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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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22 pages, 2224 KB  
Article
Modelling, Design, and Control of a Central Motor Driving Reconfigurable Quadcopter
by Zhuhuan Wu, Ke Huang and Jiaying Zhang
Drones 2025, 9(11), 736; https://doi.org/10.3390/drones9110736 - 23 Oct 2025
Cited by 3 | Viewed by 1655
Abstract
Constrained by fixed frame dimensions, conventional drones usually demonstrate insufficient capabilities to accommodate complex environments. However, the reconfigurable drone can address this limitation through its deformable frame equipped with actuators or passive interaction mechanisms. Nevertheless, these additional components may introduce an excessive weight [...] Read more.
Constrained by fixed frame dimensions, conventional drones usually demonstrate insufficient capabilities to accommodate complex environments. However, the reconfigurable drone can address this limitation through its deformable frame equipped with actuators or passive interaction mechanisms. Nevertheless, these additional components may introduce an excessive weight burden, which conflicts with the lightweight objective in aircraft design. In this work, we propose a novel reconfigurable quadrotor inspired by the swimming morphology of jellyfish, with only one actuator placed at the centre of the frame to achieve significant morphological reconfiguration. In the design of the morphing mechanism, three telescopic sleeves are driven by the actuator, enabling arms’ rotation to achieve a maximum projected area reduction of 55%. The nested design of sleeves ensures a sufficient morphing range while maintaining structural compactness in the fully deployed mode. Furthermore, key structural dimensions are optimized, reducing the central motor load by up to 65% across configurations. After deriving parameter variations during morphing, Proportion–Integration–Differentiation (PID) controllers are implemented and flight simulations are conducted in MATLAB. Results confirm the drone’s sustained controllability during and after reconfiguration, with an “8”-shaped trajectory tracking root mean square error (RMSE) of 0.109 m and successful traversal through long narrow slits, reducing mission duration under certain conditions. Full article
(This article belongs to the Topic Innovation and Inventions in Aerospace and UAV Applications)
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19 pages, 15285 KB  
Article
Towards Safer UAV Operations in Urban Air Mobility: 3D Automated Modelling for CFD-Based Microweather Systems
by Enrique Aldao, Gonzalo Veiga-Piñeiro, Pablo Domínguez-Estévez, Elena Martín, Fernando Veiga-López, Gabriel Fontenla-Carrera and Higinio González-Jorge
Drones 2025, 9(11), 730; https://doi.org/10.3390/drones9110730 - 22 Oct 2025
Cited by 2 | Viewed by 1634
Abstract
Turbulence and wind gusts pose significant risks to the safety and efficiency of UAVs (uncrewed aerial vehicles) in urban environments. In these settings, wind dynamics are strongly influenced by interactions with buildings and terrain, giving rise to small-scale phenomena such as vortex shedding [...] Read more.
Turbulence and wind gusts pose significant risks to the safety and efficiency of UAVs (uncrewed aerial vehicles) in urban environments. In these settings, wind dynamics are strongly influenced by interactions with buildings and terrain, giving rise to small-scale phenomena such as vortex shedding and gusts. These wind speed oscillations generate unsteady forces that can destabilise UAV flight, particularly for small vehicles. Additionally, predicting their formation requires high-resolution Computational Fluid Dynamics (CFD) models, as current weather forecasting tools lack the resolution to capture these phenomena. However, such models require 3D representations of study areas with high geometric consistency and detail, which are not available for most cities. To address this issue, this work introduces an automated methodology for urban CFD mesh generation using open-source data. The proposed method generates error-free meshes compatible with OpenFOAM and includes tools for geometry modification, enhancing solver convergence and enabling adjustments to mesh complexity based on computational resources. Using this approach, CFD simulations are conducted for the city of Ourense, followed by an analysis of their impact on UAV operations and the integration of the system into a trajectory optimisation framework. The CFD model is also validated using experimental anemometer measurements. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)
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23 pages, 3072 KB  
Article
Unmanned Aircraft for Emergency Deliveries Between Hospitals in Madrid: Estimating Time Savings and Predictability
by Emir Ganić, Cristina Barrado, Tatjana Krstić Simić, Jovana Kuljanin and Miguel Baena
Drones 2025, 9(11), 728; https://doi.org/10.3390/drones9110728 - 22 Oct 2025
Cited by 4 | Viewed by 3162
Abstract
Unmanned aircraft are increasingly recognized for their potential to enhance healthcare logistics, offering rapid and reliable transport solutions. Among the many envisioned use cases, emergency medical deliveries stand out as particularly promising due to their immediate societal value. This study investigates the potential [...] Read more.
Unmanned aircraft are increasingly recognized for their potential to enhance healthcare logistics, offering rapid and reliable transport solutions. Among the many envisioned use cases, emergency medical deliveries stand out as particularly promising due to their immediate societal value. This study investigates the potential of drones operating under U-space to support hospital-to-hospital emergency deliveries in Madrid. Using the GEMMA tool, we modeled and simulated operations with two drone types along direct routes between four hospitals, resulting in six hospital pairs. Drone travel times were estimated and compared against road transport times obtained from the Google Routes API, incorporating one week of traffic data to capture daily and weekend variability. The results show substantial advantages of aerial transport, with time savings ranging from 2 to 26 min, equivalent to 35–58% compared to road transport. Drones consistently ensured deliveries within 15 min, outperforming regular cars (39%) and ambulances or motorcycles in highly congested periods. Sensitivity analysis confirms their reliability in scenarios with strict time constraints, especially under 15 min. These findings demonstrate that drones reduce travel times and improve predictability, providing a robust evidence base for policymakers and regulators to advance U-space integration in healthcare logistics. Full article
(This article belongs to the Special Issue Urban Air Mobility Solutions: UAVs for Smarter Cities)
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21 pages, 8957 KB  
Article
Autonomous Navigation of Unmanned Ground Vehicles Based on Micro-Shell Resonator Gyroscope Rotary INS Aided by LDV
by Hangbin Cao, Yuxuan Wu, Longkang Chang, Yunlong Kong, Hongfu Sun, Wenqi Wu, Jiangkun Sun, Yongmeng Zhang, Xiang Xi and Tongqiao Miao
Drones 2025, 9(10), 706; https://doi.org/10.3390/drones9100706 - 13 Oct 2025
Viewed by 3306
Abstract
Micro-Shell Resonator Gyroscopes have obvious SWaP (Size, Weight and Power) advantages and applicable accuracy for the autonomous navigation of Unmanned Ground Vehicles (UGVs), especially under GNSS-denied environments. When the Micro-Shell Resonator Gyroscope Rotary Inertial Navigation System (MSRG–RINS) operates in the whole-angle mode, its [...] Read more.
Micro-Shell Resonator Gyroscopes have obvious SWaP (Size, Weight and Power) advantages and applicable accuracy for the autonomous navigation of Unmanned Ground Vehicles (UGVs), especially under GNSS-denied environments. When the Micro-Shell Resonator Gyroscope Rotary Inertial Navigation System (MSRG–RINS) operates in the whole-angle mode, its bias varies as an even-harmonic function of the pattern angle, which leads to difficulty in estimating and compensating the bias based on the MSRG in the process of attitude measurement. In this paper, an attitude measurement method based on virtual rotation self-calibration and rotary modulation is proposed for the MSRG–RINS to address this problem. The method utilizes the characteristics of the two operating modes of the MSRG, the force-rebalanced mode and whole-angle mode, to perform virtual rotation self-calibration, thereby eliminating the characteristic bias of the MSRG. In addition, the reciprocating rotary modulation method is used to suppress the residual bias of the MSRG. Furthermore, the magnetometer-aided initial alignment of the MSRG–RINS is carried out and the state-transformation extended Kalman filter is adopted to solve the large misalignment-angle problem under magnetometer assistance so as to enhance the rapidity and accuracy of initial attitude acquisition. Results from real-world experiments substantiated that the proposed method can effectively suppress the influence of MSRG’s bias on attitude measurement, thereby achieving high-precision autonomous navigation in GNSS-denied environments. In the 1 h, 3.7 km, long-range in-vehicle autonomous navigation experiments, the MSRG–RINS, integrated with a Laser Doppler Velocimetry (LDV), attained a heading accuracy of 0.35° (RMS), a horizontal positioning error of 4.9 m (RMS), and a distance-traveled accuracy of 0.24% D. Full article
(This article belongs to the Topic Navigation and Positioning System: Opportunities and Obstacles)
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17 pages, 2705 KB  
Review
Review of Hybrid Aerial Underwater Vehicle: Potential Applications in the Field of Underwater Marine Optics
by Hongyu Qi, Shuibo Hu, Jiasheng Zhang and Guofeng Wu
Drones 2025, 9(10), 667; https://doi.org/10.3390/drones9100667 - 23 Sep 2025
Cited by 4 | Viewed by 4967
Abstract
Hybrid Aerial Underwater Vehicle (HAUV) is a new type of unmanned system that can operate both in air and water, and complete underwater and air operations tasks by carrying corresponding sensors. Owing to this dual-medium operational capability, HAUVs hold significant promise for coordinated [...] Read more.
Hybrid Aerial Underwater Vehicle (HAUV) is a new type of unmanned system that can operate both in air and water, and complete underwater and air operations tasks by carrying corresponding sensors. Owing to this dual-medium operational capability, HAUVs hold significant promise for coordinated air–sea surveillance and monitoring efforts. Optical methods enable high-resolution sampling across both spatial and temporal scales, offering enhanced contextual information for the interpretation of discrete observational data. In order to evaluate the feasibility of ocean optical profiling systems based on HAUVs, this paper reviews the design features of current HAUV models and summarizes advanced techniques that support their cross-medium mobility. Subsequently, we summarized the types of commercial optical instruments commonly used for underwater observation and compared the field deployment methods. By analyzing the underwater motion performance of HAUVs and the requirements for optical observation platforms, we believe that multi-rotor HAUVs can provide new observation methods for future underwater optical acquisition due to their smooth entry and exit characteristics and the ability to maintain a controlled orientation during underwater operation. Finally, the paper explores prospective applications and outlines key obstacles to be overcome in the advancement of amphibious platforms for ocean optical profiling. Full article
(This article belongs to the Special Issue Drones in Hydrological Research and Management)
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43 pages, 7381 KB  
Review
Mechanisms and Control Strategies for Morphing Structures in Quadrotors: A Review and Future Prospects
by Osman Acar, Eija Honkavaara, Ruxandra Mihaela Botez and Deniz Çınar Bayburt
Drones 2025, 9(9), 663; https://doi.org/10.3390/drones9090663 - 22 Sep 2025
Cited by 4 | Viewed by 5646
Abstract
This review explores recent advancements in morphing structures for Unmanned Ariel Vehicles (UAVs), focusing on mechanical designs and control strategies of quadrotors that enable real-time geometric reconfiguration. Morphing mechanisms, ranging from closed-loop linkages to bioinspired and compliant structures, are evaluated in terms of [...] Read more.
