scheme provides a reliable solution for controlling quadrotor systems in uncertain environments, offering the potential for real-world applications in autonomous UAV operations. Full article
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19 pages, 4975 KiB  
Article
The Dual Impact of Winglets and Serrations on UAV Aerodynamic and Acoustic Performance
by Adam Khalaf and John Kennedy
Drones 2025, 9(4), 302; https://doi.org/10.3390/drones9040302 - 11 Apr 2025
Viewed by 693
Abstract
The rising use of UAVs in various applications has underscored the critical challenge of noise pollution and aerodynamic efficiency. This study explores the dual integration of winglets and serrations on UAV propellers to address these challenges simultaneously. Through a purpose-built test rig and [...] Read more.
The rising use of UAVs in various applications has underscored the critical challenge of noise pollution and aerodynamic efficiency. This study explores the dual integration of winglets and serrations on UAV propellers to address these challenges simultaneously. Through a purpose-built test rig and additive manufacturing techniques, the effects of various flaplet modifications—such as serration span, width, and spacing ratio—on the propeller’s aerodynamic and aeroacoustic performance were evaluated. Results demonstrate simultaneous improvements to thrust and both tonal and broadband noise reduction in a frequency range covering 14 shaft orders. The greatest enhancements for each feature are dependent on the blade geometry. Improvements of up to 9.7% increase in thrust, a 2.7 dB decrease in low-frequency broadband noise, and a 9.1 dB decrease in certain tonal BPF components were achieved compared to the baseline design. This research demonstrates a low-cost empirical approach to UAV propeller design, offering insights into the parametric influences of serrations and winglets. It establishes a framework for the low-cost empirical advancement of UAV propeller technologies, illustrating the substantial gains in operational efficiency and environmental sustainability achievable through iterative design enhancements. Full article
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23 pages, 10074 KiB  
Article
Drone Electric Propulsion System with Hybrid Power Source
by Jenica-Ileana Corcau, Liviu Dinca, Andra-Adelina Cucu and Dmitrii Condrea
Drones 2025, 9(4), 301; https://doi.org/10.3390/drones9040301 - 11 Apr 2025
Viewed by 1539
Abstract
Unmanned aerial vehicles, known today as drones, in the beginning, were small-dimension research models powered by small electric motors fed from electrical batteries. The propulsion system for these drones had to be adapted to the specific applications along their development. Electric and hybrid-electric [...] Read more.
Unmanned aerial vehicles, known today as drones, in the beginning, were small-dimension research models powered by small electric motors fed from electrical batteries. The propulsion system for these drones had to be adapted to the specific applications along their development. Electric and hybrid-electric propulsion drones represent a rapidly developing field in the aerospace industry. Electric drones are those with purely electric propulsion fed from batteries, while hybrid-electric ones have a hybrid propulsion system combining a thermal engine and an electric motor. Another class of hybrid-electric drones includes those with an electric propulsion system fed from fuel cells and batteries. This paper proposes the configuration of an electric propulsion system with a hybrid power source for a transport drone, as well as an analysis of the special electrical components onboard an electric drone, such as batteries, fuel cells, and electric motors. In the final part of the paper, this propulsion system is modeled and analyzed in Matlab/Simulink version 2021a. Design software and simulation tools specifically developed for hybrid-electric drones are essential for ensuring the accuracy and efficiency of these processes. Electric drones have the advantage of zero emissions, but at present, the batteries are still too heavy for aviation applications. By using hydrogen fuel cells as the main power source, it is possible to considerably reduce the power source weight. This is an important advantage of the system proposed in this work. Using hydrogen fuel cells in aircraft and drone propulsion is an important trend in the scientific world. This technology seems to be mature enough to be implemented in aviation. From a technical point of view, these kinds of systems are already feasible. Their usefulness and reliability have to be proven in time. Full article
(This article belongs to the Special Issue Electric-Powered and Hybrid-Powered Unmanned Aerial Vehicle Technology and Applications of Low Altitude Aviation)
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18 pages, 2796 KiB  
Article
Graph-Based Target Association for Multi-Drone Collaborative Perception Under Imperfect Detection Conditions
by Qifan Tan, Xuqi Yang, Cheng Qiu, Wenzhuo Liu, Yize Li, Zhengxia Zou and Jing Huang
Drones 2025, 9(4), 300; https://doi.org/10.3390/drones9040300 - 11 Apr 2025
Cited by 1 | Viewed by 597
Abstract
Multi-drone collaborative perception aims to address single-drone viewpoint limitations. The existing matching and association methods based on visual features and spatial topology rely heavily on detection, making it challenging to associate targets under imperfect detection conditions. To address this issue, a Graph-Based Target [...] Read more.
