Drones

Article

24 pages, 3802 KiB

Open AccessEditor’s ChoiceArticle

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 2 | Viewed by 2696

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 KiB

Open AccessEditor’s ChoiceArticle

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

Viewed by 5099

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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20 pages, 11387 KiB

Open AccessEditor’s ChoiceArticle

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

Viewed by 54172

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 KiB

Open AccessEditor’s ChoiceArticle

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 2 | Viewed by 2423

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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25 pages, 6840 KiB

Open AccessEditor’s ChoiceArticle

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

Viewed by 1554

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 KiB

Open AccessEditor’s ChoiceArticle

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 2 | Viewed by 1619

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 KiB

Open AccessEditor’s ChoiceArticle

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 4 | Viewed by 2002

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 KiB

Open AccessEditor’s ChoiceArticle

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 1 | Viewed by 1675

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 KiB

Open AccessEditor’s ChoiceArticle

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 1 | Viewed by 2426

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, 14165 KiB

Open AccessEditor’s ChoiceArticle

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 15 | Viewed by 5921

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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22 pages, 6891 KiB

Open AccessEditor’s ChoiceArticle

Transponder: Support for Localizing Distressed People through a Flying Drone Network

by Antonello Calabrò and Eda Marchetti

Drones 2024, 8(9), 465; https://doi.org/10.3390/drones8090465 - 6 Sep 2024

Cited by 2 | Viewed by 1383

Abstract

Context: In Search and Rescue (SAR) operations, the speed and techniques used by rescuers and effective communication with the person in need of rescue are vital for successful operations. Recently, drones have become an essential tool in SAR, used by both military and [...] Read more.

Context: In Search and Rescue (SAR) operations, the speed and techniques used by rescuers and effective communication with the person in need of rescue are vital for successful operations. Recently, drones have become an essential tool in SAR, used by both military and civilian organizations to locate and aid missing persons. Objective: The paper introduces Transponder, a Wi-Fi-based solution designed to enhance SAR efforts by tracking, localizing, and providing first aid information to distressed individuals, even in challenging environments such as forests, mountains, and urban areas lacking GSM/UMTS coverage or that are difficult to reach with terrestrial rescue. Methods: Provide an innovative mechanism based on Wi-Fi beacon detection, LoRa communication, and the possible mobile application to leverage the SAR operation. Provide the preliminary implementation of the Transponder and perform its assessment in scenarios with dense vegetation. Results: The Transponder functionalities have been proven to enhance and expedite the detection of missing persons. Additionally, responses to several research questions regarding its performance and effectiveness are provided. Conclusions: Transponder is an innovative detection mechanism that combines ground-based analysis with on-board analysis, optimizing energy consumption and realizing an efficient solution for real-world scenarios. Full article

(This article belongs to the Topic Recent Advances and Technologies in Emergency Response, Security and Disaster Management Applications)

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24 pages, 1530 KiB

Open AccessEditor’s ChoiceArticle

DFly: A Publicly Auditable and Privacy-Preserving UAS Traffic Management System on Blockchain

by Frederico Baptista, Marina Dehez-Clementi and Jonathan Detchart

Drones 2024, 8(8), 410; https://doi.org/10.3390/drones8080410 - 21 Aug 2024

Cited by 2 | Viewed by 1436

Abstract

The integration of Unmanned Aircraft Systems (UASs) into the current airspace poses significant challenges in terms of safety, security, and operability. As an example, in 2019, the European Union defined a set of rules to support the digitalization of UAS traffic management (UTM) [...] Read more.

The integration of Unmanned Aircraft Systems (UASs) into the current airspace poses significant challenges in terms of safety, security, and operability. As an example, in 2019, the European Union defined a set of rules to support the digitalization of UAS traffic management (UTM) systems and services, namely the U-Space regulations. Current propositions opted for a centralized and private model, concentrated around governmental authorities (e.g., AlphaTango provides the Registration service and depends on the French government). In this paper, we advocate in favor of a more decentralized and transparent model in order to improve safety, security, operability among UTM stakeholders, and legal compliance. As such, we propose DFly, a publicly auditable and privacy-preserving UAS traffic management system on Blockchain, with two initial services: Registration and Flight Authorization. We demonstrate that the use of a blockchain guarantees the public auditability of the two services and corresponding service providers’ actions. In addition, it facilitates the comprehensive and distributed monitoring of airspace occupation and the integration of additional functionalities (e.g., the creation of a live UAS tracker). The combination with zero-knowledge proofs enables the deployment of an automated, distributed, transparent, and privacy-preserving Flight Authorization service, performed on-chain thanks to the blockchain logic. In addition to its construction, this paper details the instantiation of the proposed UTM system with the Ethereum Sepolia’s testnet and the Groth16 ZK-SNARK protocol. On-chain (gas cost) and off-chain (execution time) performance analyses confirm that the proposed solution is a viable and efficient alternative in the spirit of digitalization and offers additional security guarantees. Full article

(This article belongs to the Section Innovative Urban Mobility)

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25 pages, 11288 KiB

Open AccessEditor’s ChoiceArticle

Novel Twist Morphing Aileron and Winglet Design for UAS Control and Performance

by Mir Hossein Negahban, Musavir Bashir, Clovis Priolet and Ruxandra Mihaela Botez

Drones 2024, 8(8), 392; https://doi.org/10.3390/drones8080392 - 13 Aug 2024

Cited by 2 | Viewed by 2504

Abstract

This study introduces a novel “twist morphing aileron and winglet” design for the Unmanned Aircraft System UAS-S45. Improving rolling efficiency through twist morphing ailerons and reducing induced drag through twist morphing winglets are the two main objectives of this study. A novel wing [...] Read more.

This study introduces a novel “twist morphing aileron and winglet” design for the Unmanned Aircraft System UAS-S45. Improving rolling efficiency through twist morphing ailerons and reducing induced drag through twist morphing winglets are the two main objectives of this study. A novel wing design is introduced, and a high-fidelity gradient-based aerodynamic shape optimization is performed for twist morphing ailerons and twist morphing winglets, separately, with specified objective functions. The twist morphing aileron is then compared to the conventional hinged aileron configuration in terms of rolling efficiency and other aerodynamic properties, in particular aircraft maneuverability. The results for twist morphing ailerons show that the novel morphing design increases the aileron efficiency by 34% compared to the conventional design and reduces induced drag by 61%. Next, twist morphing winglets are studied regarding the induced drag in cruise and climb flight conditions. The results for twist morphing winglets indicate that the novel design reduces induced drag by 25.7% in cruise flight and up to 16.51% in climb; it also decreases the total drag by up to 7.5% and increases aerodynamic efficiency by up to 9%. Full article

(This article belongs to the Special Issue Dynamics Modeling and Conceptual Design of UAVs)

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16 pages, 5933 KiB

Open AccessEditor’s ChoiceArticle

Wind Tunnel Investigation of the Icing of a Drone Rotor in Forward Flight

by Derek Harvey, Eric Villeneuve, Mathieu Béland and Maxime Lapalme

Drones 2024, 8(8), 380; https://doi.org/10.3390/drones8080380 - 7 Aug 2024

Cited by 1 | Viewed by 2124

Abstract

The Bell Textron APT70 is a UAV concept developed for last mile delivery and other usual applications. It performs vertical takeoff and transition into aircraft mode for forward flight. It includes four rotor each with four rotating blades. A test campaign has been [...] Read more.

The Bell Textron APT70 is a UAV concept developed for last mile delivery and other usual applications. It performs vertical takeoff and transition into aircraft mode for forward flight. It includes four rotor each with four rotating blades. A test campaign has been performed to study the effects of ice accretion on rotor performance through a parametric study of different parameters, namely MVD, LWC, rotor speed, and pitch angle. This paper presents the last experimentations of this campaign for the drone rotor operating in forward flight under simulated icing conditions in a refrigerated, closed-loop wind tunnel. Results demonstrated that the different parameters studied greatly impacted the collection efficiency of the blades and thus, the resulting ice accretion. Smaller droplets were more easily influenced by the streamlines around the rotating blades, resulting in less droplets impacting the surface and thus slower ice accumulations. Higher rotation speeds and pitch angles generated more energetic streamlines, which again transported more droplets around the airfoils instead of them impacting on the surface, which also led to slower accumulation. Slower ice accumulation resulted in slower thrust losses, since the loss in performances can be directly linked to the amount of ice accreted. This research has not only allowed the obtainment of very insightful results on the effect of each test parameter on the ice accumulation, but it has also conducted the development of a unique test bench for UAV propellers. The new circular test sections along with the new instrumentation installed in and around the tunnel will allow the laboratory to be able to generate icing on various type of UAV in forward flight under representative atmospheric conditions. Full article

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19 pages, 51994 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Impact of Clearing and Grazing on Fuel Management in a Mediterranean Oak Forest through Unmanned Aerial Vehicle Multispectral Data

by Luís Pádua, João P. Castro, José Castro, Joaquim J. Sousa and Marina Castro

Drones 2024, 8(8), 364; https://doi.org/10.3390/drones8080364 - 31 Jul 2024

Viewed by 1562

Abstract

Climate change has intensified the need for robust fire prevention strategies. Sustainable forest fuel management is crucial in mitigating the occurrence and rapid spread of forest fires. This study assessed the impact of vegetation clearing and/or grazing over a three-year period in the [...] Read more.