This review explores recent advancements in morphing structures for Unmanned Ariel Vehicles (UAVs), focusing on mechanical designs and control strategies of quadrotors that enable real-time geometric reconfiguration. Morphing mechanisms, ranging from closed-loop linkages to bioinspired and compliant structures, are evaluated in terms of adaptability, actuation simplicity, and flight stability. Control approaches, including model predictive control, reinforcement learning, and sliding mode control, are analyzed for their effectiveness in handling dynamic morphology. The review also highlights key morphing wing concepts such as GNATSpar and Zigzag Wingbox, which enhance aerodynamic efficiency and structural flexibility. A novel concept featuring an inverted slider-crank mechanism (ISCM) is introduced, enabling dual-mode UAV operation for both aerial and terrestrial missions, which is particularly useful in scenarios like wildfire suppression where stability and operation longevity are crucial. This study emphasizes the importance of integrated design approaches that align mechanical transformation with adaptive control. Critical gaps in real-world testing, swarm coordination, and scalable morphing architectures are identified, suggesting future research directions for developing robust, mission-adaptive UAV systems. Full article
(This article belongs to the Special Issue Dynamics Modeling and Conceptual Design of UAVs)
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42 pages, 13345 KB  
Article
UAV Operations and Vertiport Capacity Evaluation with a Mixed-Reality Digital Twin for Future Urban Air Mobility Viability
by Junjie Zhao, Zhang Wen, Krishnakanth Mohanta, Stefan Subasu, Rodolphe Fremond, Yu Su, Ruechuda Kallaka and Antonios Tsourdos
Drones 2025, 9(9), 621; https://doi.org/10.3390/drones9090621 - 3 Sep 2025
Cited by 4 | Viewed by 3630
Abstract
This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off [...] Read more.
This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem. Full article
(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)
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58 pages, 7149 KB  
Review
Secure Communication in Drone Networks: A Comprehensive Survey of Lightweight Encryption and Key Management Techniques
by Sayani Sarkar, Sima Shafaei, Trishtanya S. Jones and Michael W. Totaro
Drones 2025, 9(8), 583; https://doi.org/10.3390/drones9080583 - 18 Aug 2025
Cited by 16 | Viewed by 11994
Abstract
Deployment of Unmanned Aerial Vehicles (UAVs) continues to expand rapidly across a wide range of applications, including environmental monitoring, precision agriculture, and disaster response. Despite their increasing ubiquity, UAVs remain inherently vulnerable to security threats due to resource-constrained hardware, energy limitations, and reliance [...] Read more.
Deployment of Unmanned Aerial Vehicles (UAVs) continues to expand rapidly across a wide range of applications, including environmental monitoring, precision agriculture, and disaster response. Despite their increasing ubiquity, UAVs remain inherently vulnerable to security threats due to resource-constrained hardware, energy limitations, and reliance on open wireless communication channels. These factors render traditional cryptographic solutions impractical, thereby necessitating the development of lightweight, UAV-specific security mechanisms. This review article presents a comprehensive analysis of lightweight encryption techniques and key management strategies designed for energy-efficient and secure UAV communication. Special emphasis is placed on recent cryptographic advancements, including the adoption of the ASCON family of ciphers and the emergence of post-quantum algorithms that can secure UAV networks against future quantum threats. Key management techniques such as blockchain-based decentralized key exchange, Physical Unclonable Function (PUF)-based authentication, and hierarchical clustering schemes are evaluated for their performance and scalability. To ensure comprehensive protection, this review introduces a multilayer security framework addressing vulnerabilities from the physical to the application layer. Comparative analysis of lightweight cryptographic algorithms and multiple key distribution approaches is conducted based on energy consumption, latency, memory usage, and deployment feasibility in dynamic aerial environments. Unlike design- or implementation-focused studies, this work synthesizes existing literature across six interconnected security dimensions to provide an integrative foundation. Our review also identifies key research challenges, including secure and efficient rekeying during flight, resilience to cross-layer attacks, and the need for standardized frameworks supporting post-quantum cryptography in UAV swarms. By highlighting current advancements and research gaps, this study aims to guide future efforts in developing secure communication architectures tailored to the unique operational constraints of UAV networks. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)
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31 pages, 5802 KB  
Review
Exploring the Potential of Autonomous Underwater Vehicles for Microplastic Detection in Marine Environments: A Systematic Review
by Qian Zhong, Neil Bose, Jimin Hwang and Ting Zou
Drones 2025, 9(8), 580; https://doi.org/10.3390/drones9080580 - 15 Aug 2025
Cited by 3 | Viewed by 5637
Abstract
AUVs offer the potential for in situ MP detection at constant, pre-set depths in marine environments. By carrying onboard MP detectors, AUVs can serve as alternatives to traditional methods of sample collection, processing, and analysis, while also addressing the inefficiencies and complexities associated [...] Read more.
AUVs offer the potential for in situ MP detection at constant, pre-set depths in marine environments. By carrying onboard MP detectors, AUVs can serve as alternatives to traditional methods of sample collection, processing, and analysis, while also addressing the inefficiencies and complexities associated with conventional detection procedures. This study conducts a comprehensive review of existing and potential MP detection methods that can be integrated with AUVs for in situ detection. In particular, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review analyzes selected studies on MP detection using AUVs. It finds that real-time, in situ MP detection via AUVs or multi-AUV systems remains underdeveloped. Key challenges include deep-sea communication, sensor integration, and underwater durability. The review highlights the current advances, research gaps, and future directions for AUV-based MP detection technologies. Full article
(This article belongs to the Special Issue Advances in Autonomy of Underwater Vehicles (AUVs))
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21 pages, 3921 KB  
Article
A Unified Transformer Model for Simultaneous Cotton Boll Detection, Pest Damage Segmentation, and Phenological Stage Classification from UAV Imagery
by Sabina Umirzakova, Shakhnoza Muksimova, Abror Shavkatovich Buriboev, Holida Primova and Andrew Jaeyong Choi
Drones 2025, 9(8), 555; https://doi.org/10.3390/drones9080555 - 7 Aug 2025
Cited by 23 | Viewed by 2003
Abstract
The present-day issues related to the cotton-growing industry, namely yield estimation, pest effect, and growth phase diagnostics, call for integrated, scalable monitoring solutions. This write-up reveals Cotton Multitask Learning (CMTL), a transformer-driven multitask framework that launches three major agronomic tasks from UAV pictures [...] Read more.
The present-day issues related to the cotton-growing industry, namely yield estimation, pest effect, and growth phase diagnostics, call for integrated, scalable monitoring solutions. This write-up reveals Cotton Multitask Learning (CMTL), a transformer-driven multitask framework that launches three major agronomic tasks from UAV pictures at one go: boll detection, pest damage segmentation, and phenological stage classification. CMTL does not change separate pipelines, but rather merges these goals using a Cross-Level Multi-Granular Encoder (CLMGE) and a Multitask Self-Distilled Attention Fusion (MSDAF) module that both allow mutual learning across tasks and still keep their specific features. The biologically guided Stage Consistency Loss is the part of the architecture of the network that enables the system to carry out growth stage transitions that occur in reality. We executed CMTL on a tri-source UAV dataset that fused over 2100 labeled images from public and private collections, representing a variety of crop stages and conditions. The model showed its virtues state-of-the-art baselines in all the tasks: setting 0.913 mAP for boll detection, 0.832 IoU for pest segmentation, and 0.936 accuracy for growth stage classification. Additionally, it runs at the fastest speed of performance on edge devices such as NVIDIA Jetson Xavier NX (Manufactured in Shanghai, China), which makes it ideal for deployment. These outcomes evoke CMTL’s promise as a single and productive instrument of aerial crop intelligence in precision cotton agriculture. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture—2nd Edition)
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25 pages, 13994 KB  
Article
A Semi-Autonomous Aerial Platform Enhancing Non-Destructive Tests
by Simone D’Angelo, Salvatore Marcellini, Alessandro De Crescenzo, Michele Marolla, Vincenzo Lippiello and Bruno Siciliano
Drones 2025, 9(8), 516; https://doi.org/10.3390/drones9080516 - 23 Jul 2025
Cited by 2 | Viewed by 2486
Abstract
The use of aerial robots for inspection and maintenance in industrial settings demands high maneuverability, precise control, and reliable measurements. This study explores the development of a fully customized unmanned aerial manipulator (UAM), composed of a tilting drone and an articulated robotic arm, [...] Read more.
The use of aerial robots for inspection and maintenance in industrial settings demands high maneuverability, precise control, and reliable measurements. This study explores the development of a fully customized unmanned aerial manipulator (UAM), composed of a tilting drone and an articulated robotic arm, designed to perform non-destructive in-contact inspections of iron structures. The system is intended to operate in complex and potentially hazardous environments, where autonomous execution is supported by shared-control strategies that include human supervision. A parallel force–impedance control framework is implemented to enable smooth and repeatable contact between a sensor for ultrasonic testing (UT) and the inspected surface. During interaction, the arm applies a controlled push to create a vacuum seal, allowing accurate thickness measurements. The control strategy is validated through repeated trials in both indoor and outdoor scenarios, demonstrating consistency and robustness. The paper also addresses the mechanical and control integration of the complex robotic system, highlighting the challenges and solutions in achieving a responsive and reliable aerial platform. The combination of semi-autonomous control and human-in-the-loop operation significantly improves the effectiveness of inspection tasks in hard-to-reach environments, enhancing both human safety and task performance. Full article
(This article belongs to the Special Issue Unmanned Aerial Manipulation with Physical Interaction)
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22 pages, 6496 KB  
Article
Real-Time Search and Rescue with Drones: A Deep Learning Approach for Small-Object Detection Based on YOLO
by Francesco Ciccone and Alessandro Ceruti
Drones 2025, 9(8), 514; https://doi.org/10.3390/drones9080514 - 22 Jul 2025
Cited by 21 | Viewed by 10562
Abstract
Unmanned aerial vehicles are increasingly used in civil Search and Rescue operations due to their rapid deployment and wide-area coverage capabilities. However, detecting missing persons from aerial imagery remains challenging due to small object sizes, cluttered backgrounds, and limited onboard computational resources, especially [...] Read more.