Multi-drone collaborative perception aims to address single-drone viewpoint limitations. The existing matching and association methods based on visual features and spatial topology rely heavily on detection, making it challenging to associate targets under imperfect detection conditions. To address this issue, a Graph-Based Target Association Network (GTA-Net) is proposed to utilize graph matching to associate the key objects before affine transforming and matching both detected and undetected targets. The Key Object Detection Network (KODN) finds the key object that is more likely to be a True Positive and more important. The Graph Feature Network (GFN) treats the key objects as graph nodes and extracts the graph feature. The Association Module utilizes graph matching to associate the top-k-like matching objects and iterable affine transformation to associate all objects. The experiment results show that our method achieved a 42% accuracy improvement on the public dataset. The ablation experiments under imperfect detection simulation demonstrate robust performance. Full article
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27 pages, 7043 KiB  
Article
An Adaptive Navigation System for an Autonomous Underwater Vehicle Based on Data Transmitted via an Acoustic Channel from a Hydroacoustic Station
by Chang Liu, Vladimir Filaretov, Anton Gubankov and Dmitry Yukhimets
Drones 2025, 9(4), 299; https://doi.org/10.3390/drones9040299 - 11 Apr 2025
Viewed by 550
Abstract
Currently, it is becoming relevant to use cheap autonomous underwater vehicles (AUVs) to perform various underwater operations (environmental monitoring, aquatic protection, search and tracking of underwater biological objects, etc.). At the same time, the main way to reduce the cost of AUVs is [...] Read more.
Currently, it is becoming relevant to use cheap autonomous underwater vehicles (AUVs) to perform various underwater operations (environmental monitoring, aquatic protection, search and tracking of underwater biological objects, etc.). At the same time, the main way to reduce the cost of AUVs is to reduce the number of expensive on-board acoustic sensors. But this leads to a decrease in the accuracy of determining the parameters of the movement of these AUVs and the difficulty of performing missions. To solve this problem, this paper proposes a new method for the synthesis of an AUV navigation system, which recovers unavailable information (due to the absence of an expensive sensor) based on the dynamic model of the AUV and its thruster control signals. At the same time, these estimates are corrected using AUV position information, which is generated by an external hydroacoustic station (HAS) and transmitted via acoustic communication channels. This approach does not require synchronization procedures between the AUV and the HAS, but its accuracy significantly depends on the accuracy of determining the AUV dynamic model and the parameters of underwater currents. Two new approaches are proposed to ensure the accuracy of the navigation system. The first approach is to use the Kalman filter to combine data obtained from different sources with different periods and to take into account delays in receiving AUV position information at the stage of correcting estimates made by the Kalman filter. The second approach is to more accurately estimate the parameters of the AUV model and underwater currents based on data on the trajectory of the AUV obtained from the HAS. The use of these refined parameters of the AUV dynamic model makes it possible to significantly increase the accuracy of the navigation system. The simulation results carried out take into account the characteristics of real on-board sensors of the AUV and the HAS, and acoustic data transmission channels showed the high accuracy of the proposed method of constructing a navigation system, which reduces the cost of creating AUVs. In addition, the proposed algorithm can also be used during the failure of a number of AUV on-board navigation sensors. Full article
(This article belongs to the Special Issue AI-Assisted Control Strategies and Their Applications to the Stabilization, Guidance and Navigation of Drones)
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18 pages, 28391 KiB  
Article
Monitoring Plateau Pika and Revealing the Associated Influencing Mechanisms in the Alpine Grasslands Using Unmanned Aerial Vehicles
by Xinyu Liu, Yu Qin, Yi Sun and Shuhua Yi
Drones 2025, 9(4), 298; https://doi.org/10.3390/drones9040298 - 11 Apr 2025
Cited by 1 | Viewed by 516
Abstract
Plateau pika (Ochotona curzoniae, hereafter pika) is a key species in the alpine grasslands on the Qinghai-Tibetan Plateau (QTP). They are susceptible to the influence of external disturbance and may present great variation, which is important to evaluate their ecological role [...] Read more.