Climate change has intensified the need for robust fire prevention strategies. Sustainable forest fuel management is crucial in mitigating the occurrence and rapid spread of forest fires. This study assessed the impact of vegetation clearing and/or grazing over a three-year period in the herbaceous and shrub parts of a Mediterranean oak forest. Using high-resolution multispectral data from an unmanned aerial vehicle (UAV), four flight surveys were conducted from 2019 (pre- and post-clearing) to 2021. These data were used to evaluate different scenarios: combined vegetation clearing and grazing, the individual application of each method, and a control scenario that was neither cleared nor purposely grazed. The UAV data allowed for the detailed monitoring of vegetation dynamics, enabling the classification into arboreal, shrubs, herbaceous, and soil categories. Grazing pressure was estimated through GPS collars on the sheep flock. Additionally, a good correlation (r = 0.91) was observed between UAV-derived vegetation volume estimates and field measurements. These practices proved to be efficient in fuel management, with cleared and grazed areas showing a lower vegetation regrowth, followed by areas only subjected to vegetation clearing. On the other hand, areas not subjected to any of these treatments presented rapid vegetation growth. Full article

(This article belongs to the Section Drones in Agriculture and Forestry)

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20 pages, 1709 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Fed4UL: A Cloud–Edge–End Collaborative Federated Learning Framework for Addressing the Non-IID Data Issue in UAV Logistics

by Chong Zhang, Xiao Liu, Aiting Yao, Jun Bai, Chengzu Dong, Shantanu Pal and Frank Jiang

Drones 2024, 8(7), 312; https://doi.org/10.3390/drones8070312 - 10 Jul 2024

Cited by 3 | Viewed by 2587

Abstract

Artificial intelligence and the Internet of Things (IoT) have brought great convenience to people’s everyday lives. With the emergence of edge computing, IoT devices such as unmanned aerial vehicles (UAVs) can process data instantly at the point of generation, which significantly decreases the [...] Read more.

Artificial intelligence and the Internet of Things (IoT) have brought great convenience to people’s everyday lives. With the emergence of edge computing, IoT devices such as unmanned aerial vehicles (UAVs) can process data instantly at the point of generation, which significantly decreases the requirement for on-board processing power and minimises the data transfer time to enable real-time applications. Meanwhile, with federated learning (FL), UAVs can enhance their intelligent decision-making capabilities by learning from other UAVs without directly accessing their data. This facilitates rapid model iteration and improvement while safeguarding data privacy. However, in many UAV applications such as UAV logistics, different UAVs may perform different tasks and cover different areas, which can result in heterogeneous data and add to the problem of non-independent and identically distributed (Non-IID) data for model training. To address such a problem, we introduce a novel cloud–edge–end collaborative FL framework, which organises and combines local clients through clustering and aggregation. By employing the cosine similarity, we identified and integrated the most appropriate local model into the global model, which can effectively address the issue of Non-IID data in UAV logistics. The experimental results showed that our approach outperformed traditional FL algorithms on two real-world datasets, CIFAR-10 and MNIST. Full article

(This article belongs to the Special Issue Advances of Drones in Logistics)

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18 pages, 7743 KiB

Open AccessEditor’s ChoiceArticle

Design of a UAV Trajectory Prediction System Based on Multi-Flight Modes

by Zhuoyong Shi, Jiandong Zhang, Guoqing Shi, Longmeng Ji, Dinghan Wang and Yong Wu

Drones 2024, 8(6), 255; https://doi.org/10.3390/drones8060255 - 10 Jun 2024

Cited by 6 | Viewed by 2222

Abstract

With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm [...] Read more.

With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm of UAV flight state recognition and trajectory prediction. Presenting an innovative approach focused on improving the precision of unmanned aerial vehicle (UAV) path forecasting via the identification of flight states, this study demonstrates its efficacy through the implementation of two prediction models. Firstly, UAV flight data acquisition was realized in this paper by the use of multi-sensors. Finally, two models for UAV trajectory prediction were designed based on machine learning methods and classical mathematical prediction methods, respectively, and the results before and after flight pattern recognition are compared. The experimental results show that the prediction error of the UAV trajectory prediction method based on multiple flight modes is smaller than the traditional trajectory prediction method in different flight stages. Full article

(This article belongs to the Special Issue AI-Assisted Control Strategies and Their Applications to the Stabilization, Guidance and Navigation of Drones)

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23 pages, 9478 KiB

Open AccessEditor’s ChoiceArticle

Research on Multi-UAV Obstacle Avoidance with Optimal Consensus Control and Improved APF

by Pengfei Zhang, Yin He, Zhongliu Wang, Shujie Li and Qinyang Liang

Drones 2024, 8(6), 248; https://doi.org/10.3390/drones8060248 - 6 Jun 2024

Cited by 7 | Viewed by 2280

Abstract

To address collision challenges between multi-UAVs (unmanned aerial vehicles) during obstacle avoidance, a novel formation control method is proposed. Leveraging the concept of APF (artificial potential field), the proposed approach integrates UAV maneuver constraints with a consensus formation control algorithm, optimizing UAV velocities [...] Read more.

To address collision challenges between multi-UAVs (unmanned aerial vehicles) during obstacle avoidance, a novel formation control method is proposed. Leveraging the concept of APF (artificial potential field), the proposed approach integrates UAV maneuver constraints with a consensus formation control algorithm, optimizing UAV velocities through the particle swarm optimization (PSO) algorithm. The optimal consensus control algorithm is then employed to achieve the optimal convergence rate of the UAV formation. To mitigate the limitations of traditional APF, a collinear force deflection angle is introduced, along with an obstacle avoidance method aimed at preventing UAVs from being trapped in locally optimal solutions. Additionally, an obstacle avoidance algorithm based on virtual force fields between UAVs is designed. Comparative analysis against the basic algorithm demonstrates the effectiveness of the designed optimal consensus algorithm in improving formation convergence performance. Moreover, the improved APF resolves local optimal solution issues, enabling UAVs to effectively navigate around obstacles. Simulation results validate the efficacy of this method in achieving multi-UAV formation control while effectively avoiding obstacles. Full article

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19 pages, 6305 KiB

Open AccessEditor’s ChoiceArticle

Deep Reinforcement Learning-Based 3D Trajectory Planning for Cellular Connected UAV

by Xiang Liu, Weizhi Zhong, Xin Wang, Hongtao Duan, Zhenxiong Fan, Haowen Jin, Yang Huang and Zhipeng Lin

Drones 2024, 8(5), 199; https://doi.org/10.3390/drones8050199 - 15 May 2024

Cited by 6 | Viewed by 2787

Abstract

To address the issue of limited application scenarios associated with connectivity assurance based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep reinforcement learning (DRL) -based three-dimensional (3D) trajectory planning method for cellular unmanned aerial vehicles (UAVs) communication. By considering the [...] Read more.

To address the issue of limited application scenarios associated with connectivity assurance based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep reinforcement learning (DRL) -based three-dimensional (3D) trajectory planning method for cellular unmanned aerial vehicles (UAVs) communication. By considering the 3D space environment and integrating factors such as UAV mission completion time and connectivity, we develop an objective function for path optimization and utilize the advanced dueling double deep Q network (D3QN) to optimize it. Additionally, we introduce the prioritized experience replay (PER) mechanism to enhance learning efficiency and expedite convergence. In order to further aid in trajectory planning, our method incorporates a simultaneous navigation and radio mapping (SNARM) framework that generates simulated 3D radio maps and simulates flight processes by utilizing measurement signals from the UAV during flight, thereby reducing actual flight costs. The simulation results demonstrate that the proposed approach effectively enable UAVs to avoid weak coverage regions in space, thereby reducing the weighted sum of flight time and expected interruption time. 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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18 pages, 1851 KiB

Open AccessEditor’s ChoiceArticle

Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue

by Dan Han, Hao Jiang, Lifang Wang, Xinyu Zhu, Yaqing Chen and Qizhou Yu

Drones 2024, 8(4), 138; https://doi.org/10.3390/drones8040138 - 3 Apr 2024

Cited by 17 | Viewed by 2627

Abstract

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique [...] Read more.

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations. Full article

(This article belongs to the Special Issue Distributed Control, Optimization, and Game of UAV Swarm Systems)

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17 pages, 4983 KiB

Open AccessEditor’s ChoiceArticle

HiFly-Dragon: A Dragonfly Inspired Flapping Flying Robot with Modified, Resonant, Direct-Driven Flapping Mechanisms

by He Ma, Peiyi Gong, Yuqiang Tian, Qingnan Wu, Min Pan, Hao Yin, Youjiang Liu and Chilai Chen

Drones 2024, 8(4), 126; https://doi.org/10.3390/drones8040126 - 28 Mar 2024

Cited by 6 | Viewed by 4238

Abstract

This paper describes a dragonfly-inspired Flapping Wing Micro Air Vehicle (FW-MAV), named HiFly-Dragon. Dragonflies exhibit exceptional flight performance in nature, surpassing most of the other insects, and benefit from their abilities to independently move each of their four wings, including adjusting the flapping [...] Read more.