Unmanned aerial vehicles are increasingly used in civil Search and Rescue operations due to their rapid deployment and wide-area coverage capabilities. However, detecting missing persons from aerial imagery remains challenging due to small object sizes, cluttered backgrounds, and limited onboard computational resources, especially when managed by civil agencies. In this work, we present a comprehensive methodology for optimizing YOLO-based object detection models for real-time Search and Rescue scenarios. A two-stage transfer learning strategy was employed using VisDrone for general aerial object detection and Heridal for Search and Rescue-specific fine-tuning. We explored various architectural modifications, including enhanced feature fusion (FPN, BiFPN, PB-FPN), additional detection heads (P2), and modules such as CBAM, Transformers, and deconvolution, analyzing their impact on performance and computational efficiency. The best-performing configuration (YOLOv5s-PBfpn-Deconv) achieved a mAP@50 of 0.802 on the Heridal dataset while maintaining real-time inference on embedded hardware (Jetson Nano). Further tests at different flight altitudes and explainability analyses using EigenCAM confirmed the robustness and interpretability of the model in real-world conditions. The proposed solution offers a viable framework for deploying lightweight, interpretable AI systems for UAV-based Search and Rescue operations managed by civil protection authorities. Limitations and future directions include the integration of multimodal sensors and adaptation to broader environmental conditions. Full article
(This article belongs to the Special Issue Innovative Applications of UAVs in Search and Rescue: Improving Safety and Effectiveness)
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27 pages, 9802 KB  
Article
Flight-Safe Inference: SVD-Compressed LSTM Acceleration for Real-Time UAV Engine Monitoring Using Custom FPGA Hardware Architecture
by Sreevalliputhuru Siri Priya, Penneru Shaswathi Sanjana, Rama Muni Reddy Yanamala, Rayappa David Amar Raj, Archana Pallakonda, Christian Napoli and Cristian Randieri
Drones 2025, 9(7), 494; https://doi.org/10.3390/drones9070494 - 14 Jul 2025
Cited by 13 | Viewed by 2288
Abstract
Predictive maintenance (PdM) is a proactive strategy that enhances safety, minimizes unplanned downtime, and optimizes operational costs by forecasting equipment failures before they occur. This study presents a novel Field Programmable Gate Array (FPGA)-accelerated predictive maintenance framework for UAV engines using a Singular [...] Read more.
Predictive maintenance (PdM) is a proactive strategy that enhances safety, minimizes unplanned downtime, and optimizes operational costs by forecasting equipment failures before they occur. This study presents a novel Field Programmable Gate Array (FPGA)-accelerated predictive maintenance framework for UAV engines using a Singular Value Decomposition (SVD)-optimized Long Short-Term Memory (LSTM) model. The model performs binary classification to predict the likelihood of imminent engine failure by processing normalized multi-sensor data, including temperature, pressure, and vibration measurements. To enable real-time deployment on resource-constrained UAV platforms, the LSTM’s weight matrices are compressed using Singular Value Decomposition (SVD), significantly reducing computational complexity while preserving predictive accuracy. The compressed model is executed on a Xilinx ZCU-104 FPGA and uses a pipelined, AXI-based hardware accelerator with efficient memory mapping and parallelized gate calculations tailored for low-power onboard systems. Unlike prior works, this study uniquely integrates a tailored SVD compression strategy with a custom hardware accelerator co-designed for real-time, flight-safe inference in UAV systems. Experimental results demonstrate a 98% classification accuracy, a 24% reduction in latency, and substantial FPGA resource savings—specifically, a 26% decrease in BRAM usage and a 37% reduction in DSP consumption—compared to the 32-bit floating-point SVD-compressed FPGA implementation, not CPU or GPU. These findings confirm the proposed system as an efficient and scalable solution for real-time UAV engine health monitoring, thereby enhancing in-flight safety through timely fault prediction and enabling autonomous engine monitoring without reliance on ground communication. Full article
(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones)
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28 pages, 47806 KB  
Article
Experimental Validation of UAV Search and Detection System in Real Wilderness Environment
by Stella Dumenčić, Luka Lanča, Karlo Jakac and Stefan Ivić
Drones 2025, 9(7), 473; https://doi.org/10.3390/drones9070473 - 3 Jul 2025
Cited by 5 | Viewed by 2525
Abstract
Search and rescue (SAR) missions require reliable search methods to locate survivors, especially in challenging environments. Introducing unmanned aerial vehicles (UAVs) can enhance the efficiency of SAR missions while simultaneously increasing the safety of everyone involved. Motivated by this, we experiment with autonomous [...] Read more.
Search and rescue (SAR) missions require reliable search methods to locate survivors, especially in challenging environments. Introducing unmanned aerial vehicles (UAVs) can enhance the efficiency of SAR missions while simultaneously increasing the safety of everyone involved. Motivated by this, we experiment with autonomous UAV search for humans in Mediterranean karst environment. The UAVs are directed using the Heat equation-driven area coverage (HEDAC) ergodic control method based on known probability density and detection function. The sensing framework consists of a probabilistic search model, motion control system, and object detection enabling to calculate the target’s detection probability. This paper focuses on the experimental validation of the proposed sensing framework. The uniform probability density, achieved by assigning suitable tasks to 78 volunteers, ensures the even probability of finding targets. The detection model is based on the You Only Look Once (YOLO) model trained on a previously collected orthophoto image database. The experimental search is carefully planned and conducted, while recording as many parameters as possible. The thorough analysis includes the motion control system, object detection, and search validation. The assessment of the detection and search performance strongly indicates that the detection model in the UAV control algorithm is aligned with real-world results. Full article
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14 pages, 3505 KB  
Article
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
Cited by 1 | Viewed by 5734
Abstract
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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20 pages, 7513 KB  
Article
UAV Autonomous Navigation System Based on Air–Ground Collaboration in GPS-Denied Environments
by Pengyu Yue, Jing Xin, Yan Huang, Jiahang Zhao, Christopher Zhang, Wei Chen and Mao Shan
Drones 2025, 9(6), 442; https://doi.org/10.3390/drones9060442 - 16 Jun 2025
Cited by 13 | Viewed by 7890
Abstract
This paper explores breakthroughs from the perspective of UAV navigation architectures and proposes a UAV autonomous navigation method based on aerial–ground cooperative perception to address the challenge of UAV navigation in GPS-denied and unknown environments. The approach consists of two key components. Firstly, [...] Read more.
This paper explores breakthroughs from the perspective of UAV navigation architectures and proposes a UAV autonomous navigation method based on aerial–ground cooperative perception to address the challenge of UAV navigation in GPS-denied and unknown environments. The approach consists of two key components. Firstly, a mobile anchor trilateration and environmental modeling method is developed using a multi-UAV system by integrating the visual sensing capabilities of aerial surveillance UAVs with ultra-wideband technology. It constructs a real-time global 3D environmental model and provides precise positioning information, supporting autonomous planning and target guidance for near-ground UAV navigation. Secondly, based on real-time environmental perception, an improved D* Lite algorithm is employed to plan rapid and collision-free flight trajectories for near-ground navigation. This allows the UAV to autonomously execute collision-free movement from the initial position to the target position in complex environments. The results of real-world flight experiments demonstrate that the system can efficiently construct a global 3D environmental model in real time. It also provides accurate flight trajectories for the near-ground navigation of UAVs while delivering real-time positional updates during flight. The system enables UAVs to autonomously navigate in GPS-denied and unknown environments, and this work verifies the practicality and effectiveness of the proposed air–ground cooperative perception navigation system, as well as the mobile anchor trilateration and environmental modeling method. Full article
(This article belongs to the Special Issue Autonomous Drone Navigation in GPS-Denied Environments)
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48 pages, 2716 KB  
Review
Tethered Drones: A Comprehensive Review of Technologies, Challenges, and Applications
by Francesco Fattori and Silvio Cocuzza
Drones 2025, 9(6), 425; https://doi.org/10.3390/drones9060425 - 11 Jun 2025
Cited by 11 | Viewed by 20227
Abstract
Tethered drones—defined in this work as multirotor aerial platforms physically connected to a ground station via a cable—have emerged as a transformative subclass of Tethered Unmanned Aerial Vehicles (TUAVs), offering enhanced power autonomy, communication robustness, and safety through a physical ground connection. This [...] Read more.
Tethered drones—defined in this work as multirotor aerial platforms physically connected to a ground station via a cable—have emerged as a transformative subclass of Tethered Unmanned Aerial Vehicles (TUAVs), offering enhanced power autonomy, communication robustness, and safety through a physical ground connection. This review provides a comprehensive analysis of the current state of tethered drone systems technology, focusing on critical system components such as power delivery, data transmission, tether management, and modeling frameworks. Emphasis is placed on the tether multifunctional role—not only as a physical link but also as a sensor, actuator, and communication channel—impacting both hardware design and control strategies. By consolidating fragmented research across disciplines, this work offers a unified reference for the design, implementation, and advancement of TUAV systems, with tethered drones as their principal application. Full article
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26 pages, 1616 KB  
Review
Unmanned Aerial Vehicles in Last-Mile Parcel Delivery: A State-of-the-Art Review
by Almodather Mohamed and Moataz Mohamed
Drones 2025, 9(6), 413; https://doi.org/10.3390/drones9060413 - 6 Jun 2025
Cited by 19 | Viewed by 9345
Abstract
Unmanned Aerial Vehicles (UAVs) are being increasingly implemented in parcel delivery applications. The scientific progress in this field is progressing exponentially. However, there is a notable gap in synthesizing recent research progress in UAV applications for last-mile delivery. This review study addresses this [...] Read more.