Plateau pika (Ochotona curzoniae, hereafter pika) is a key species in the alpine grasslands on the Qinghai-Tibetan Plateau (QTP). They are susceptible to the influence of external disturbance and may present great variation, which is important to evaluate their ecological role in alpine grasslands. However, our knowledge regarding their interannual variation and the influencing mechanism is still limited due to the lack of long-term observation of pika density. This study aimed to investigate the spatiotemporal variations in pika and the associated key influencing factors by aerial photographing at 181 sites in Gannan Tibetan Autonomous Prefecture in 2016, 2019, and 2022. Our findings showed that: (1) pika primarily distributed in the central and northeastern Maqu County and the southwestern part of Luqu County, and their average density was in a range of 9.87 ha−1 to 14.43 ha−1 from 2016 to 2022; (2) high pika density were found in 1.22 to 3.61 °C for annual mean temperature, 12.86 to 15.06 °C for diurnal temperature range, 3400 to 3800 m for DEM and less than 3° for slope; and (3) pika density showed varied response to interannual changes in mean diurnal range, annual precipitation and precipitation of the driest month in different years. Our results concluded that pika density showed significant spatiotemporal variations, and climate and terrain variables dominantly affected pika density. Given the great interannual fluctuation of climate variables and different responses of pika density to these variables, our results suggested that long-term monitoring of pika is crucial to reveal their real distribution, response mechanism to habitat environment, and role in alpine grasslands. Moreover, unmanned aerial vehicles are cost-effective tools for the long-term monitoring of pika. Full article
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20 pages, 4898 KiB  
Article
IRWT-YOLO: A Background Subtraction-Based Method for Anti-Drone Detection
by Xueqi Cheng, Fan Wang, Xiaopeng Hu, Xinrong Wu and Min Nuo
Drones 2025, 9(4), 297; https://doi.org/10.3390/drones9040297 - 11 Apr 2025
Viewed by 848
Abstract
To effectively separate low-contrast weak drone objects from complex backgrounds, the IRWT-YOLO model is proposed, in which image segmentation algorithms are leveraged to reduce background interference. The model integrates object detection and image segmentation, with segmentation utilized to extract additional image information. Furthermore, [...] Read more.
To effectively separate low-contrast weak drone objects from complex backgrounds, the IRWT-YOLO model is proposed, in which image segmentation algorithms are leveraged to reduce background interference. The model integrates object detection and image segmentation, with segmentation utilized to extract additional image information. Furthermore, to address the challenges of limited receptive fields and weak contextual communication in infrared weak object detection, the DCPPA and RCSCAA modules are introduced. The DCPPA module employs dual convolutions to expand the receptive field and enhance feature extraction for weak drone objects. The RCSCAA module incorporates a contextual attention mechanism to capture long-range dependencies and extract multi-scale texture features. Extensive experiments on three datasets demonstrate the superiority of IRWT-YOLO, with a precision improvement of 15.5% on the SIRSTv2 dataset, a recall improvement of 14.5% on the IRSTD-1k dataset, and a 21.0% improvement in mAP50−95 on the 3rd Anti-UAV dataset compared to YOLOv8. These results highlight the model’s robustness and effectiveness in detecting weak objects under complex infrared conditions. Full article
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21 pages, 11935 KiB  
Article
Tree Species Classification Using UAV-Based RGB Images and Spectral Information on the Loess Plateau, China
by Zhen Li, Shichuan Yu, Quanping Ye, Mei Zhang, Daihao Yin and Zhong Zhao
Drones 2025, 9(4), 296; https://doi.org/10.3390/drones9040296 - 10 Apr 2025
Cited by 1 | Viewed by 754
Abstract
Accurate and efficient tree species classification and mapping is crucial for forest management and conservation, especially on the Loess Plateau, where forest quality urgently needs improvement. This study selected three research sites—Yongshou (YS), Zhengning (ZN), and Yanchang (YC)—on the Loess Plateau and classified [...] Read more.