This paper describes a dragonfly-inspired Flapping Wing Micro Air Vehicle (FW-MAV), named HiFly-Dragon. Dragonflies exhibit exceptional flight performance in nature, surpassing most of the other insects, and benefit from their abilities to independently move each of their four wings, including adjusting the flapping amplitude and the flapping amplitude offset. However, designing and fabricating a flapping robot with multi-degree-of-freedom (multi-DOF) flapping driving mechanisms under stringent size, weight, and power (SWaP) constraints poses a significant challenge. In this work, we propose a compact microrobot dragonfly with four tandem independently controllable wings, which is directly driven by four modified resonant flapping mechanisms integrated on the Printed Circuit Boards (PCBs) of the avionics. The proposed resonant flapping mechanism was tested to be able to enduringly generate 10 gf lift at a frequency of 28 Hz and an amplitude of 180° for a single wing with an external DC power supply, demonstrating the effectiveness of the resonance and durability improvement. All of the mechanical parts were integrated on two PCBs, and the robot demonstrates a substantial weight reduction. The latest prototype has a wingspan of 180 mm, a total mass of 32.97 g, and a total lift of 34 gf. The prototype achieved lifting off on a balance beam, demonstrating that the directly driven robot dragonfly is capable of overcoming self-gravity with onboard batteries. Full article

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25 pages, 3769 KiB

Open AccessEditor’s ChoiceArticle

Harmonized Skies: A Survey on Drone Acceptance across Europe

by Maria Stolz, Anne Papenfuß, Franziska Dunkel and Eva Linhuber

Drones 2024, 8(3), 107; https://doi.org/10.3390/drones8030107 - 20 Mar 2024

Cited by 4 | Viewed by 3772

Abstract

This study investigated the public acceptance of drones in six European countries. For this purpose, an online questionnaire was created, which was completed by 2998 participants. The general attitude towards drones, concerns, approval for different use cases, minimum tolerable flight altitude, acceptable flight [...] Read more.

This study investigated the public acceptance of drones in six European countries. For this purpose, an online questionnaire was created, which was completed by 2998 participants. The general attitude towards drones, concerns, approval for different use cases, minimum tolerable flight altitude, acceptable flight areas, and the impact of personal and demographic attributes on drone acceptance were analyzed. Overall, attitudes towards drones were quite positive in the entire sample and even improved slightly in a second measurement at the end of the questionnaire. However, the results also show that acceptance strongly depends on the use case. Drones for civil and public applications are more widely accepted than those for private and commercial applications. Moreover, the population still has high concerns about privacy and safety. Knowledge about drones, interest in technologies, and age proved essential to predicting acceptance. Thus, tailored communication strategies, for example, through social media, can enhance public awareness and acceptance. Full article

(This article belongs to the Collection Feature Papers of Drones Volume II)

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20 pages, 3022 KiB

Open AccessEditor’s ChoiceArticle

PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8

by Noor Ul Ain Tahir, Zhe Long, Zuping Zhang, Muhammad Asim and Mohammed ELAffendi

Drones 2024, 8(3), 84; https://doi.org/10.3390/drones8030084 - 28 Feb 2024

Cited by 54 | Viewed by 6591

Abstract

In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small [...] Read more.

In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feature extraction for small object detection. Secondly, to address the challenge of missed detections, we opt to integrate the CBAM into the neck of the YOLOv8. Both the channel and the spatial attention modules are used in this addition because of how well they extract feature information flow across the network. Finally, we employ Soft-NMS to improve the accuracy of pedestrian and vehicle detection in occlusion situations. Soft-NMS increases performance and manages overlapped boundary boxes well. The proposed network reduced the fraction of small objects overlooked and enhanced model detection performance. Performance comparisons with different YOLO versions ( for example YOLOv3 extremely small, YOLOv5, YOLOv6, and YOLOv7), YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), and classical object detectors (Faster-RCNN, Cascade R-CNN, RetinaNet, and CenterNet) were used to validate the superiority of the proposed PVswin-YOLOv8s model. The efficiency of the PVswin-YOLOv8s model was confirmed by the experimental findings, which showed a 4.8% increase in average detection accuracy (mAP) compared to YOLOv8s on the VisDrone2019 dataset. Full article

(This article belongs to the Special Issue Advances in Detection and Tracking Applications for Drones and UAM Systems)

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21 pages, 7284 KiB

Open AccessEditor’s ChoiceArticle

Dynamic Scene Path Planning of UAVs Based on Deep Reinforcement Learning

by Jin Tang, Yangang Liang and Kebo Li

Drones 2024, 8(2), 60; https://doi.org/10.3390/drones8020060 - 9 Feb 2024

Cited by 17 | Viewed by 4799

Abstract

Traditional unmanned aerial vehicle path planning methods focus on addressing planning issues in static scenes, struggle to balance optimality and real-time performance, and are prone to local optima. In this paper, we propose an improved deep reinforcement learning approach for UAV path planning [...] Read more.

Traditional unmanned aerial vehicle path planning methods focus on addressing planning issues in static scenes, struggle to balance optimality and real-time performance, and are prone to local optima. In this paper, we propose an improved deep reinforcement learning approach for UAV path planning in dynamic scenarios. Firstly, we establish a task scenario including an obstacle assessment model and model the UAV’s path planning problem using the Markov Decision Process. We translate the MDP model into the framework of reinforcement learning and design the state space, action space, and reward function while incorporating heuristic rules into the action exploration policy. Secondly, we utilize the Q function approximation of an enhanced D3QN with a prioritized experience replay mechanism and design the algorithm’s network structure based on the TensorFlow framework. Through extensive training, we obtain reinforcement learning path planning policies for both static and dynamic scenes and innovatively employ a visualized action field to analyze their planning effectiveness. Simulations demonstrate that the proposed algorithm can accomplish UAV dynamic scene path planning tasks and outperforms classical methods such as A*, RRT, and DQN in terms of planning effectiveness. Full article

(This article belongs to the Special Issue Advances in Quadrotor Unmanned Aerial Vehicles)

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17 pages, 1286 KiB

Open AccessEditor’s ChoiceArticle

On the Dynamics of Flexible Wings for Designing a Flapping-Wing UAV

by Renan Cavenaghi Silva and Douglas D. Bueno

Drones 2024, 8(2), 56; https://doi.org/10.3390/drones8020056 - 7 Feb 2024

Cited by 2 | Viewed by 3369

Abstract

The increasing number of applications involving the use of UAVs has motivated the research for design considerations that increase the safety, endurance, range, and payload capability of these vehicles. In this article, the dynamics of a flexible flapping wing is investigated, focused on [...] Read more.

The increasing number of applications involving the use of UAVs has motivated the research for design considerations that increase the safety, endurance, range, and payload capability of these vehicles. In this article, the dynamics of a flexible flapping wing is investigated, focused on designing bio-inspired UAVs. A dynamic model of the Flapping-Wing UAV is proposed by using 2D beam elements defined in the absolute nodal coordinate formulation, and the flapping is imposed through constraint equations coupled to the equation of motion using Lagrange multipliers. The nodal coordinate trajectories are obtained by integrating the equation of motion using the Runge–Kutta algorithm. The imposed flapping is modulated using a proposed smooth function to reduce transient vibrations at the start of the motion. The results shows that wing flexibility yields significant differences compared to rigid-wing models, depending on the flapping frequency. Limited amplitude of oscillation is obtained when considering a non-resonant flapping strategy, whereas in resonance, the energy levels efficiently increase. The results also demonstrate the influence of different flapping strategies on the energy dissipation, which are relevant to increasing the time of flight. The proposed approach is an interesting alternative for designing flexible, bio-inspired, flapping-wing UAVs. Full article

(This article belongs to the Special Issue Conceptual Design, Modeling, and Control Strategies of Drones-II)

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15 pages, 9694 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Thermal Image Tracking for Search and Rescue Missions with a Drone

by Seokwon Yeom

Drones 2024, 8(2), 53; https://doi.org/10.3390/drones8020053 - 5 Feb 2024

Cited by 15 | Viewed by 9889

Abstract

Infrared thermal imaging is useful for human body recognition for search and rescue (SAR) missions. This paper discusses thermal object tracking for SAR missions with a drone. The entire process consists of object detection and multiple-target tracking. The You-Only-Look-Once (YOLO) detection model is [...] Read more.

Infrared thermal imaging is useful for human body recognition for search and rescue (SAR) missions. This paper discusses thermal object tracking for SAR missions with a drone. The entire process consists of object detection and multiple-target tracking. The You-Only-Look-Once (YOLO) detection model is utilized to detect people in thermal videos. Multiple-target tracking is performed via track initialization, maintenance, and termination. Position measurements in two consecutive frames initialize the track. Tracks are maintained using a Kalman filter. A bounding box gating rule is proposed for the measurement-to-track association. This proposed rule is combined with the statistically nearest neighbor association rule to assign measurements to tracks. The track-to-track association selects the fittest track for a track and fuses them. In the experiments, three videos of three hikers simulating being lost in the mountains were captured using a thermal imaging camera on a drone. Capturing was assumed under difficult conditions; the objects are close or occluded, and the drone flies arbitrarily in horizontal and vertical directions. Robust tracking results were obtained in terms of average total track life and average track purity, whereas the average mean track life was shortened in harsh searching environments. Full article

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

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23 pages, 10401 KiB

Open AccessEditor’s ChoiceArticle

Adaptive AUV Mission Control System Tested in the Waters of Baffin Bay

by Jimin Hwang, Neil Bose, Gina Millar, Craig Bulger, Ginelle Nazareth and Xi Chen

Drones 2024, 8(2), 45; https://doi.org/10.3390/drones8020045 - 1 Feb 2024

Cited by 2 | Viewed by 2652

Abstract

The primary objectives of this paper are to test an adaptive sampling method for an autonomous underwater vehicle, specifically tailored to track a hydrocarbon plume in the water column. An overview of the simulation of the developed applications within the autonomous system is [...] Read more.