Unmanned Aerial Vehicles (UAVs) are being increasingly implemented in parcel delivery applications. The scientific progress in this field is progressing exponentially. However, there is a notable gap in synthesizing recent research progress in UAV applications for last-mile delivery. This review study addresses this gap and conducts an in-depth review of UAV research for last-mile delivery across seven domains: environmental performance, economic impacts, social impacts, policy and regulations, routing and scheduling, charging infrastructure, and energy consumption. The review indicates that UAVs promise to reduce last-mile delivery emissions by 71% and costs by 96.5% compared to truck delivery. Saturated knowledge analysis is conducted across the seven domains to identify potential research gaps. Additionally, this review identifies key knowledge gaps, including variability in environmental and cost data, limitations associated with 2D modelling, and a lack of experimental validation. Future research interventions aimed at advancing UAV adoption in last-mile delivery applications are discussed. Full article
(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)
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57 pages, 24925 KB  
Review
AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
by Zheng Yang, Yuting Zhang, Jie Zeng, Yifan Yang, Yufei Jia, Hua Song, Tiejun Lv, Qian Sun and Jianping An
Drones 2025, 9(6), 392; https://doi.org/10.3390/drones9060392 - 23 May 2025
Cited by 25 | Viewed by 15268
Abstract
As unmanned aerial vehicle (UAV) applications expand across logistics, agriculture, and emergency response, safety and security threats are becoming increasingly complex. Addressing these evolving threats, including physical safety and network security threats, requires continued advancement by integrating traditional artificial intelligence (AI) tools such [...] Read more.
As unmanned aerial vehicle (UAV) applications expand across logistics, agriculture, and emergency response, safety and security threats are becoming increasingly complex. Addressing these evolving threats, including physical safety and network security threats, requires continued advancement by integrating traditional artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL), which contribute to significantly enhancing UAV safety and security. Large language models (LLMs), a cutting-edge trend in the AI field, are associated with strong capabilities for learning and adapting across various environments. Their emergence reflects a broader trend toward intelligent systems that may eventually demonstrate behavior comparable to human-level reasoning. This paper summarizes the typical safety and security threats affecting UAVs, reviews the progress of traditional AI technologies, as described in the literature, and identifies strategies for reducing the impact of such threats. It also highlights the limitations of traditional AI technologies and summarizes the current application status of LLMs in UAV safety and security. Finally, this paper discusses the challenges and future research directions for improving UAV safety and security with LLMs. By leveraging their advanced capabilities, LLMs offer potential benefits in critical domains such as urban air traffic management, precision agriculture, and emergency response, fostering transformative progress toward adaptive, reliable, and secure UAV systems that address modern operational complexities. Full article
(This article belongs to the Special Issue AI for Cybersecurity in Unmanned Aerial Systems (UAS))
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46 pages, 9673 KB  
Review
Advances in UAV Path Planning: A Comprehensive Review of Methods, Challenges, and Future Directions
by Wenlong Meng, Xuegang Zhang, Lvzhuoyu Zhou, Hangyu Guo and Xin Hu
Drones 2025, 9(5), 376; https://doi.org/10.3390/drones9050376 - 16 May 2025
Cited by 50 | Viewed by 23961
Abstract
Unmanned aerial vehicles (UAVs) have revolutionized fields such as monitoring, cargo delivery, precision farming, and emergency response, demonstrating remarkable flexibility and operational effectiveness. A fundamental aspect of UAV autonomy lies in route optimization, which determines efficient paths while considering factors like mission goals, [...] Read more.
Unmanned aerial vehicles (UAVs) have revolutionized fields such as monitoring, cargo delivery, precision farming, and emergency response, demonstrating remarkable flexibility and operational effectiveness. A fundamental aspect of UAV autonomy lies in route optimization, which determines efficient paths while considering factors like mission goals, safety, and power consumption. This article presents an extensive overview of methodologies for UAV route planning, including deterministic models, stochastic sampling techniques, biologically inspired methods, and integrated algorithmic frameworks. The discussion extends to their performance in various operational contexts, including stationary, moving, and three-dimensional settings. Innovative methods utilizing artificial intelligence, particularly machine learning and neural networks, are emphasized for their promise in facilitating adaptive responses to intricate, evolving environments. Furthermore, strategies focused on reducing energy usage and enabling coordinated operations among multiple drones are analyzed, addressing issues such as prolonged operation, distribution of assignments, and navigation around obstacles. Although notable advancements have been achieved, challenges like high computational demands and the need for immediate responsiveness persist. By consolidating the latest progress, this survey provides meaningful perspectives and guidance for the ongoing evolution of UAV route planning solutions. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)
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21 pages, 7844 KB  
Article
WRRT-DETR: Weather-Robust RT-DETR for Drone-View Object Detection in Adverse Weather
by Bei Liu, Jiangliang Jin, Yihong Zhang and Chen Sun
Drones 2025, 9(5), 369; https://doi.org/10.3390/drones9050369 - 14 May 2025
Cited by 16 | Viewed by 6832
Abstract
With the rapid advancement of UAV technology, robust object detection under adverse weather conditions has become critical for enhancing UAVs’ environmental perception. However, object detection in such challenging conditions remains a significant hurdle, and standardized evaluation benchmarks are still lacking. To bridge this [...] Read more.
With the rapid advancement of UAV technology, robust object detection under adverse weather conditions has become critical for enhancing UAVs’ environmental perception. However, object detection in such challenging conditions remains a significant hurdle, and standardized evaluation benchmarks are still lacking. To bridge this gap, we introduce the Adverse Weather Object Detection (AWOD) dataset—a large-scale dataset tailored for object detection in complex maritime environments. The AWOD dataset comprises 20,000 images captured under three representative adverse weather conditions: foggy, flare, and low-light. To address the challenges of scale variation and visual degradation introduced by harsh weather, we propose WRRT-DETR, a weather-robust object detection framework optimized for small objects. Within this framework, we design a gated single-head global–local attention backbone block (GLCE) to fuse local convolutional features with global attention, enhancing small object distinguishability. Additionally, a Frequency–Spatial Feature Augmentation Module (FSAE) is introduced to incorporate frequency-domain information for improved robustness, while an Attention-based Cross-Fusion Module (ACFM) facilitates the integration of multi-scale features. Experimental results demonstrate that WRRT-DETR outperforms SOTA methods on the AWOD dataset, exhibiting superior robustness and detection accuracy in complex weather conditions. Full article
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37 pages, 8477 KB  
Review
Thermal Management for Unmanned Aerial Vehicle Payloads: Mechanisms, Systems, and Applications
by Ganapathi Pamula and Ashwin Ramachandran
Drones 2025, 9(5), 350; https://doi.org/10.3390/drones9050350 - 5 May 2025
Cited by 12 | Viewed by 12175
Abstract
Unmanned aerial vehicles (UAVs) are emerging as powerful tools for transporting temperature-sensitive payloads, including medical supplies, biological samples, and research materials, to remote or hard-to-reach locations. Effective thermal management is essential for maintaining payload integrity, especially during extended flights or harsh environmental conditions. [...] Read more.
Unmanned aerial vehicles (UAVs) are emerging as powerful tools for transporting temperature-sensitive payloads, including medical supplies, biological samples, and research materials, to remote or hard-to-reach locations. Effective thermal management is essential for maintaining payload integrity, especially during extended flights or harsh environmental conditions. This review presents a comprehensive analysis of temperature control mechanisms for UAV payloads, covering both passive and active strategies. Passive systems, such as phase-change materials and high-performance insulation, provide energy-efficient solutions for short-duration flights. In contrast, active systems, including thermoelectric cooling modules and Joule heating elements, offer precise temperature regulation for more demanding applications. We examined case studies that highlight the integration of these technologies in real-world UAV applications, such as vaccine delivery, blood sample transport, and in-flight polymerase chain reaction diagnostics. Additionally, we discussed critical design considerations, including power efficiency, payload capacity, and the impact of thermal management on flight endurance. We then presented an outlook on emerging technologies, such as hybrid power systems and smart feedback control loops, which promise to enhance UAV-based thermal management. This work aimed to guide researchers and practitioners in advancing thermal control technologies, enabling reliable, efficient, and scalable solutions for temperature-sensitive deliveries using UAVs. Full article
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26 pages, 37822 KB  
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
Cited by 4 | Viewed by 5236
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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26 pages, 4680 KB  
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
Cited by 19 | Viewed by 15969
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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28 pages, 20307 KB  
Article
AI-Driven UAV and IoT Traffic Optimization: Large Language Models for Congestion and Emission Reduction in Smart Cities
by Álvaro Moraga , J. de Curtò, I. de Zarzà and Carlos T. Calafate
Drones 2025, 9(4), 248; https://doi.org/10.3390/drones9040248 - 26 Mar 2025
Cited by 28 | Viewed by 6260
Abstract
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, modeled as induction loops, with Large Language Models (LLMs) to optimize traffic flow. Using the SUMO simulator, we conducted [...] Read more.
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, modeled as induction loops, 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 was interfaced with the simulation to dynamically adjust vehicle speeds based on real-time traffic conditions. 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, scalable traffic management, aligning with the future of drone-assisted urban mobility solutions. Full article
(This article belongs to the Special Issue Visual Language Models and Large Language Models for Unmanned Aerial Vehicles)
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46 pages, 4683 KB  
Review
A Review of Last-Mile Delivery Optimization: Strategies, Technologies, Drone Integration, and Future Trends
by Abdullahi Sani Shuaibu, Ashraf Sharif Mahmoud and Tarek Rahil Sheltami
Drones 2025, 9(3), 158; https://doi.org/10.3390/drones9030158 - 21 Feb 2025
Cited by 63 | Viewed by 45090
Abstract
Last-mile delivery (LMD) is an important aspect of contemporary logistics that directly affects operational cost, efficiency, and customer satisfaction. In this paper, we provide a review of the optimization techniques of LMD, focusing on Artificial Intelligence (AI) driven decision-making, IoT-supported real-time monitoring, and [...] Read more.