Accurate and efficient tree species classification and mapping is crucial for forest management and conservation, especially on the Loess Plateau, where forest quality urgently needs improvement. This study selected three research sites—Yongshou (YS), Zhengning (ZN), and Yanchang (YC)—on the Loess Plateau and classified the main forest tree species using RGB images acquired by an unmanned aerial vehicle (UAV). The RGB images were normalized, and vegetation indices (VIs) were extracted. Feature selection was performed using the Boruta algorithm. Two classifiers, Support Vector Machine (SVM) and Random Forest (RF), were used to evaluate the contribution of different input features to classification and their performance differences across regions. The results showed that YC achieved the best classification performance with an overall accuracy (OA) of over 83% and a Kappa value of at least 0.78. The results showed that YC achieved the best classification performance (OA > 83%, Kappa ≥ 0.78), followed by ZN and YS. The addition of VIs significantly improved classification accuracy, particularly in the YS region with imbalanced sample distribution. The OA increased by more than 13.27%, and the Kappa improved by more than 0.17. Feature selection retained most of the advantages of the complete feature set, achieving slightly lower accuracy. Both RF and SVM are effective for tree species classification based on RGB images, with comparable performance (OA difference ≤ 1.5%, Kappa difference < 0.02). This study demonstrates the feasibility of UAV-based RGB images in tree species classification on the Loess Plateau and the great potential of RGBVIs in tree species classification, especially in areas with imbalanced class distributions. It provides a viable approach and methodology for tree species classification based on RGB images. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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27 pages, 1845 KiB  
Article
Offshore Wind Farm Delivery with Autonomous Drones: A Holistic View of System Architecture and Onboard Capabilities
by Simon Schopferer, Philipp Schitz, Mark Spiller, Alexander Donkels, Pranav Nagarajan, Fabian Krause, Sebastian Schirmer, Christoph Torens, Johann C. Dauer, Sebastian Cain and Vincenz Schneider
Drones 2025, 9(4), 295; https://doi.org/10.3390/drones9040295 - 10 Apr 2025
Viewed by 814
Abstract
Maintenance of offshore wind farms requires the transportation of tools and spare parts in close coordination with the deployment of technicians and the cost-intensive shutdown of the wind turbines. In addition to ships and helicopters, drones are envisioned to support the offshore transportation [...] Read more.
Maintenance of offshore wind farms requires the transportation of tools and spare parts in close coordination with the deployment of technicians and the cost-intensive shutdown of the wind turbines. In addition to ships and helicopters, drones are envisioned to support the offshore transportation system in the future. For cost-efficient and scalable offshore drone operations, autonomy is key to minimize the required infrastructure and personnel. In this work, we present a system architecture that integrates the key onboard capabilities for autonomous offshore drone operations: onboard mission and contingency management, en-route trajectory planning, robust flight control, safe landing, communication management, and runtime monitoring. We also present technical solutions for each of these capabilities and discuss their integration and interaction within the autonomy architecture. Furthermore, remaining challenges and the feasibility of autonomous drone operations for offshore wind farm cargo delivery are addressed, contributing to the realization of this vision in the near future. The work presented here summarizes the results of autonomous cargo drone operations within the UDW research project, a joint project between the German Aerospace Center (DLR) and the energy supplier EnBW. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning: 2nd Edition)
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30 pages, 9702 KiB  
Article
SiamCTCA: Cross-Temporal Correlation Aggregation Siamese Network for UAV Tracking
by Qiaochu Wang, Faxue Liu, Bao Zhang, Jinghong Liu, Fang Xu and Yulong Wang
Drones 2025, 9(4), 294; https://doi.org/10.3390/drones9040294 - 10 Apr 2025
Viewed by 607
Abstract
In aerial target-tracking research, complex scenarios place extremely high demands on the precision and robustness of tracking algorithms. Although the existing target-tracking algorithms have achieved good performance in general scenarios, all of them ignore the correlation between contextual information to a certain extent, [...] Read more.