The primary objectives of this paper are to test an adaptive sampling method for an autonomous underwater vehicle, specifically tailored to track a hydrocarbon plume in the water column. An overview of the simulation of the developed applications within the autonomous system is presented together with the subsequent validation achieved through field trials in an area of natural oil seeps near to Scott Inlet in Baffin Bay. This builds upon our prior published work in methodological development. The method employed involves an integrated backseat drive of the AUV, which processes in situ sensor data in real time, assesses mission status, and determines the next task. The core of the developed system comprises three modular components—Search, Survey, and Sample—each designed for independent and sequential execution. Results from tests in Baffin Bay demonstrate that the backseat drive operating system successfully accomplished mission goals, recovering water samples at depths of 20 m, 50 m, and 200 m before mission completion and vehicle retrieval. The principal conclusion drawn from these trials underscores the system’s resilience in enhanced decision autonomy and validates its applicability to marine pollutant assessment and mitigation. Full article

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34 pages, 7532 KiB

Open AccessEditor’s ChoiceArticle

Rockfall Analysis from UAV-Based Photogrammetry and 3D Models of a Cliff Area

by Daniele Cirillo, Michelangelo Zappa, Anna Chiara Tangari, Francesco Brozzetti and Fabio Ietto

Drones 2024, 8(1), 31; https://doi.org/10.3390/drones8010031 - 22 Jan 2024

Cited by 30 | Viewed by 6025

Abstract

The application of Unmanned Aerial Vehicles (UAVs), commonly known as drones, in geological, geomorphological, and geotechnical studies has gained significant attention due to their versatility and capability to capture high-resolution data from challenging terrains. This research uses drone-based high-resolution photogrammetry to assess the [...] Read more.

The application of Unmanned Aerial Vehicles (UAVs), commonly known as drones, in geological, geomorphological, and geotechnical studies has gained significant attention due to their versatility and capability to capture high-resolution data from challenging terrains. This research uses drone-based high-resolution photogrammetry to assess the geomechanical properties and rockfall potential of several rock scarps within a wide area of 50 ha. Traditional methods for evaluating geomechanical parameters on rock scarps involve time-consuming field surveys and measurements, which can be hazardous in steep and rugged environments. By contrast, drone photogrammetry offers a safer and more efficient approach, allowing for the creation of detailed 3D models of a cliff area. These models provide valuable insights into the topography, geological structures, and potential failure mechanisms. This research processed the acquired drone imagery using advanced geospatial software to generate accurate orthophotos and digital elevation models. These outputs analysed the key factors contributing to rockfall triggering, including identifying discontinuities, joint orientations, kinematic analysis of failures, and fracturing frequency. More than 8.9 × 10⁷ facets, representing discontinuity planes, were recognised and analysed for the kinematic failure modes, showing that direct toppling is the most abundant rockfall type, followed by planar sliding and flexural toppling. Three different fracturation grades were also identified based on the number of planar facets recognised on rock surfaces. The approach used in this research contributes to the ongoing development of fast, practical, low-cost, and non-invasive techniques for geomechanical assessment on vertical rock scarps. In particular, the results show the effectiveness of drone-based photogrammetry for rapidly collecting comprehensive geomechanical data valid to recognise the prone areas to rockfalls in vast regions. Full article

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14 pages, 4004 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Exploring Meteorological Conditions and Microscale Temperature Inversions above the Great Barrier Reef through Drone-Based Measurements

by Christian Eckert, Kim I. Monteforte, Daniel P. Harrison and Brendan P. Kelaher

Drones 2023, 7(12), 695; https://doi.org/10.3390/drones7120695 - 4 Dec 2023

Cited by 3 | Viewed by 3496

Abstract

Understanding the atmospheric conditions in remote areas contributes to assessing local weather phenomena. Obtaining vertical profiles of the atmosphere in isolated locations can introduce significant challenges for the deployment and maintenance of equipment, as well as regulatory obstacles. Here, we assessed the potential [...] Read more.

Understanding the atmospheric conditions in remote areas contributes to assessing local weather phenomena. Obtaining vertical profiles of the atmosphere in isolated locations can introduce significant challenges for the deployment and maintenance of equipment, as well as regulatory obstacles. Here, we assessed the potential of consumer drones equipped with lightweight atmospheric sensors to collect vertical meteorological profiles off One Tree Island (Great Barrier Reef), located approximately 85 km off the east coast of Australia. We used a DJI Matrice 300 drone with two InterMet Systems iMet-XQ2 UAV sensors, capturing data on atmospheric pressure, temperature, relative humidity, and wind up to an altitude of 1500 m. These flights were conducted three times per day (9 a.m., 12 noon, and 3 p.m.) and compared against ground-based weather sensors. Over the Austral summer/autumn, we completed 72 flights, obtaining 24 complete sets of daily measurements of atmospheric characteristics over the entire vertical profile. On average, the atmospheric temperature and dewpoint temperature were significantly influenced by the time of sampling, and also varied among days. The mean daily temperature and dewpoint temperature reached their peaks at 3 p.m., with the temperature gradually rising from its morning low. The mean dewpoint temperature obtained its lowest point around noon. We also observed wind speed variations, but changes in patterns throughout the day were much less consistent. The drone-mounted atmospheric sensors exhibited a consistent warm bias in temperature compared to the reference weather station. Relative humidity showed greater variability with no clear bias pattern, indicating potential limitations in the humidity sensor’s performance. Microscale temperature inversions were prevalent around 1000 m, peaking around noon and present in approximately 27% of the profiles. Overall, the drone-based vertical profiles helped characterise atmospheric dynamics around One Tree Island Reef and demonstrated the utility of consumer drones in providing cost-effective meteorological information in remote, environmentally sensitive areas. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicles in Atmospheric Research)

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18 pages, 2479 KiB

Open AccessEditor’s ChoiceArticle

Implementation of an Edge-Computing Vision System on Reduced-Board Computers Embedded in UAVs for Intelligent Traffic Management

by Sergio Bemposta Rosende, Sergio Ghisler, Javier Fernández-Andrés and Javier Sánchez-Soriano

Drones 2023, 7(11), 682; https://doi.org/10.3390/drones7110682 - 20 Nov 2023

Cited by 13 | Viewed by 4027

Abstract

Advancements in autonomous driving have seen unprecedented improvement in recent years. This work addresses the challenge of enhancing the navigation of autonomous vehicles in complex urban environments such as intersections and roundabouts through the integration of computer vision and unmanned aerial vehicles (UAVs). [...] Read more.

Advancements in autonomous driving have seen unprecedented improvement in recent years. This work addresses the challenge of enhancing the navigation of autonomous vehicles in complex urban environments such as intersections and roundabouts through the integration of computer vision and unmanned aerial vehicles (UAVs). UAVs, owing to their aerial perspective, offer a more effective means of detecting vehicles involved in these maneuvers. The primary objective is to develop, evaluate, and compare different computer vision models and reduced-board (and small-power) hardware for optimizing traffic management in these scenarios. A dataset was constructed using two sources, several models (YOLO 5 and 8, DETR, and EfficientDetLite) were selected and trained, four reduced-board computers were chosen (Raspberry Pi 3B+ and 4, Jetson Nano, and Google Coral), and the models were tested on these boards for edge computing in UAVs. The experiments considered training times (with the dataset and its optimized version), model metrics were obtained, inference frames per second (FPS) were measured, and energy consumption was quantified. After the experiments, it was observed that the combination that best suits our use case is the YoloV8 model with the Jetson Nano. On the other hand, a combination with much higher inference speed but lower accuracy involves the EfficientDetLite models with the Google Coral board. Full article

(This article belongs to the Special Issue Edge Computing and IoT Technologies for Drones)

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13 pages, 2415 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Drone with Mounted Thermal Infrared Cameras for Monitoring Terrestrial Mammals

by Hanne Lyngholm Larsen, Katrine Møller-Lassesen, Esther Magdalene Ellersgaard Enevoldsen, Sarah Bøgh Madsen, Maria Trier Obsen, Peter Povlsen, Dan Bruhn, Cino Pertoldi and Sussie Pagh

Drones 2023, 7(11), 680; https://doi.org/10.3390/drones7110680 - 18 Nov 2023

Cited by 11 | Viewed by 6554

Abstract

This study investigates the use of a drone equipped with a thermal camera for recognizing wild mammal species in open areas and to determine the sex and age of red deer (Cervus elaphus) and roe deer (Capreolus capreoulus) in [...] Read more.