Last-mile delivery (LMD) is an important aspect of contemporary logistics that directly affects operational cost, efficiency, and customer satisfaction. In this paper, we provide a review of the optimization techniques of LMD, focusing on Artificial Intelligence (AI) driven decision-making, IoT-supported real-time monitoring, and hybrid delivery networks. The combination of AI and IoT improves predictive analytics, dynamic routing, and fleet management, but scalability and regulatory issues are still major concerns. Hybrid frameworks that integrate drones or Unmanned Aerial Vehicles (UAVs), ground robots, and conventional vehicles reduce energy expenditure and increase delivery range, especially in urban contexts. Furthermore, sustainable logistics approaches, including electric vehicle fleets and shared delivery infrastructures, provide promise for minimizing environmental impact. However, economic viability, legal frameworks, and infrastructure readiness still influence the feasibility of large-scale adoption. This review offers a perspective on the changing patterns in LMD, calling for regulatory evolution, technological advancement, as well as interdisciplinary approaches toward cost-effective, durable, and environmentally friendly logistics systems. Full article
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59 pages, 45108 KB  
Review
Safety Systems for Emergency Landing of Civilian Unmanned Aerial Vehicles (UAVs)—A Comprehensive Review
by Mohsen Farajijalal, Hossein Eslamiat, Vikrant Avineni, Eric Hettel and Clark Lindsay
Drones 2025, 9(2), 141; https://doi.org/10.3390/drones9020141 - 14 Feb 2025
Cited by 16 | Viewed by 11204
Abstract
The expanding use of civilian unmanned aerial vehicles (UAVs) has brought forth a crucial need to address the safety risks they pose in the event of failure, especially when flying in populated areas. This paper reviews recent advancements in recovery systems designed for [...] Read more.
The expanding use of civilian unmanned aerial vehicles (UAVs) has brought forth a crucial need to address the safety risks they pose in the event of failure, especially when flying in populated areas. This paper reviews recent advancements in recovery systems designed for the emergency landing of civilian UAVs. It covers a wide range of recovery methods, categorizing them based on different recovery approaches and UAV types, including multirotor and fixed-wing. The study highlights the diversity of recovery strategies, ranging from parachute and airbag systems to software-based methods and hybrid solutions. It emphasizes the importance of considering UAV-specific characteristics and operational environments when selecting appropriate safety systems. Furthermore, by comparing various emergency landing systems, this study reveals that integrating multiple approaches based on the UAV type and mission requirements can achieve broader cover of emergency situations compared to using a single system for a specific scenario. Examples of UAVs that utilize emergency landing systems are also provided. For each recovery system, three key parameters of operating altitude, flight speed and added weight are presented. Researchers and UAV developers can utilize this information to identify a suitable emergency landing method tailored to their mission requirements and available UAVs. Based on the key trends and challenges found in the literature, this review concludes by proposing specific, actionable recommendations. These recommendations are directed towards researchers, UAV developers, and regulatory bodies, and focus on enhancing the safety of civilian UAV operations through the improvement of emergency landing systems. Full article
(This article belongs to the Section Drone Design and Development)
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22 pages, 2471 KB  
Article
Underwater Acoustic MAC Protocol for Multi-Objective Optimization Based on Multi-Agent Reinforcement Learning
by Jinfang Jiang, Yiling Dong, Guangjie Han and Gang Su
Drones 2025, 9(2), 123; https://doi.org/10.3390/drones9020123 - 7 Feb 2025
Cited by 5 | Viewed by 2333
Abstract
In underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel allocation fairness and energy efficiency, [...] Read more.
In underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel allocation fairness and energy efficiency, this paper proposes a multi-objective optimization MAC protocol (MOMA-MAC) based on multi-agent reinforcement learning. MOMA-MAC utilizes a delay reward mechanism combined with the Multi-agent Proximal Policy Optimization Algorithm (MAPPO) to design a dual reward mechanism, which enables agents to adaptively collaborate and compete to optimize the use of network resources. According to experimental results, MOMA-MAC performs noticeably better than traditional MAC protocols and deep reinforcement learning-based methods in terms of throughput, energy efficiency, and fairness in multi-agent scenarios, showing great potential for improving communication efficiency and energy utilization. Full article
(This article belongs to the Topic Advances in Wireless and Mobile Networking)
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30 pages, 11390 KB  
Article
A Multi-Objective Black-Winged Kite Algorithm for Multi-UAV Cooperative Path Planning
by Xiukang Liu, Fufu Wang, Yu Liu and Long Li
Drones 2025, 9(2), 118; https://doi.org/10.3390/drones9020118 - 5 Feb 2025
Cited by 15 | Viewed by 2752
Abstract
In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning [...] Read more.
In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning issues that involve multiple machines. Here, we introduce an advanced mathematical model for cooperative path planning among multiple UAVs in urban logistics scenarios, employing the non-dominated sorting black-winged kite algorithm (NSBKA) to address this multi-objective optimization challenge. To evaluate the efficacy of NSBKA, it was benchmarked against other algorithms using the Zitzler, Deb, and Thiele (ZDT) test problems, Deb, Thiele, Laumanns, and Zitzler (DTLZ) test problems, and test functions from the conference on evolutionary computation 2009 (CEC2009) for three types of multi-objective problems. Comparative analyses and statistical results indicate that the proposed algorithm outperforms on all 22 test functions. To verify the capability of NSBKA in addressing the multi-UAV cooperative problem model, the algorithm is applied to solve the problem. Simulation experiments for three UAVs and five UAVs show that the proposed algorithm can obtain a more reasonable collaborative path solution set for UAVs. Moreover, path planning based on NSBKA is generally superior to other algorithms in terms of energy saving, safety, and computing efficiency during planning. This affirms the effectiveness of the meta-heuristic algorithm in dealing with multiple objective multi-UAV cooperation problems and further enhances the robustness and competitiveness of NSBKA. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning: 2nd Edition)
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25 pages, 943 KB  
Article
Optimization of Bandwidth Allocation and UAV Placement in Active RIS-Assisted UAV Communication Networks with Wireless Backhaul
by Thi-Thuy-Minh Tran, Binh-Minh Vu and Oh-Soon Shin
Drones 2025, 9(2), 111; https://doi.org/10.3390/drones9020111 - 2 Feb 2025
Cited by 7 | Viewed by 4197
Abstract
In this paper, we present a novel design for unmanned aerial vehicle (UAV) communication networks with wireless backhaul, where an active reconfigurable intelligent surface (ARIS) is deployed to improve connections between a UAV and multiple users, while mitigating channel impairments in complex environments. [...] Read more.
In this paper, we present a novel design for unmanned aerial vehicle (UAV) communication networks with wireless backhaul, where an active reconfigurable intelligent surface (ARIS) is deployed to improve connections between a UAV and multiple users, while mitigating channel impairments in complex environments. The proposed design aims to maximize the achievable sum rate of all networks by jointly optimizing UAV placement; resource management strategies; transmit power allocation; and ARIS reflection coefficients, subject to backhaul constraints and power budget limitations in the ARIS system. The resulting optimization problem is highly non-convex, posing significant challenges. To tackle this, we decompose the problem into three interrelated sub-problems and apply inner approximation (IA) techniques to handle the non-convexities within each sub-problem. Moreover, a comprehensive alternating optimization framework is proposed to implement an iterative solution for the sub-problems. Simulation results demonstrate that the proposed algorithm achieves approximately 59% improvement in the average sum rate, substantially enhancing overall network reliability compared to existing benchmark schemes. Full article
(This article belongs to the Special Issue Drone Communication, Networking, and Trajectory Control in Urban Environments)
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42 pages, 40649 KB  
Article
A Multi-Drone System Proof of Concept for Forestry Applications
by André G. Araújo, Carlos A. P. Pizzino, Micael S. Couceiro and Rui P. Rocha
Drones 2025, 9(2), 80; https://doi.org/10.3390/drones9020080 - 21 Jan 2025
Cited by 14 | Viewed by 7530
Abstract
This study presents a multi-drone proof of concept for efficient forest mapping and autonomous operation, framed within the context of the OPENSWARM EU Project. The approach leverages state-of-the-art open-source simultaneous localisation and mapping (SLAM) frameworks, like LiDAR (Light Detection And Ranging) Inertial Odometry [...] Read more.
This study presents a multi-drone proof of concept for efficient forest mapping and autonomous operation, framed within the context of the OPENSWARM EU Project. The approach leverages state-of-the-art open-source simultaneous localisation and mapping (SLAM) frameworks, like LiDAR (Light Detection And Ranging) Inertial Odometry via Smoothing and Mapping (LIO-SAM), and Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm (DCL-SLAM), seamlessly integrated within the MRS UAV System and Swarm Formation packages. This integration is achieved through a series of procedures compliant with Robot Operating System middleware (ROS), including an auto-tuning particle swarm optimisation method for enhanced flight control and stabilisation, which is crucial for autonomous operation in challenging environments. Field experiments conducted in a forest with multiple drones demonstrate the system’s ability to navigate complex terrains as a coordinated swarm, accurately and collaboratively mapping forest areas. Results highlight the potential of this proof of concept, contributing to the development of scalable autonomous solutions for forestry management. The findings emphasise the significance of integrating multiple open-source technologies to advance sustainable forestry practices using swarms of drones. Full article
(This article belongs to the Special Issue Feature Papers for Drones in Agriculture and Forestry Section: 2nd Edition)
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22 pages, 9199 KB  
Review
UAV Detection with Passive Radar: Algorithms, Applications, and Challenges
by Zhibo Tang, He Ma, Youmin Qu and Xingpeng Mao
Drones 2025, 9(1), 76; https://doi.org/10.3390/drones9010076 - 20 Jan 2025
Cited by 21 | Viewed by 19682
Abstract
The unmanned aerial vehicle (UAV) industry has developed rapidly in recent years and is being applied in a wide range of fields. However, incidents involving unauthorized UAVs that threaten public safety have occurred frequently, highlighting the need for effective and accurate methods to [...] Read more.
The unmanned aerial vehicle (UAV) industry has developed rapidly in recent years and is being applied in a wide range of fields. However, incidents involving unauthorized UAVs that threaten public safety have occurred frequently, highlighting the need for effective and accurate methods to detect and respond to illegal UAVs. This has led to the emergence of various UAV detection technologies, among which passive radar stands out due to its unique advantages. This review aims to offer insights that can support further research and development in the field of UAV detection using passive radar. We begin by exploring the origins of passive radar and then provide a comprehensive overview of its progress from multiple angles, particularly focusing on its application in UAV detection. Finally, we provide a forward-looking discussion on the future development trends and challenges faced by passive radar in UAV detection. Full article
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19 pages, 6329 KB  
Article
Spray Deposition and Drift as Influenced by Wind Speed and Spray Nozzles from a Remotely Piloted Aerial Application System
by Daniel E. Martin, Jeffrey W. Perine, Shanique Grant, Farah Abi-Akar, Jerri Lynn Henry and Mohamed A. Latheef
Drones 2025, 9(1), 66; https://doi.org/10.3390/drones9010066 - 16 Jan 2025
Cited by 13 | Viewed by 2855
Abstract
The phenomenal growth of remotely piloted aerial application systems (RPAASs) in recent years has raised questions about their impact on the off-target movement of plant protection products. The spray droplet spectrum is one of the important determining factors that govern droplet trajectories and [...] Read more.