In aerial target-tracking research, complex scenarios place extremely high demands on the precision and robustness of tracking algorithms. Although the existing target-tracking algorithms have achieved good performance in general scenarios, all of them ignore the correlation between contextual information to a certain extent, and the manipulation between features exacerbates the loss of information, leading to the degradation of precision and robustness, especially in the field of UAV target tracking. In response to this, we propose a new lightweight Siamese-based tracker, SiamCTCA. Its innovative cross-temporal aggregated strategy and three feature correlation fusion networks play a key role, in which the Transformer multistage embedding achieves cross-branch information fusion with the help of the intertemporal correlation interactive vision Transformer modules to efficiently integrate different levels of features, and the feed-forward residual multidimensional fusion edge mechanism reduces information loss by introducing residuals to cope with dynamic changes in the search region; and the response significance filter aggregation network suppresses the shallow noise amplification problem of neural networks. The modules are confirmed to be effective after ablation and comparison experiments, indicating that the tracker exhibits excellent tracking performance, and with faster tracking speeds than other trackers, these can be better deployed in the field of a UAV as a platform. Full article
(This article belongs to the Special Issue Detection, Identification and Tracking of UAVs and Drones)
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21 pages, 9976 KiB  
Article
RLRD-YOLO: An Improved YOLOv8 Algorithm for Small Object Detection from an Unmanned Aerial Vehicle (UAV) Perspective
by Hanyun Li, Yi Li, Linsong Xiao, Yunfeng Zhang, Lihua Cao and Di Wu
Drones 2025, 9(4), 293; https://doi.org/10.3390/drones9040293 - 10 Apr 2025
Cited by 1 | Viewed by 2324
Abstract
In Unmanned Aerial Vehicle (UAV) target detection tasks, issues such as missing and erroneous detections frequently occur owing to the small size of the targets and the complexity of the image background. To improve these issues, an improved target detection algorithm named RLRD-YOLO, [...] Read more.
In Unmanned Aerial Vehicle (UAV) target detection tasks, issues such as missing and erroneous detections frequently occur owing to the small size of the targets and the complexity of the image background. To improve these issues, an improved target detection algorithm named RLRD-YOLO, based on You Only Look Once version 8 (YOLOv8), is proposed. First, the backbone network initially integrates the Receptive Field Attention Convolution (RFCBAMConv) Module, which combines the Convolutional Block Attention Module (CBAM) and Receptive Field Attention Convolution (RFAConv). This integration improves the issue of shared attention weights in receptive field features. It also combines attention mechanisms across both channel and spatial dimensions, enhancing the capability of feature extraction. Subsequently, Large-Scale Kernel Attention (LSKA) is integrated to further optimize the Spatial Pyramid Pooling Fast (SPPF) layer. This enhancement employs a large-scale convolutional kernel to improve the capture of intricate small target features and minimize background interference. To enhance feature fusion and effectively integrate low-level details with high-level semantic information, the Reparameterized Generalized Feature Pyramid Network (RepGFPN) replaces the original architecture in the neck network. Additionally, a small-target detection layer is added to enhance the model’s ability to perceive small targets. Finally, the detecting head is replaced with the Dynamic Head, designed to improve the localization accuracy of small targets in complex scenarios by optimizing for Scale Awareness, Spatial Awareness, and Task Awareness. The experimental results showed that RLRD-YOLO outperformed YOLOv8 on the VisDrone2019 dataset, achieving improvements of 12.2% in mAP@0.5 and 8.4% in mAP@0.5:0.95. It also surpassed other widely used object detection methods. Furthermore, experimental results on the HIT-HAV dataset demonstrate that RLRD-YOLO sustains excellent precision in infrared UAV imagery, validating its generalizability across diverse scenarios. Finally, RLRD-YOLO was deployed and validated on the typical airborne platform, Jetson Nano, providing reliable technical support for the improvement of detection algorithms in aerial scenarios and their practical applications. Full article
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22 pages, 4043 KiB  
Article
Prescribed Performance Sliding Mode Fault-Tolerant Tracking Control for Unmanned Morphing Flight Vehicles with Actuator Faults
by Ziqi Ye, Guangbin Cai, Hui Xu, Yiming Shang and Changhua Hu
Drones 2025, 9(4), 292; https://doi.org/10.3390/drones9040292 - 10 Apr 2025
Cited by 1 | Viewed by 467
Abstract
This article focuses on the prescribed performance sliding mode fault-tolerant control problem for an unmanned morphing flight vehicle (MFV) with actuator faults and composite disturbances during wing deformation. Firstly, the longitudinal nonlinear dynamic model of the unmanned MFV is introduced. Then, a control [...] Read more.