This study investigates the use of a drone equipped with a thermal camera for recognizing wild mammal species in open areas and to determine the sex and age of red deer (Cervus elaphus) and roe deer (Capreolus capreoulus) in a 13 km² moor in Denmark. Two separate surveys were conducted: (1) To achieve drone images for the identification of mammals, the drone was tested around a bait place with a live wildlife camera that was often visited by European badger (Meles meles), stone marten (Martes foina), European hare (Lepus europaeus), roe deer and cattle (Bos taurus). The thermal images of wild animal species could be distinguished by their body measures when the drone filmed with the camera pointed perpendicular to the ground in an altitude range of 50–120 m. A PCA ordination showed nonoverlapping body characteristics and MANOVA showed that the combined body measures used were significantly distinctive F = 6.8, p < 0.001. The reactions and behavioral responses of the different species to the altitude and noise of the drone were also tested in this place. (2) On a 13 km² moor, a drone was used for a population study of deer. Red deer and roe deer were counted and separated by body measures. Red deer individuals could, at the right altitude, be separated into adults and calves, and males and females. Body length was the most conclusive body measure, and therefore a reference measurement in the field is recommended. The frame thermal images were effective in species recognition and for use in population studies of deer, and are thought to be more time-efficient and less invasive than traditional methods. In autumn, the number of stags and the life stage of red deer, as well as the distribution of deer in different types of vegetation, could be determined. Full article

(This article belongs to the Special Issue Drone-Based Wildlife Protection, Monitoring, and Conservation Management)

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14 pages, 4121 KiB

Open AccessEditor’s ChoiceArticle

QuickNav: An Effective Collision Avoidance and Path-Planning Algorithm for UAS

by Dipraj Debnath, Ahmad Faizul Hawary, Muhammad Iftishah Ramdan, Fernando Vanegas Alvarez and Felipe Gonzalez

Drones 2023, 7(11), 678; https://doi.org/10.3390/drones7110678 - 17 Nov 2023

Cited by 6 | Viewed by 4318

Abstract

Obstacle avoidance is a desirable capability for Unmanned Aerial Systems (UASs)/drones which prevents crashes and reduces pilot fatigue, particularly when operating in the Beyond Visual Line of Sight (BVLOS). In this paper, we present QuickNav, a solution for obstacle detection and avoidance designed [...] Read more.

Obstacle avoidance is a desirable capability for Unmanned Aerial Systems (UASs)/drones which prevents crashes and reduces pilot fatigue, particularly when operating in the Beyond Visual Line of Sight (BVLOS). In this paper, we present QuickNav, a solution for obstacle detection and avoidance designed to function as a pre-planned onboard navigation system for UAS flying in a known obstacle-cluttered environment. Our method uses a geometrical approach and a predefined safe perimeter (square area) based on Euclidean Geometry for the estimation of intercepting points, as a simple and efficient way to detect obstacles. The square region is treated as the restricted zone that the UAS must avoid entering, therefore providing a perimeter for manoeuvring and arriving at the next waypoints. The proposed algorithm is developed in a MATLAB environment and can be easily translated into other programming languages. The proposed algorithm is tested in scenarios with increasing levels of complexity, demonstrating that the QuickNav algorithm is able to successfully and efficiently generate a series of avoiding waypoints. Furthermore, QuickNav produces shorter distances as compared to those of the brute force method and is able to solve difficult obstacle avoidance problems in fractions of the time and distance required by the other methods. QuickNav can be used to improve the safety and efficiency of UAV missions and can be applied to the deployment of UAVs for surveillance, search and rescue, and delivery operations. Full article

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21 pages, 3479 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Burrow-Nesting Seabird Survey Using UAV-Mounted Thermal Sensor and Count Automation

by Jacob Virtue, Darren Turner, Guy Williams, Stephanie Zeliadt, Henry Walshaw and Arko Lucieer

Drones 2023, 7(11), 674; https://doi.org/10.3390/drones7110674 - 13 Nov 2023

Cited by 3 | Viewed by 3690

Abstract

Seabird surveys are used to monitor population demography and distribution and help us understand anthropogenic pressures on seabird species. Burrow-nesting seabirds are difficult to survey. Current ground survey methods are invasive, time-consuming and detrimental to colony health. Data derived from short transects used [...] Read more.

Seabird surveys are used to monitor population demography and distribution and help us understand anthropogenic pressures on seabird species. Burrow-nesting seabirds are difficult to survey. Current ground survey methods are invasive, time-consuming and detrimental to colony health. Data derived from short transects used in ground surveys are extrapolated to derive whole-colony population estimates, which introduces sampling bias due to factors including uneven burrow distribution and varying terrain. We investigate a new survey technique for nocturnally active burrow-nesting seabirds using unoccupied aerial vehicles (UAVs) and thermal sensor technology. We surveyed a three-hectare short-tailed shearwater (Ardenna tenuirostris) colony in Tasmania, Australia. Occupied burrows with resident chicks produced pronounced thermal signatures. This survey method captured a thermal response of every occupied burrow in the colony. Count automation techniques were developed to detect occupied burrows. To validate the results, we compared automated and manual counts of thermal imagery. Automated counts of occupied burrows were 9.3% higher and took approximately 5% of the time needed for manual counts. Using both manual and automated counts, we estimated that there were 5249–5787 chicks for the 2021/2022 breeding season. We provide evidence that high-resolution UAV thermal remote sensing and count automation can improve population estimates of burrow-nesting seabirds. Full article

(This article belongs to the Special Issue Drone Advances in Wildlife Research)

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17 pages, 6989 KiB

Open AccessEditor’s ChoiceArticle

UAV-Based Subsurface Data Collection Using a Low-Tech Ground-Truthing Payload System Enhances Shallow-Water Monitoring

by Aris Thomasberger and Mette Møller Nielsen

Drones 2023, 7(11), 647; https://doi.org/10.3390/drones7110647 - 25 Oct 2023

Cited by 5 | Viewed by 3072

Abstract

Unoccupied Aerial Vehicles (UAVs) are a widely applied tool used to monitor shallow water habitats. A recurrent issue when conducting UAV-based monitoring of submerged habitats is the collection of ground-truthing data needed as training and validation samples for the classification of aerial imagery, [...] Read more.

Unoccupied Aerial Vehicles (UAVs) are a widely applied tool used to monitor shallow water habitats. A recurrent issue when conducting UAV-based monitoring of submerged habitats is the collection of ground-truthing data needed as training and validation samples for the classification of aerial imagery, as well as for the identification of ecologically relevant information such as the vegetation depth limit. To address these limitations, a payload system was developed to collect subsurface data in the form of videos and depth measurements. In a 7 ha large study area, 136 point observations were collected and subsequently used to (1) train and validate the object-based classification of aerial imagery, (2) create a class distribution map based on the interpolation of point observations, (3) identify additional ecological relevant information and (4) create a bathymetry map of the study area. The classification based on ground-truthing samples achieved an overall accuracy of 98% and agreed to 84% with the class distribution map based on point interpolation. Additional ecologically relevant information, such as the vegetation depth limit, was recorded, and a bathymetry map of the study site was created. The findings of this study show that UAV-based shallow-water monitoring can be improved by applying the proposed tool. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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18 pages, 6362 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning-Based Weed Detection Using UAV Images: A Comparative Study

by Tej Bahadur Shahi, Sweekar Dahal, Chiranjibi Sitaula, Arjun Neupane and William Guo

Drones 2023, 7(10), 624; https://doi.org/10.3390/drones7100624 - 7 Oct 2023

Cited by 23 | Viewed by 7787

Abstract

Semantic segmentation has been widely used in precision agriculture, such as weed detection, which is pivotal to increasing crop yields. Various well-established and swiftly evolved AI models have been developed of late for semantic segmentation in weed detection; nevertheless, there is insufficient information [...] Read more.

Semantic segmentation has been widely used in precision agriculture, such as weed detection, which is pivotal to increasing crop yields. Various well-established and swiftly evolved AI models have been developed of late for semantic segmentation in weed detection; nevertheless, there is insufficient information about their comparative study for optimal model selection in terms of performance in this field. Identifying such a model helps the agricultural community make the best use of technology. As such, we perform a comparative study of cutting-edge AI deep learning-based segmentation models for weed detection using an RGB image dataset acquired with UAV, called CoFly-WeedDB. For this, we leverage AI segmentation models, ranging from SegNet to DeepLabV3+, combined with five backbone convolutional neural networks (VGG16, ResNet50, DenseNet121, EfficientNetB0 and MobileNetV2). The results show that UNet with EfficientNetB0 as a backbone CNN is the best-performing model compared with the other candidate models used in this study on the CoFly-WeedDB dataset, imparting Precision (88.20%), Recall (88.97%), F1-score (88.24%) and mean Intersection of Union (56.21%). From this study, we suppose that the UNet model combined with EfficientNetB0 could potentially be used by the concerned stakeholders (e.g., farmers, the agricultural industry) to detect weeds more accurately in the field, thereby removing them at the earliest point and increasing crop yields. Full article

(This article belongs to the Special Issue Drones in Sustainable Agriculture)

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22 pages, 23235 KiB

Open AccessEditor’s ChoiceArticle

Efficient YOLOv7-Drone: An Enhanced Object Detection Approach for Drone Aerial Imagery

by Xiaofeng Fu, Guoting Wei, Xia Yuan, Yongshun Liang and Yuming Bo

Drones 2023, 7(10), 616; https://doi.org/10.3390/drones7100616 - 1 Oct 2023

Cited by 22 | Viewed by 6216

Abstract

In recent years, the rise of low-cost mini rotary-wing drone technology across diverse sectors has emphasized the crucial role of object detection within drone aerial imagery. Low-cost mini rotary-wing drones come with intrinsic limitations, especially in computational power. Drones come with intrinsic limitations, [...] Read more.