The phenomenal growth of remotely piloted aerial application systems (RPAASs) in recent years has raised questions about their impact on the off-target movement of plant protection products. The spray droplet spectrum is one of the important determining factors that govern droplet trajectories and off-target movement of pesticide particles. A field study was conducted to compare in-swath and downwind spray deposition on ground samplers from a 20 L RPAAS platform, equipped with three different nozzles, which provided fine, medium, and extra-coarse droplet spectra. A fluorescent dye was used as a tracer to determine spray deposition. Airborne spray droplets were measured at 10 and 20 m downwind. Downwind deposition measured on ground samplers showed that the extra-coarse nozzle received significantly fewer deposits than the medium or the fine nozzle. Similarly, the airborne deposition for the extra-coarse nozzle was significantly less compared to either the fine or the medium nozzle. Linear mixed effects modeling confirmed these results and showed that wind speed served as a covariate by refining the deposition differences among nozzles. Results indicated that spray drift from RPAAS platforms may be mitigated by using appropriate nozzles that produce larger droplet spectra. These results will provide aerial applicators with a better understanding of the best management practices to mitigate drift. Full article
(This article belongs to the Special Issue Drones in Sustainable Agriculture)
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30 pages, 9579 KB  
Review
Unmanned Aircraft Systems (UASs): Current State, Emerging Technologies, and Future Trends
by Gennaro Ariante and Giuseppe Del Core
Drones 2025, 9(1), 59; https://doi.org/10.3390/drones9010059 - 15 Jan 2025
Cited by 41 | Viewed by 23924
Abstract
Unmanned aircraft, commonly referred to as drones, represent a valuable alternative for various operational tasks due to their versatility, flexibility, cost-effectiveness, and reusability. These features make them particularly advantageous in environments that are hazardous or inaccessible to humans. Recent developments have highlighted a [...] Read more.
Unmanned aircraft, commonly referred to as drones, represent a valuable alternative for various operational tasks due to their versatility, flexibility, cost-effectiveness, and reusability. These features make them particularly advantageous in environments that are hazardous or inaccessible to humans. Recent developments have highlighted a significant increase in the use of unmanned aircraft within metropolitan areas. This growth has necessitated the implementation of new regulations and guidelines to ensure the safe integration of UAS into urban environments. Consequently, the concept of UAM has emerged. UAM refers to an innovative air transportation paradigm designed for both passengers and cargo within urban settings, leveraging the capabilities of drones. This review manuscript explores the latest advancements for UAS, focusing on updated regulations, definitions, enabling technologies, and airspace classifications relevant to UAM operations. Additionally, it provides a comprehensive overview of unmanned aircraft systems, including their classifications, key features, and primary applications. Full article
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44 pages, 13137 KB  
Article
The Future of Last-Mile Delivery: Lifecycle Environmental and Economic Impacts of Drone-Truck Parallel Systems
by Danwen Bao, Yu Yan, Yuhan Li and Jiajun Chu
Drones 2025, 9(1), 54; https://doi.org/10.3390/drones9010054 - 14 Jan 2025
Cited by 23 | Viewed by 15145
Abstract
With rapid advancements in unmanned aerial vehicle (UAV) technology, its integration into logistics operations has emerged as a promising solution for improving efficiency and sustainability. Among the emerging solutions, a collaborative delivery model involving drones and trucks addresses last-mile delivery challenges by leveraging [...] Read more.
With rapid advancements in unmanned aerial vehicle (UAV) technology, its integration into logistics operations has emerged as a promising solution for improving efficiency and sustainability. Among the emerging solutions, a collaborative delivery model involving drones and trucks addresses last-mile delivery challenges by leveraging the complementary strengths of both modes of transport. However, evaluating the environmental and economic impacts of this transportation mode requires a systematic framework to capture its unique characteristics and minimize environmental impacts and costs. This paper investigates the Parallel Drone Scheduling Traveling Salesman Problem (PDSTSP) to evaluate the environmental and economic sustainability of a collaborative drone-truck delivery system. Specifically, a mathematical model for this delivery system is developed to optimize joint delivery operations. Environmental impacts are assessed using a comprehensive Life Cycle Assessment (LCA), including emissions and operational noise, while a Life Cycle Cost Analysis (LCCA) quantifies economic performance across five cost dimensions. Sensitivity analysis explores factors such as delivery density, traffic congestion, and wind conditions. Results show that, compared to the electric vehicle fleet, the proposed model achieves an approximate 20% reduction in carbon emissions, while delivering a 20–30% cost reduction relative to the fuel truck fleet. Drones’ efficiency in short-distance deliveries alleviates trucks’ load, cutting environmental and operational costs. This study offers practical insights and recommendations for implementing drone-truck parallel delivery systems, particularly in high-demand density areas. Full article
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23 pages, 482 KB  
Systematic Review
Systematic Literature Review Methodology for Drone Recharging Processes in Agriculture and Disaster Management
by Leonardo Grando, Juan Fernando Galindo Jaramillo, José Roberto Emiliano Leite and Edson Luiz Ursini
Drones 2025, 9(1), 40; https://doi.org/10.3390/drones9010040 - 8 Jan 2025
Cited by 19 | Viewed by 7160
Abstract
Unmanned Aerial Vehicles (UAVs), or drones, are becoming increasingly vital in agriculture and disaster management due to their autonomous monitoring, data collection, and service delivery capability. However, energy constraints often limit their potential, highlighting the need for efficient recharging and energy management solutions. [...] Read more.
Unmanned Aerial Vehicles (UAVs), or drones, are becoming increasingly vital in agriculture and disaster management due to their autonomous monitoring, data collection, and service delivery capability. However, energy constraints often limit their potential, highlighting the need for efficient recharging and energy management solutions. This systematic literature review (SLR) examines the current simulations of drone recharging technologies within precision agriculture and disaster relief. It highlights recent advancements, including various algorithms for path and mission planning, while identifying ongoing challenges, particularly the scarcity of studies on the recharging coordination that affects UAV operations in these fields. The review encompasses 36 high-quality studies from 2038 papers initially found in the literature. Despite significant progress in recharging technologies, achieving sustainable and continuous UAV operation remains challenging, especially in high-demand energy environments such as disaster zones and agricultural areas. We identify three research gaps—knowledge, methodological, and practical. There is a lack of drone recharging studies, as drones are energy-demanding devices. The studies show that the coordination process relies on communication, which can use more battery, and we also find a lack of real-world applications in the studies. Another finding is that the context of disaster is studied more than agricultural usage. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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18 pages, 5874 KB  
Review
Key Technology for Human-System Integration of Unmanned Aircraft Systems in Urban Air Transportation
by Chuanyan Feng, Jinwei Hou, Shuang Liu, Xiaoru Wanyan, Menglong Ding, Huadong Li, De Yan and Dawei Bie
Drones 2025, 9(1), 18; https://doi.org/10.3390/drones9010018 - 27 Dec 2024
Cited by 4 | Viewed by 3153
Abstract
Effective integration of human factors and systems engineering has become a technical challenge that constrains the full realization of human performance in unmanned aircraft systems (UAS) for urban air transportation. To address this challenge, breakthroughs are needed in key technologies related to human-system [...] Read more.
Effective integration of human factors and systems engineering has become a technical challenge that constrains the full realization of human performance in unmanned aircraft systems (UAS) for urban air transportation. To address this challenge, breakthroughs are needed in key technologies related to human-system integration (HSI) of UAS. Based on literature review and industry practices, unique HF challenges of UAS are identified, and two research issues, HSI analysis throughout UAS development lifecycle and HSI practice under UAS typical lifecycle stages, are summarized. To address these issues, a model-based human-system integration (MBHSI) design framework is proposed for the UAS development lifecycle, along with an HSI practice framework for UAS under typical human readiness levels. The HSI design and practice framework can provide references for HF design of UAS in urban air transportation. 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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24 pages, 3802 KB  
Article
Performance of Individual Tree Segmentation Algorithms in Forest Ecosystems Using UAV LiDAR Data
by Javier Marcello, María Spínola, Laia Albors, Ferran Marqués, Dionisio Rodríguez-Esparragón and Francisco Eugenio
Drones 2024, 8(12), 772; https://doi.org/10.3390/drones8120772 - 19 Dec 2024
Cited by 11 | Viewed by 8273
Abstract
Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This [...] Read more.
Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This study primarily assesses individual tree segmentation algorithms in two forest ecosystems with different levels of complexity using high-density LiDAR data captured by the Zenmuse L1 sensor on a DJI Matrice 300RTK platform. The processing methodology for LiDAR data includes preliminary preprocessing steps to create Digital Elevation Models, Digital Surface Models, and Canopy Height Models. A comprehensive evaluation of the most effective techniques for classifying ground points in the LiDAR point cloud and deriving accurate models was performed, concluding that the Triangular Irregular Network method is a suitable choice. Subsequently, the segmentation step is applied to enable the analysis of forests at the individual tree level. Segmentation is crucial for monitoring forest health, estimating biomass, and understanding species composition and diversity. However, the selection of the most appropriate segmentation technique remains a hot research topic with a lack of consensus on the optimal approach and metrics to be employed. Therefore, after the review of the state of the art, a comparative assessment of four common segmentation algorithms (Dalponte2016, Silva2016, Watershed, and Li2012) was conducted. Results demonstrated that the Li2012 algorithm, applied to the normalized 3D point cloud, achieved the best performance with an F1-score of 91% and an IoU of 83%. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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22 pages, 10279 KB  
Article
Cybersecurity Challenges in UAV Systems: IEMI Attacks Targeting Inertial Measurement Units
by Issam Boukabou, Naima Kaabouch and Dulana Rupanetti
Drones 2024, 8(12), 738; https://doi.org/10.3390/drones8120738 - 8 Dec 2024
Cited by 5 | Viewed by 8471
Abstract
The rapid expansion in unmanned aerial vehicles (UAVs) across various sectors, such as surveillance, agriculture, disaster management, and infrastructure inspection, highlights the growing need for robust navigation systems. However, this growth also exposes critical vulnerabilities, particularly in UAV package delivery operations, where intentional [...] Read more.