This article focuses on the prescribed performance sliding mode fault-tolerant control problem for an unmanned morphing flight vehicle (MFV) with actuator faults and composite disturbances during wing deformation. Firstly, the longitudinal nonlinear dynamic model of the unmanned MFV is introduced. Then, a control framework is proposed by decomposing the integrated dynamic model into attitude and velocity subsystems, effectively simplifying controller architecture and improving fault tolerance. Further, the constrained tracking errors are systematically transformed into unconstrained counterparts via projection operators to facilitate controller design. For each subsystem, a prescribed performance sliding mode fault-tolerant controller is developed, ensuring both transient performance and steady-state tracking accuracy. Finally, the simulation results verify the feasibility and effectiveness of the proposed fault-tolerant control strategy. Full article
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18 pages, 1795 KiB  
Article
Impact of UAV-Derived RTK/PPK Products on Geometric Correction of VHR Satellite Imagery
by Muhammed Enes Atik, Mehmet Arkali and Saziye Ozge Atik
Drones 2025, 9(4), 291; https://doi.org/10.3390/drones9040291 - 9 Apr 2025
Cited by 1 | Viewed by 1004
Abstract
Satellite imagery is a widely used source of spatial information in many applications, such as land use/land cover, object detection, agricultural monitoring, and urban area monitoring. Numerous factors, including projection, tilt angle, scanner, atmospheric conditions, terrain curvature, and fluctuations, can cause satellite images [...] Read more.
Satellite imagery is a widely used source of spatial information in many applications, such as land use/land cover, object detection, agricultural monitoring, and urban area monitoring. Numerous factors, including projection, tilt angle, scanner, atmospheric conditions, terrain curvature, and fluctuations, can cause satellite images to become distorted. Eliminating systematic errors caused by the sensor and platform is a crucial step to obtaining reliable information from satellite images. To utilize satellite images directly in applications requiring high accuracy, the errors in the images should be removed by geometric correction. In this study, geometric correction was applied to the Pléiades 1A (PHR) image using non-parametric methods, and the effects of different transformation models and digital elevation models (DEMs) were investigated. Ground control points (GCPs) were obtained from orthophotos created by the photogrammetric method using precise positioning. The effect of photogrammetric DEMs with various spatial resolutions on geometric correction was investigated. Additionally, the effect of DEMs obtained using the photogrammetric method was compared with those from open-source DEMs, including SRTM, ASTER GDEM, COP30, AW3D30, and NASADEM. Two-dimensional polynomial transformation, the thin plate spline (TPS), and the rational function model (RFM) were applied as transformation methods. Our results showed that a higher-accuracy geometric correction process could be achieved with orthophotos and DEMs created using precise positioning techniques such as RTK and PPK. According to the results obtained, an RMSE of 0.633 m was achieved with RFM using RTK-DEM, while an RMSE of 0.615 m was achieved with RFM using PPK-DEM. Full article
(This article belongs to the Special Issue Applications of UVs in Digital Photogrammetry and Image Processing)
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21 pages, 3597 KiB  
Article
Tracking Fin Whale Morphology with Drone Photogrammetry: Growth Tendencies, Developmental Changes, and Sexual Dimorphism
by Dorottya Mészáros, Beatriu Tort and Eduard Degollada
Drones 2025, 9(4), 290; https://doi.org/10.3390/drones9040290 - 9 Apr 2025
Viewed by 1147
Abstract
Morphological changes during body development measurements are crucial in understanding growth rates, allometric relationships, and sexual dimorphism. Recent advances in drone technology provide a new perspective enabling an indirect, non-invasive morphological assessment of free-ranging cetaceans. In this study, 10 body parameters were measured [...] Read more.
Morphological changes during body development measurements are crucial in understanding growth rates, allometric relationships, and sexual dimorphism. Recent advances in drone technology provide a new perspective enabling an indirect, non-invasive morphological assessment of free-ranging cetaceans. In this study, 10 body parameters were measured and examined with drone-based aerial photogrammetry across 82 individual fin whales (Balaenoptera physalus) along the Catalan coast of the Northwestern Mediterranean Sea, between 2021 and 2023. The growth pattern of each body parameter relative to the total length was determined as negative allometry. The developmental changes depicted that the head region at first decreases proportionally until the animal reaches approximately 14 m in length. Then, it remains constant until 18 m, subsequently followed by a relative increase. The difference in the growth rates among the sexes leads to a proportional shift between females and males approximately between 15 and 17 m in length. Overall, males exhibit a more rapid body elongation, along with moderate development of the other body parameters, while females display the contrary. The morphological parameters reveal insights into the population status dynamics and provide information on the reproductive status. These parameters are critical for the proper conservation and management of the local population of the species. Full article
(This article belongs to the Special Issue Drone Advances in Wildlife Research: 2nd Edition)
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