In recent years, the rise of low-cost mini rotary-wing drone technology across diverse sectors has emphasized the crucial role of object detection within drone aerial imagery. Low-cost mini rotary-wing drones come with intrinsic limitations, especially in computational power. Drones come with intrinsic limitations, especially in resource availability. This context underscores an urgent need for solutions that synergize low latency, high precision, and computational efficiency. Previous methodologies have primarily depended on high-resolution images, leading to considerable computational burdens. To enhance the efficiency and accuracy of object detection in drone aerial images, and building on the YOLOv7, we propose the Efficient YOLOv7-Drone. Recognizing the common presence of small objects in aerial imagery, we eliminated the less efficient P5 detection head and incorporated the P2 detection head for increased precision in small object detection. To ensure efficient feature relay from the Backbone to the Neck, channels within the CBS module were optimized. To focus the model more on the foreground and reduce redundant computations, the TGM-CESC module was introduced, achieving the generation of pixel-level constrained sparse convolution masks. Furthermore, to mitigate potential data losses from sparse convolution, we embedded the head context-enhanced method (HCEM). Comprehensive evaluation using the VisDrone and UAVDT datasets demonstrated our model’s efficacy and practical applicability. The Efficient Yolov7-Drone achieved state-of-the-art scores while ensuring real-time detection performance. Full article

(This article belongs to the Special Issue Advanced Unmanned System Control and Data Processing)

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18 pages, 4298 KiB

Open AccessEditor’s ChoiceArticle

Large-Sized Multirotor Design: Accurate Modeling with Aerodynamics and Optimization for Rotor Tilt Angle

by Anhuan Xie, Xufei Yan, Weisheng Liang, Shiqiang Zhu and Zheng Chen

Drones 2023, 7(10), 614; https://doi.org/10.3390/drones7100614 - 29 Sep 2023

Cited by 1 | Viewed by 3272

Abstract

Advancements in aerial mobility (AAM) are driven by needs in transportation, logistics, rescue, and disaster relief. Consequently, large-sized multirotor unmanned aerial vehicles (UAVs) with strong power and ample space show great potential. In order to optimize the design process for large-sized multirotors and [...] Read more.

Advancements in aerial mobility (AAM) are driven by needs in transportation, logistics, rescue, and disaster relief. Consequently, large-sized multirotor unmanned aerial vehicles (UAVs) with strong power and ample space show great potential. In order to optimize the design process for large-sized multirotors and reduce physical trial and error, a detailed dynamic model is firstly established, with an accurate aerodynamic model. In addition, the center of gravity (CoG) offset and actuator dynamics are also well considered, which are usually ignored in small-sized multirotors. To improve the endurance and maneuverability of large-sized multirotors, which is the key concern in real applications, a two-loop optimization method for rotor tilt angle design is proposed based on the mathematical model established previously. Its inner loop solves the dynamic equilibrium points to relax the complex dynamic constraints caused by aerodynamics in the overall optimization problem, which improves the solution efficiency. The ideal design results can be obtained through the offline process, which greatly reduces the difficulties of physical trial and error. Finally, various experiments are carried out to demonstrate the accuracy of the established model and the effectiveness of the optimization method. Full article

(This article belongs to the Special Issue Optimal Design, Dynamics, and Navigation of Drones)

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20 pages, 16424 KiB

Open AccessEditor’s ChoiceArticle

Estimating Maize Maturity by Using UAV Multi-Spectral Images Combined with a CCC-Based Model

by Zhao Liu, Huapeng Li, Xiaohui Ding, Xinyuan Cao, Hui Chen and Shuqing Zhang

Drones 2023, 7(9), 586; https://doi.org/10.3390/drones7090586 - 19 Sep 2023

Cited by 3 | Viewed by 2673

Abstract

Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, [...] Read more.

Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, and senescence. The main goal of this study was to estimate maize GMC at maturity through CCC retrieved from multi-spectral UAV images using a PROSAIL model inversion and compare its performance with GMC estimation through simple vegetation indices (VIs) approaches. This study was conducted in two separate maize fields of 50.3 and 56 ha located in Hailun County, Heilongjiang Province, China. Each of the fields was cultivated with two maize varieties. One field was used as reference data for constructing the model, and the other field was applied to validate. The leaf chlorophyll content (LCC) and leaf area index (LAI) of maize were collected at three critical stages of crop growth, and meanwhile, the GMC of maize at maturity was also obtained. During the collection of field data, a UAV flight campaign was performed to obtain multi-spectral images from two fields at three main crop growth stages. In order to calibrate and evaluate the PROSAIL model for obtaining maize CCC, crop canopy spectral reflectance was simulated using crop-specific parameters. In addition, various VIs were computed from multi-spectral images to estimate maize GMC at maturity and compare the results with CCC estimations. When the CCC-retrieved results were compared to measured data, the R² value was 0.704, the RMSE was 34.58 μg/cm², and the MAE was 26.27 μg/cm². The estimation accuracy of the maize GMC based on the normalized red edge index (NDRE) was demonstrated to be the greatest among the selected VIs in both fields, with R² values of 0.6 and 0.619, respectively. Although the VIs of UAV inversion GMC accuracy are lower than those of CCC, their rapid acquisition, high spatial and temporal resolution, suitability for empirical models, and capture of growth differences within the field are still helpful techniques for field-scale crop monitoring. We found that maize varieties are the main reason for the maturity variation of maize under the same geographical and environmental conditions. The method described in this article enables precision agriculture based on UAV remote sensing by giving growers a spatial reference for crop maturity at the field scale. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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15 pages, 3112 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Impacts of Drone Flight Altitude on Behaviors and Species Identification of Marsh Birds in Florida

by Jeremy P. Orange, Ronald R. Bielefeld, William A. Cox and Andrea L. Sylvia

Drones 2023, 7(9), 584; https://doi.org/10.3390/drones7090584 - 16 Sep 2023

Cited by 6 | Viewed by 2953

Abstract

Unmanned aerial vehicles (hereafter drones) are rapidly replacing manned aircraft as the preferred tool used for aerial wildlife surveys, but questions remain about which survey protocols are most effective and least impactful on wildlife behaviors. We evaluated the effects of drone overflights on [...] Read more.

Unmanned aerial vehicles (hereafter drones) are rapidly replacing manned aircraft as the preferred tool used for aerial wildlife surveys, but questions remain about which survey protocols are most effective and least impactful on wildlife behaviors. We evaluated the effects of drone overflights on nontarget species to inform the development of a Florida mottled duck (MODU; Anas fulvigula fulvigula) survey. Our objectives were to (1) evaluate the effect of flight altitude on the behavior of marsh birds, (2) evaluate the effect of altitude on a surveyor’s ability to identify the species of detected birds, and (3) test protocols for upcoming MODU surveys. We flew 120 continuously moving transects at altitudes ranging from 12 to 91 m and modeled variables that influenced detection, species identification, and behavior of nontarget species. Few marsh birds were disturbed during drone flights, but we were unable to confidently detect birds at the two highest altitudes, and we experienced difficulties identifying the species of birds detected in video collected at 30 m. Our findings indicate that MODUs could be surveyed at altitudes as low as 12–30 m with minimal impact to adjacent marsh birds and that larger-bodied nontarget marsh species can be identified from videos collected during MODU drone surveys. Full article

(This article belongs to the Special Issue Drone-Based Wildlife Protection, Monitoring, and Conservation Management)

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34 pages, 20980 KiB

Open AccessEditor’s ChoiceArticle

Go with the Flow: Estimating Wind Using Uncrewed Aircraft

by Marc D. Compere, Kevin A. Adkins and Avinash Muthu Krishnan

Drones 2023, 7(9), 564; https://doi.org/10.3390/drones7090564 - 1 Sep 2023

Cited by 1 | Viewed by 4574

Abstract

This paper presents a fundamentally different approach to wind estimation using Uncrewed Aircraft (UA) than the vast majority of existing methods. This method uses no on-board flow sensor and does not attempt to estimate thrust or drag forces. Using only GPS and orientation [...] Read more.

This paper presents a fundamentally different approach to wind estimation using Uncrewed Aircraft (UA) than the vast majority of existing methods. This method uses no on-board flow sensor and does not attempt to estimate thrust or drag forces. Using only GPS and orientation sensors, the strategy estimates wind vectors in an Earth-fixed frame during turning maneuvers. The method presented here is called the Wind-Arc method. The philosophy behind this method has been seen in practice, but this paper presents an alternative derivation with resulting performance evaluations in simulations and flight tests. The simulations verify the method provides perfect performance under ideal conditions using simulated GPS, heading angle, and satisfied assumptions. When applied to experimental flight test data, the method works and follows both the airspeed and wind speed trends, but improvements can still be made. Wind triangles are displayed at each instant in time along the flight path that illustrate the graphical nature of the approach and solution. Future work will include wind gust estimation and a Quality of Estimate (QoE) metric to determine what conditions provide good wind speed estimates while preserving the method’s generality and simplicity. Full article

(This article belongs to the Special Issue Weather Impacts on Uncrewed Aircraft)

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25 pages, 1672 KiB

Open AccessEditor’s ChoiceArticle

Drone-Based Environmental Emergency Response in the Brazilian Amazon

by Janiele Custodio and Hernan Abeledo

Drones 2023, 7(9), 554; https://doi.org/10.3390/drones7090554 - 27 Aug 2023

Cited by 2 | Viewed by 3083

Abstract

This paper introduces a location–allocation model to support environmental emergency response strategic planning using a drone-based network. Drones are used to verify potential emergencies, gathering additional information to support emergency response missions when time and resources are limited. The resulting discrete facility location–allocation [...] Read more.