The rapid expansion in unmanned aerial vehicles (UAVs) across various sectors, such as surveillance, agriculture, disaster management, and infrastructure inspection, highlights the growing need for robust navigation systems. However, this growth also exposes critical vulnerabilities, particularly in UAV package delivery operations, where intentional electromagnetic interference (IEMI) poses significant security and safety threats. This paper addresses IEMI attacks targeting inertial measurement units (IMUs) in UAVs, focusing on their susceptibility to medium-power electromagnetic interference. Our approach combines a comprehensive literature review and QuickField simulation with experimental validation using a commercially available 6-degree-of-freedom (DOF) IMU sensor. We propose a hardware-based electromagnetic shielding solution using mu-metal to mitigate IEMI’s impact on sensor performance. The study combines experimental testing with simulations to evaluate the shielding effectiveness under controlled conditions. The results of the measurements showed that medium-power IEMI significantly distorted IMU sensor readings, but our proposed shielding method effectively reduces the impact, improving sensor reliability. We demonstrate the mechanisms by which medium-power IEMI disrupts sensor operation, offering insights for future research directions. These findings also highlight the importance of integrating hardware-based shielding solutions to safeguard UAV systems against electromagnetic threats. Full article
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28 pages, 19518 KB  
Review
Urban Air Mobility Communications and Networking: Recent Advances, Techniques, and Challenges
by Muhammad Yeasir Arafat and Sungbum Pan
Drones 2024, 8(12), 702; https://doi.org/10.3390/drones8120702 - 24 Nov 2024
Cited by 24 | Viewed by 10539
Abstract
Over the past few years, our traditional ground-based transportation system has encountered various challenges, including overuse, traffic congestion, growing urban populations, high infrastructure costs, and disorganization. Unmanned aerial vehicles, commonly referred to as drones, have significantly impacted aerial communication in both the academic [...] Read more.
Over the past few years, our traditional ground-based transportation system has encountered various challenges, including overuse, traffic congestion, growing urban populations, high infrastructure costs, and disorganization. Unmanned aerial vehicles, commonly referred to as drones, have significantly impacted aerial communication in both the academic and industrial sectors. Therefore, researchers and scientists from the aviation and automotive industries have collaborated to create an innovative air transport system that solves traditional transport problems. In the coming years, urban air mobility (UAM) is expected to become an emerging air transportation system that enables on-demand air travel. UAM is also anticipated to offer more environmentally friendly, cost-effective, and faster modes of transportation than ground-based alternatives. Owing to the unique characteristics of personal air vehicles, ensuring reliable communication and maintaining proper safety and security, air traffic management, collision detection, path planning, and highly accurate localization and navigation have become increasingly complex. This article provides an extensive literature review of recent technologies to address the challenges UAM faces. First, we present UAM communication requirements in terms of coverage, data rate, latency, spectrum efficiency, networking, and computing capabilities. Subsequently, we identify the potential key technological enablers to meet these requirements and overcome their challenges. Finally, we discuss open research issues, challenges, and future research directions for UAM deployment. Full article
(This article belongs to the Section Innovative Urban Mobility)
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20 pages, 11387 KB  
Article
An Algorithm for Affordable Vision-Based GNSS-Denied Strapdown Celestial Navigation
by Samuel Teague and Javaan Chahl
Drones 2024, 8(11), 652; https://doi.org/10.3390/drones8110652 - 7 Nov 2024
Cited by 1 | Viewed by 60709
Abstract
Celestial navigation is rarely seen in modern Uncrewed Aerial Vehicles (UAVs). The size and weight of a stabilized imaging system, and the lack of precision, tend to be at odds with the operational requirements of the aircraft. Nonetheless, celestial navigation is one of [...] Read more.
Celestial navigation is rarely seen in modern Uncrewed Aerial Vehicles (UAVs). The size and weight of a stabilized imaging system, and the lack of precision, tend to be at odds with the operational requirements of the aircraft. Nonetheless, celestial navigation is one of the few non-emissive modalities that enables global navigation over the ocean at night in Global Navigation Satellite System (GNSS) denied environments. This study demonstrates a modular, low cost, lightweight strapdown celestial navigation solution that is utilized in conjunction with Ardupilot running on a Cube Orange to produce position estimates to within 4 km. By performing an orbit through a full rotation of compass heading and averaging the position output, we demonstrate that the biases present in a strapdown imaging system can be nullified to drastically improve the position estimate. Furthermore, an iterative method is presented which enables the geometric alignment of the camera with the Attitude and Heading Reference System (AHRS) in-flight without an external position input. The algorithm is tested using real flight data captured from a fixed wing aircraft. The results from this study offer promise for the application of low cost celestial navigation as a redundant navigation modality in affordable, lightweight drones. Full article
(This article belongs to the Special Issue Drones Navigation and Orientation)
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25 pages, 23247 KB  
Article
Infrared and Visible Camera Integration for Detection and Tracking of Small UAVs: Systematic Evaluation
by Ana Pereira, Stephen Warwick, Alexandra Moutinho and Afzal Suleman
Drones 2024, 8(11), 650; https://doi.org/10.3390/drones8110650 - 6 Nov 2024
Cited by 11 | Viewed by 8662
Abstract
Given the recent proliferation of Unmanned Aerial Systems (UASs) and the consequent importance of counter-UASs, this project aims to perform the detection and tracking of small non-cooperative UASs using Electro-optical (EO) and Infrared (IR) sensors. Two data integration techniques, at the decision and [...] Read more.
Given the recent proliferation of Unmanned Aerial Systems (UASs) and the consequent importance of counter-UASs, this project aims to perform the detection and tracking of small non-cooperative UASs using Electro-optical (EO) and Infrared (IR) sensors. Two data integration techniques, at the decision and pixel levels, are compared with the use of each sensor independently to evaluate the system robustness in different operational conditions. The data are submitted to a YOLOv7 detector merged with a ByteTrack tracker. For training and validation, additional efforts are made towards creating datasets of spatially and temporally aligned EO and IR annotated Unmanned Aerial Vehicle (UAV) frames and videos. These consist of the acquisition of real data captured from a workstation on the ground, followed by image calibration, image alignment, the application of bias-removal techniques, and data augmentation methods to artificially create images. The performance of the detector across datasets shows an average precision of 88.4%, recall of 85.4%, and mAP@0.5 of 88.5%. Tests conducted on the decision-level fusion architecture demonstrate notable gains in recall and precision, although at the expense of lower frame rates. Precision, recall, and frame rate are not improved by the pixel-level fusion design. Full article
(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones, 2nd Edition)
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27 pages, 4611 KB  
Review
Vision-Based Drone Detection in Complex Environments: A Survey
by Ziyi Liu, Pei An, You Yang, Shaohua Qiu, Qiong Liu and Xinghua Xu
Drones 2024, 8(11), 643; https://doi.org/10.3390/drones8110643 - 5 Nov 2024
Cited by 43 | Viewed by 16108
Abstract
The frequent illegal use of drones poses a serious threat to public security and property. Counter-drones are crucial tools. The prerequisite for an effective counter-drone is to detect drones accurately. With the rapid advancements in computer vision, vision-based drone detection methods have emerged [...] Read more.
The frequent illegal use of drones poses a serious threat to public security and property. Counter-drones are crucial tools. The prerequisite for an effective counter-drone is to detect drones accurately. With the rapid advancements in computer vision, vision-based drone detection methods have emerged as a hot topic of research. However, current reviews of vision-based drone detection are less focused on algorithmic summarization and analysis. For this reason, this survey aims to comprehensively review the latest methods for vision-based drone detection in complex environments, with the goal of providing a more thorough reference and guidance for related research. We first explore the imaging and drone characteristics in complex environments and summarize the main challenges of visual UAV detection. Then, we summarize the existing solutions for the main challenges. Finally, we systematically organize and introduce the commonly used datasets and evaluation metrics and conduct experiment comparisons based on the representative methods. We not only reveal the current development status of visual UAV detection but also analyze the deficiencies in current research. On this basis, we further look forward to future research directions and possible breakthroughs, with a view to providing useful insights for further research and development in related fields. Full article
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25 pages, 6840 KB  
Article
Air Route Design of Multi-Rotor UAVs for Urban Air Mobility
by Shan Li, Honghai Zhang, Zhuolun Li and Hao Liu
Drones 2024, 8(10), 601; https://doi.org/10.3390/drones8100601 - 18 Oct 2024
Cited by 4 | Viewed by 4068
Abstract
UAVs will present significant air traffic in the urban airspace in the future, which brings new challenges to urban air traffic management and control. This paper presents an air route design scheme for multi-rotor UAVs in urban airspace to enable UAV operations at [...] Read more.
UAVs will present significant air traffic in the urban airspace in the future, which brings new challenges to urban air traffic management and control. This paper presents an air route design scheme for multi-rotor UAVs in urban airspace to enable UAV operations at orderly levels. The air routes include legs and intersections, which are the three-dimensional channels of UAV flight. Based on the concept of structured and layered urban airspace, the cylindrical pipeline flight leg is designed, and the operation concept, characteristic parameters and flight procedures of along-road and roundabout intersections are proposed. By defining UAV conflict risk and intersection service level metrics, the operation situation of UAVs is quantitatively evaluated. Taking an urban transportation scenario as a case, the proposed route design scheme is simulated in different scale UAV operating scenarios. The results show that the number of UAVs at the intersection is positively correlated with the conflict probability, the number of crossing routes is negatively correlated with the intersection passing rate, and the UAV arrival rate is positively correlated with the intersection average passing time. The along-road type intersection is suitable for the area with fewer crossing routes and sparse UAVs, while the roundabout type intersection is adapted for the area with more crossing routes and dense UAVs. This research provides a new idea for urban UAV air route design, which is helpful in promoting the standardized management of UAVs and accelerating the integration of UAVs into urban airspace. Full article
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16 pages, 12318 KB  
Article
Digital Traffic Lights: UAS Collision Avoidance Strategy for Advanced Air Mobility Services
by Zachary McCorkendale, Logan McCorkendale, Mathias Feriew Kidane and Kamesh Namuduri
Drones 2024, 8(10), 590; https://doi.org/10.3390/drones8100590 - 17 Oct 2024
Cited by 7 | Viewed by 3366
Abstract
With the advancing development of Advanced Air Mobility (AAM), there is a collaborative effort to increase safety in the airspace. AAM is an advancing field of aviation that aims to contribute to the safe transportation of goods and people using aerial vehicles. When [...] Read more.
With the advancing development of Advanced Air Mobility (AAM), there is a collaborative effort to increase safety in the airspace. AAM is an advancing field of aviation that aims to contribute to the safe transportation of goods and people using aerial vehicles. When aerial vehicles are operating in high-density locations such as urban areas, it can become crucial to incorporate collision avoidance systems. Currently, there are available pilot advisory systems such as Traffic Collision and Avoidance Systems (TCAS) providing assistance to manned aircraft, although there are currently no collision avoidance systems for autonomous flights. Standards Organizations such as the Institute of Electrical and Electronics Engineers (IEEE), Radio Technical Commission for Aeronautics (RTCA), and General Aviation Manufacturers Association (GAMA) are working to develop cooperative autonomous flights using UAS-to-UAS Communication in structured and unstructured airspaces. This paper presents a new approach for collision avoidance strategies within structured airspace known as “digital traffic lights”. The digital traffic lights are deployed over an area of land, controlling all UAVs that enter a potential collision zone and providing specific directions to mitigate a collision in the airspace. This strategy is proven through the results demonstrated through simulation in a Cesium Environment. With the deployment of the system, collision avoidance can be achieved for autonomous flights in all airspaces. Full article
(This article belongs to the Collection Feature Papers Collection of the World’s Top 2% Scientists in Drones)
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22 pages, 125192 KB  
Article
Under-Canopy Drone 3D Surveys for Wild Fruit Hotspot Mapping
by Paweł Trybała, Luca Morelli, Fabio Remondino, Levi Farrand and Micael S. Couceiro
Drones 2024, 8(10), 577; https://doi.org/10.3390/drones8100577 - 12 Oct 2024
Cited by 11 | Viewed by 6724
Abstract
Advances in mobile robotics and AI have significantly expanded their application across various domains and challenging conditions. In the past, this has been limited to safe, controlled, and highly structured settings, where simplifying assumptions and conditions allowed for the effective resolution of perception-based [...] Read more.
Advances in mobile robotics and AI have significantly expanded their application across various domains and challenging conditions. In the past, this has been limited to safe, controlled, and highly structured settings, where simplifying assumptions and conditions allowed for the effective resolution of perception-based tasks. Today, however, robotics and AI are moving into the wild, where human–robot collaboration and robust operation are essential. One of the most demanding scenarios involves deploying autonomous drones in GNSS-denied environments, such as dense forests. Despite the challenges, the potential to exploit natural resources in these settings underscores the importance of developing technologies that can operate in such conditions. In this study, we present a methodology that addresses the unique challenges of natural forest environments by integrating positioning methods, leveraging cameras, LiDARs, GNSS, and vision AI with drone technology for under-canopy wild berry mapping. To ensure practical utility for fruit harvesters, we generate intuitive heat maps of berry locations and provide users with a mobile app that supports interactive map visualization, real-time positioning, and path planning assistance. Our approach, tested in a Scandinavian forest, refines the identification of high-yield wild fruit locations using V-SLAM, demonstrating the feasibility and effectiveness of autonomous drones in these demanding applications. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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16 pages, 1699 KB  
Article
Characteristics Analysis and Modeling of Integrated Sensing and Communication Channel for Unmanned Aerial Vehicle Communications
by Xinru Li, Yu Liu, Xinrong Zhang, Yi Zhang, Jie Huang and Ji Bian
Drones 2024, 8(10), 538; https://doi.org/10.3390/drones8100538 - 1 Oct 2024
Cited by 7 | Viewed by 3536
Abstract
As an important part of 6th generation (6G) communication, integrated sensing and communication (ISAC) for unmanned aerial vehicle (UAV) communication has attracted more and more attention. The UAV ISAC channel model considering the space-time evolution of joint and shared clusters is the basis [...] Read more.
As an important part of 6th generation (6G) communication, integrated sensing and communication (ISAC) for unmanned aerial vehicle (UAV) communication has attracted more and more attention. The UAV ISAC channel model considering the space-time evolution of joint and shared clusters is the basis of UAV ISAC system design and network evaluation. This paper introduces the UAV ISAC channel characteristics analysis and modeling method. In the UAV ISAC network, the channel consists of a communication channel and a sensing channel. A joint channel parameter is a combination of all (communication and sensing) multiple path component (MPC) parameter sets, while a shared path is the intersection of the communication path and sensing path that have some of the same MPC parameters. Based on the data collected from a ray-tracing (RT) UAV-to-ground scenario, the joint paths and shared paths of ISAC channels are clustered. Then, by introducing the occurrence and disappearance of clusters based on the birth–death (B–D) process, the space-time evolution of different clusters is described, and the influence of the addition of sensing clusters and the change in flight altitude on the B–D process is explored. Finally, the effects of the sensing cluster and flight altitude on the UAV ISAC channel characteristics, including the angle, time–varying characteristics, and sharing degree (SD), are analyzed. The related UAV ISAC channel characteristics analysis can provide reference for the future development of UAV ISAC systems. Full article
(This article belongs to the Special Issue Technologies and Applications of UAV Channel Models in Communications and Spectrum Awareness)
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22 pages, 4864 KB  
Article
High-Altitude-UAV-Relayed Satellite D2D Communications for 6G IoT Network
by Jie Wang, Tao Hong, Fei Qi, Lei Liu and Xieyao He
Drones 2024, 8(10), 532; https://doi.org/10.3390/drones8100532 - 29 Sep 2024
Cited by 8 | Viewed by 6703
Abstract
High-altitude UAVs (HA-UAVs) have emerged as vital components in 6G communication infrastructures, providing stable relays for telecommunications services above terrestrial and aerial disturbances. This paper explores the multifaceted roles of HA-UAVs in remote sensing, data relay, and telecommunication network enhancement. A Large Language [...] Read more.
High-altitude UAVs (HA-UAVs) have emerged as vital components in 6G communication infrastructures, providing stable relays for telecommunications services above terrestrial and aerial disturbances. This paper explores the multifaceted roles of HA-UAVs in remote sensing, data relay, and telecommunication network enhancement. A Large Language Model (LLM) framework is introduced that dynamically predicts optimal HA-UAV connectivity for IoT devices, enhancing network performance and adaptability. The study emphasizes HA-UAVs’ operational efficiency, broad coverage, and potential to transform global communications, particularly in remote and underserved areas. Our proposed satellite-HA-UAV-IoT architecture with LLM optimization demonstrated substantial improvements, including a 25% increase in network throughput (from 20 Mbps to 25 Mbps at a 20 km distance), a 40% reduction in latency (from 25 ms to 15 ms), and a 28% enhancement in energy efficiency (from 0.25 μJ/bit to 0.18 μJ/bit), significantly advancing the performance and adaptability of next-generation IoT networks. These advancements pave the way for unprecedented connectivity and set the stage for future communication technologies. Full article
(This article belongs to the Special Issue Advances in IoT and Wireless Networks of UAVs: State of the Art, Achievements and Perspectives)
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24 pages, 10240 KB  
Review
A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods
by Bingshu Wang, Qiang Li, Qianchen Mao, Jinbao Wang, C. L. Philip Chen, Aihong Shangguan and Haosu Zhang
Drones 2024, 8(9), 518; https://doi.org/10.3390/drones8090518 - 23 Sep 2024
Cited by 47 | Viewed by 16506
Abstract
The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key [...] Read more.
The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key challenges of existing anti-UAV and delve into various detection and tracking methods. It is noteworthy that our study emphasizes the shift toward deep learning to enhance detection accuracy and tracking performance. Secondly, the survey organizes some public datasets, provides effective links, and discusses the characteristics and limitations of each dataset. Next, by analyzing current research trends, we have identified key areas of innovation, including the progress of deep learning techniques in real-time detection and tracking, multi-sensor fusion systems, and the automatic switching mechanisms that adapt to different conditions. Finally, this survey discusses the limitations and future research directions. This paper aims to deepen the understanding of innovations in anti-UAV detection and tracking methods. Hopefully our work can offer a valuable resource for researchers and practitioners involved in anti-UAV research. Full article
(This article belongs to the Special Issue Detection, Identification and Tracking of UAVs and Drones)
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24 pages, 14165 KB  
Article
Real-Time Fire Detection: Integrating Lightweight Deep Learning Models on Drones with Edge Computing
by Md Fahim Shahoriar Titu, Mahir Afser Pavel, Goh Kah Ong Michael, Hisham Babar, Umama Aman and Riasat Khan
Drones 2024, 8(9), 483; https://doi.org/10.3390/drones8090483 - 13 Sep 2024
Cited by 44 | Viewed by 12182
Abstract
Fire accidents are life-threatening catastrophes leading to losses of life, financial damage, climate change, and ecological destruction. Promptly and efficiently detecting and extinguishing fires is essential to reduce the loss of lives and damage. This study uses drone, edge computing, and artificial intelligence [...] Read more.
Fire accidents are life-threatening catastrophes leading to losses of life, financial damage, climate change, and ecological destruction. Promptly and efficiently detecting and extinguishing fires is essential to reduce the loss of lives and damage. This study uses drone, edge computing, and artificial intelligence (AI) techniques, presenting novel methods for real-time fire detection. This proposed work utilizes a comprehensive dataset of 7187 fire images and advanced deep learning models, e.g., Detection Transformer (DETR), Detectron2, You Only Look Once YOLOv8, and Autodistill-based knowledge distillation techniques to improve the model performance. The knowledge distillation approach has been implemented with the YOLOv8m (medium) as the teacher (base) model. The distilled (student) frameworks are developed employing the YOLOv8n (Nano) and DETR techniques. The YOLOv8n attains the best performance with 95.21% detection accuracy and 0.985 F1 score. A powerful hardware setup, including a Raspberry Pi 5 microcontroller, Pi camera module 3, and a DJI F450 custom-built drone, has been constructed. The distilled YOLOv8n model has been deployed in the proposed hardware setup for real-time fire identification. The YOLOv8n model achieves 89.23% accuracy and an approximate frame rate of 8 for the conducted live experiments. Integrating deep learning techniques with drone and edge devices demonstrates the proposed system’s effectiveness and potential for practical applications in fire hazard mitigation. Full article
(This article belongs to the Special Issue Drones for Wildfire and Prescribed Fire Science)
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