This paper introduces a location–allocation model to support environmental emergency response strategic planning using a drone-based network. Drones are used to verify potential emergencies, gathering additional information to support emergency response missions when time and resources are limited. The resulting discrete facility location–allocation model with mobile servers assumes a centralized network operated out of sight by first responders and government agents. The optimization problem seeks to find the minimal cost configuration that meets operational constraints and performance objectives. To test the practical applicability of the proposed model, a real-life case study was implemented for the municipality of Ji-Paraná, in the Brazilian Amazon, using demand data from a mobile whistle-blower application and from satellite imagery projects that monitor deforestation and fire incidents in the region. Experiments are performed to understand the model’s sensitivity to various demand scenarios and capacity restrictions. Full article

(This article belongs to the Special Issue UAV IoT Sensing and Networking)

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21 pages, 7349 KiB

Open AccessEditor’s ChoiceArticle

Stepwise Soft Actor–Critic for UAV Autonomous Flight Control

by Ha Jun Hwang, Jaeyeon Jang, Jongkwan Choi, Jung Ho Bae, Sung Ho Kim and Chang Ouk Kim

Drones 2023, 7(9), 549; https://doi.org/10.3390/drones7090549 - 24 Aug 2023

Cited by 3 | Viewed by 3812

Abstract

Despite the growing demand for unmanned aerial vehicles (UAVs), the use of conventional UAVs is limited, as most of them require being remotely operated by a person who is not within the vehicle’s field of view. Recently, many studies have introduced reinforcement learning [...] Read more.

Despite the growing demand for unmanned aerial vehicles (UAVs), the use of conventional UAVs is limited, as most of them require being remotely operated by a person who is not within the vehicle’s field of view. Recently, many studies have introduced reinforcement learning (RL) to address hurdles for the autonomous flight of UAVs. However, most previous studies have assumed overly simplified environments, and thus, they cannot be applied to real-world UAV operation scenarios. To address the limitations of previous studies, we propose a stepwise soft actor–critic (SeSAC) algorithm for efficient learning in a continuous state and action space environment. SeSAC aims to overcome the inefficiency of learning caused by attempting challenging tasks from the beginning. Instead, it starts with easier missions and gradually increases the difficulty level during training, ultimately achieving the final goal. We also control a learning hyperparameter of the soft actor–critic algorithm and implement a positive buffer mechanism during training to enhance learning effectiveness. Our proposed algorithm was verified in a six-degree-of-freedom (DOF) flight environment with high-dimensional state and action spaces. The experimental results demonstrate that the proposed algorithm successfully completed missions in two challenging scenarios, one for disaster management and another for counter-terrorism missions, while surpassing the performance of other baseline approaches. Full article

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

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22 pages, 11112 KiB

Open AccessEditor’s ChoiceArticle

An Evaluation of Sun-Glint Correction Methods for UAV-Derived Secchi Depth Estimations in Inland Water Bodies

by Edvinas Tiškus, Martynas Bučas, Diana Vaičiūtė, Jonas Gintauskas and Irma Babrauskienė

Drones 2023, 7(9), 546; https://doi.org/10.3390/drones7090546 - 23 Aug 2023

Cited by 6 | Viewed by 2763

Abstract

This study investigates the application of unoccupied aerial vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies—Hedley, Goodman, Lyzenga, Joyce, and threshold-removed glint—to [...] Read more.

This study investigates the application of unoccupied aerial vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies—Hedley, Goodman, Lyzenga, Joyce, and threshold-removed glint—to model the SD values derived from UAV multispectral imagery, highlighting the role of reflectance accuracy and algorithmic precision in SD modeling. While Goodman’s method showed a higher correlation (0.92) with in situ SD measurements, Hedley’s method exhibited the smallest average deviation (0.65 m), suggesting its potential in water resource management, environmental monitoring, and ecological modeling. The study also underscored the quasi-analytical algorithm (QAA) potential in estimating SD due to its flexibility to process data from various sensors without requiring in situ measurements, offering scalability for large-scale water quality surveys. The accuracy of SD measures calculated using QAA was related to variability in water constituents of colored dissolved organic matter and the solar zenith angle. A practical workflow for SD acquisition using UAVs and multispectral data is proposed for monitoring inland water bodies. Full article

(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)

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23 pages, 46577 KiB

Open AccessEditor’s ChoiceArticle

Drone-YOLO: An Efficient Neural Network Method for Target Detection in Drone Images

by Zhengxin Zhang

Drones 2023, 7(8), 526; https://doi.org/10.3390/drones7080526 - 11 Aug 2023

Cited by 113 | Viewed by 20863

Abstract

Object detection in unmanned aerial vehicle (UAV) imagery is a meaningful foundation in various research domains. However, UAV imagery poses unique challenges, including large image sizes, small sizes detection objects, dense distribution, overlapping instances, and insufficient lighting impacting the effectiveness of object detection. [...] Read more.

Object detection in unmanned aerial vehicle (UAV) imagery is a meaningful foundation in various research domains. However, UAV imagery poses unique challenges, including large image sizes, small sizes detection objects, dense distribution, overlapping instances, and insufficient lighting impacting the effectiveness of object detection. In this article, we propose Drone-YOLO, a series of multi-scale UAV image object detection algorithms based on the YOLOv8 model, designed to overcome the specific challenges associated with UAV image object detection. To address the issues of large scene sizes and small detection objects, we introduce improvements to the neck component of the YOLOv8 model. Specifically, we employ a three-layer PAFPN structure and incorporate a detection head tailored for small-sized objects using large-scale feature maps, significantly enhancing the algorithm’s capability to detect small-sized targets. Furthermore, we integrate the sandwich-fusion module into each layer of the neck’s up–down branch. This fusion mechanism combines network features with low-level features, providing rich spatial information about the objects at different layer detection heads. We achieve this fusion using depthwise separable evolution, which balances parameter costs and a large receptive field. In the network backbone, we employ RepVGG modules as downsampling layers, enhancing the network’s ability to learn multi-scale features and outperforming traditional convolutional layers. The proposed Drone-YOLO methods have been evaluated in ablation experiments and compared with other state-of-the-art approaches on the VisDrone2019 dataset. The results demonstrate that our Drone-YOLO (large) outperforms other baseline methods in the accuracy of object detection. Compared to YOLOv8, our method achieves a significant improvement in

m A P_{0.5}

metrics, with a 13.4% increase on the VisDrone2019-test and a 17.40% increase on the VisDrone2019-val. Additionally, the parameter-efficient Drone-YOLO (tiny) with only 5.25 M parameters performs equivalently or better than the baseline method with 9.66M parameters on the dataset. These experiments validate the effectiveness of the Drone-YOLO methods in the task of object detection in drone imagery. Full article

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24 pages, 10632 KiB

Open AccessEditor’s ChoiceArticle

Automatic Real-Time Creation of Three-Dimensional (3D) Representations of Objects, Buildings, or Scenarios Using Drones and Artificial Intelligence Techniques

by Jorge Cujó Blasco, Sergio Bemposta Rosende and Javier Sánchez-Soriano

Drones 2023, 7(8), 516; https://doi.org/10.3390/drones7080516 - 5 Aug 2023

Cited by 2 | Viewed by 5750

Abstract

This work presents the development and evaluation of a real-time 3D reconstruction system using drones. The system leverages innovative artificial intelligence techniques in photogrammetry and computer vision (CDS-MVSNet and DROID-SLAM) to achieve the accurate and efficient reconstruction of 3D environments. By integrating vision, [...] Read more.

This work presents the development and evaluation of a real-time 3D reconstruction system using drones. The system leverages innovative artificial intelligence techniques in photogrammetry and computer vision (CDS-MVSNet and DROID-SLAM) to achieve the accurate and efficient reconstruction of 3D environments. By integrating vision, navigation, and 3D reconstruction subsystems, the proposed system addresses the limitations of existing applications and software in terms of speed and accuracy. The project encountered challenges related to scheduling, resource availability, and algorithmic complexity. The obtained results validate the applicability of the system in real-world scenarios and open avenues for further research in diverse areas. One of the tests consisted of a one-minute-and-three-second flight around a small figure, while the reconstruction was performed in real time. The reference Meshroom software completed the 3D reconstruction in 136 min and 12 s, while the proposed system finished the process in just 1 min and 13 s. This work contributes to the advancement in the field of 3D reconstruction using drones, benefiting from advancements in technology and machine learning algorithms. Full article

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

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16 pages, 1362 KiB

Open AccessEditor’s ChoiceArticle

DECCo-A Dynamic Task Scheduling Framework for Heterogeneous Drone Edge Cluster

by Zhiyang Zhang, Die Wu, Fengli Zhang and Ruijin Wang

Drones 2023, 7(8), 513; https://doi.org/10.3390/drones7080513 - 3 Aug 2023

Cited by 4 | Viewed by 2602

Abstract

The heterogeneity of unmanned aerial vehicle (UAV) nodes and the dynamic service demands make task scheduling particularly complex in the drone edge cluster (DEC) scenario. In this paper, we provide a universal intelligent collaborative task scheduling framework, named DECCo, which schedules dynamically changing [...] Read more.

The heterogeneity of unmanned aerial vehicle (UAV) nodes and the dynamic service demands make task scheduling particularly complex in the drone edge cluster (DEC) scenario. In this paper, we provide a universal intelligent collaborative task scheduling framework, named DECCo, which schedules dynamically changing task requests for the heterogeneous DEC. Benefiting from the latest advances in deep reinforcement learning (DRL), DECCo autonomously learns task scheduling strategies with high response rates and low communication latency through a collaborative Advantage Actor–Critic algorithm, which avoids the interference of resource overload and local downtime while ensuring load balancing. To better adapt to the real drone collaborative scheduling scenario, DECCo switches between heuristic and DRL-based scheduling solutions based on real-time scheduling performance, thus avoiding suboptimal decisions that severely affect Quality of Service (QoS) and Quality of Experience (QoE). With flexible parameter control, DECCo can adapt to various task requests on drone edge clusters. Google Cluster Usage Traces are used to verify the effectiveness of DECCo. Therefore, our work represents a state-of-the-art method for task scheduling in the heterogeneous DEC. Full article

(This article belongs to the Special Issue UAV-Assisted Internet of Things)

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24 pages, 23578 KiB

Open AccessEditor’s ChoiceArticle

Digital Recording of Historical Defensive Structures in Mountainous Areas Using Drones: Considerations and Comparisons

by Luigi Barazzetti, Mattia Previtali, Lorenzo Cantini and Annunziata Maria Oteri

Drones 2023, 7(8), 512; https://doi.org/10.3390/drones7080512 - 3 Aug 2023

Cited by 5 | Viewed by 2318

Abstract

Digital recording of historic buildings and sites in mountainous areas could be challenging. The paper considers and discusses the case of historical defensive structures in the Italian Alps, designed and built to be not accessible. Drone images and photogrammetric techniques for 3D modeling [...] Read more.

Digital recording of historic buildings and sites in mountainous areas could be challenging. The paper considers and discusses the case of historical defensive structures in the Italian Alps, designed and built to be not accessible. Drone images and photogrammetric techniques for 3D modeling play a fundamental role in the digital documentation of fortified constructions with non-contact techniques. This manuscript describes the use of drones for reconstructing the external surfaces of some fortified structures using traditional photogrammetric/SfM solutions and novel methods based on NeRFs. The case of direct orientation based on PPK and traditional GCPs placed on the ground is also discussed, considering the difficulties in placing and measuring control points in such environments. Full article

(This article belongs to the Special Issue Digital Twins and Extended Reality: Opportunities and Challenges of Integrated Applications)

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27 pages, 683 KiB

Open AccessEditor’s ChoiceArticle

Comparative Analysis of Nonlinear Programming Solvers: Performance Evaluation, Benchmarking, and Multi-UAV Optimal Path Planning

by Giovanni Lavezzi, Kidus Guye, Venanzio Cichella and Marco Ciarcià

Drones 2023, 7(8), 487; https://doi.org/10.3390/drones7080487 - 25 Jul 2023

Cited by 6 | Viewed by 3181

Abstract

In this paper, we propose a set of guidelines to select a solver for the solution of nonlinear programming problems. We conduct a comparative analysis of the convergence performances of commonly used solvers for both unconstrained and constrained nonlinear programming problems. The comparison [...] Read more.

In this paper, we propose a set of guidelines to select a solver for the solution of nonlinear programming problems. We conduct a comparative analysis of the convergence performances of commonly used solvers for both unconstrained and constrained nonlinear programming problems. The comparison metrics involve accuracy, convergence rate, and computational time. MATLAB is chosen as the implementation platform due to its widespread adoption in academia and industry. Our study includes solvers which are either freely available or require a license, or are extensively documented in the literature. Moreover, we differentiate solvers if they allow the selection of different optimal search methods. We assess the performance of 24 algorithms on a set of 60 benchmark problems. We also evaluate the capability of each solver to tackle two large-scale UAV optimal path planning scenarios, specifically the 3D minimum time problem for UAV landing and the 3D minimum time problem for UAV formation flying. To enrich our analysis, we discuss the effects of each solver’s inner settings on accuracy, convergence rate, and computational time. Full article

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32 pages, 20812 KiB

Open AccessEditor’s ChoiceArticle

Digital Twin Development for the Airspace of the Future

by Toufik Souanef, Saba Al-Rubaye, Antonios Tsourdos, Samuel Ayo and Dimitrios Panagiotakopoulos

Drones 2023, 7(7), 484; https://doi.org/10.3390/drones7070484 - 23 Jul 2023

Cited by 13 | Viewed by 6054

Abstract

The UK aviation industry is committed to achieving net zero emissions by 2050 through sustainable measures and one of the key aspects of this effort is the implementation of Unmanned Traffic Management (UTM) systems. These UTM systems play a crucial role in enabling [...] Read more.

The UK aviation industry is committed to achieving net zero emissions by 2050 through sustainable measures and one of the key aspects of this effort is the implementation of Unmanned Traffic Management (UTM) systems. These UTM systems play a crucial role in enabling the safe and efficient integration of unmanned aerial vehicles (UAVs) into the airspace. As part of the Airspace of the Future (AoF) project, the development and implementation of UTM services have been prioritised. This paper aims to create an environment where routine drone services can operate safely and effectively. To facilitate this, a digital twin of the National Beyond Visual Line of Sight Experimentation Corridor has been created. This digital twin serves as a virtual replica of the corridor and allows for the synthetic testing of unmanned traffic management concepts. The implementation of the digital twin involves both simulated and hybrid flights with real drones. Simulated flights allow for the testing and refinement of UTM services in a controlled environment. Hybrid flights, on the other hand, involve the integration of real drones into the airspace to assess their performance and compatibility with the UTM systems. By leveraging the capabilities of UTM systems and utilising the digital twin for testing, the AoF project aims to advance the development of safer and more efficient drone operations. The Experimentation Corridor has been developed to simulate and test concepts related to managing unmanned traffic. The paper provides a detailed account of the implementation of the digital twin for the AoF project, including simulated and hybrid flights involving real drones. Full article

(This article belongs to the Section Drone Communications)

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22 pages, 19025 KiB

Open AccessEditor’s ChoiceArticle

Flow Structure around a Multicopter Drone: A Computational Fluid Dynamics Analysis for Sensor Placement Considerations

by Mauro Ghirardelli, Stephan T. Kral, Nicolas Carlo Müller, Richard Hann, Etienne Cheynet and Joachim Reuder

Drones 2023, 7(7), 467; https://doi.org/10.3390/drones7070467 - 13 Jul 2023

Cited by 9 | Viewed by 5784

Abstract

This study presents a computational fluid dynamics (CFD) based approach to determine the optimal positioning for an atmospheric turbulence sensor on a rotary-wing uncrewed aerial vehicle (UAV) with X8 configuration. The vertical (

z_{B F}

) and horizontal (

x_{B F}

[...] Read more.

This study presents a computational fluid dynamics (CFD) based approach to determine the optimal positioning for an atmospheric turbulence sensor on a rotary-wing uncrewed aerial vehicle (UAV) with X8 configuration. The vertical (

z_{B F}

) and horizontal (

x_{B F}

) distances of the sensor to the UAV center to reduce the effect of the propeller-induced flow are investigated by CFD simulations based on the

k - ϵ

turbulence model and the actuator disc theory. To ensure a realistic geometric design of the simulations, the tilt angles of a test UAV in flight were measured by flying the drone along a fixed pattern at different constant ground speeds. Based on those measurement results, a corresponding geometry domain was generated for the CFD simulations. Specific emphasis was given to the mesh construction followed by a sensitivity study on the mesh resolution to find a compromise between acceptable simulation accuracy and available computational resources. The final CFD simulations (twelve in total) were performed for four inflow conditions (2.5 m s⁻¹, 5 m s⁻¹, 7.5 m s⁻¹ and 10 m s⁻¹) and three payload configurations (15 kg, 20 kg and 25 kg) of the UAV. The results depend on the inflows and show that the most efficient way to reduce the influence of the propeller-induced flow is mounting the sensor upwind, pointing along the incoming flow direction at

x_{B F}

varying between 0.46 and 1.66 D, and under the mean plane of the rotors at

z_{B F}

between 0.01 and 0.7 D. Finally, results are then applied to the possible real-case scenario of a Foxtech D130 carrying a CSAT3B ultrasonic anemometer, that aims to sample wind with mean flows higher than 5 m s⁻¹. The authors propose

x_{B F} = 1.7 m

and

z_{B F} = 20 c m

below the mean rotor plane as a feasible compromise between propeller-induced flow reduction and safety. These results will be used to improve the design of a novel drone-based atmospheric turbulence measurement system, which aims to combine accurate wind and turbulence measurements by a research-grade ultrasonic anemometer with the high mobility and flexibility of UAVs as sensor carriers. Full article

(This article belongs to the Special Issue Weather Impacts on Uncrewed Aircraft)

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