Drones

11 pages, 1933 KiB

Open AccessArticle

Limited Impact of Drone Transport of Blood on Platelet Activation

by Nico Callewaert, Inge Pareyn, Tessa Acke, Brian Desplinter, Kyana Van de Pitte, Joke Van Vooren, Mathieu De Meyer, Ellen Seeldraeyers, Frank Peeters, Simon F De Meyer, Karen Vanhoorelbeke and Claudia Tersteeg

Drones 2024, 8(12), 752; https://doi.org/10.3390/drones8120752 - 12 Dec 2024

Abstract

The rapid transportation of blood samples and blood products using drones has high potential in the medical sector. However, before this can be implemented, sufficient evidence that drone transportation is not affecting clinical outcomes is needed. Currently, limited data on the stability of [...] Read more.

The rapid transportation of blood samples and blood products using drones has high potential in the medical sector. However, before this can be implemented, sufficient evidence that drone transportation is not affecting clinical outcomes is needed. Currently, limited data on the stability of blood, and specifically on platelet activation, after transport using drones are available. Therefore, the impact of drone transportation on platelet activation parameters was analyzed. Blood was drawn from 20 healthy volunteers and lactate dehydrogenase (LDH), potassium, free hemoglobin, PFA-100 closure time, platelet factor 4 (PF4) plasma levels and platelet activation membrane markers were determined in blood that was transported by drone and compared to non-transported samples. In addition, a control group was included where blood samples were transported by car. Transport by both drone and car increased LDH and potassium levels, but the values mostly remained within the total allowable error. Both drone and car transportation impacted platelet activation, as indicated by a small increase in the baseline P-selectin expression and increased PF4 plasma levels. To our knowledge, this is the first study assessing the impact of drone transport on platelet activation. Transportation of blood tubes from healthy individuals using drones has only minimal impacts on blood stability and platelet activation parameters, and is comparable to blood transportation by car. Therefore, the effects observed as a result of drone transportation will likely not impact clinical decision making. Full article

(This article belongs to the Special Issue Application of Drones in Medicine and Healthcare)

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15 pages, 6009 KiB

Open AccessArticle

Positioning Method for Unmanned Aerial Vehicle (UAV) Based on Airborne Two-Dimensional Laser Doppler Velocimeter: Experiment and Dead Reckoning

by Lanjian Chen, Chongbin Xi, Shilong Jin and Jian Zhou

Drones 2024, 8(12), 751; https://doi.org/10.3390/drones8120751 - 12 Dec 2024

Abstract

In the autonomous navigation of drones, improving positioning accuracy is of significant importance to obtain highly accurate information on flight velocity. Traditional microwave and acoustic velocity measurement methods have the disadvantages of poor precision and susceptibility to interference. In this study, an unmanned [...] Read more.

In the autonomous navigation of drones, improving positioning accuracy is of significant importance to obtain highly accurate information on flight velocity. Traditional microwave and acoustic velocity measurement methods have the disadvantages of poor precision and susceptibility to interference. In this study, an unmanned aerial vehicle (UAV)-mounted two-dimensional laser Doppler velocimeter was developed and investigated, and a relevant drone flight navigation and positioning experiment was carried out. The UAV-mounted two-dimensional laser Doppler velocimeter (LDV) prototype developed in this study applies a scheme of dual-beam measurement light, sharing a focusing lens group. After process integration, the performance of the prototype was measured. It shows that a velocity measurement effect with a high signal-to-noise ratio can be achieved by using two measurement probe beams within a working distance range of 40 m–60 m. In the flight experiment, the flight trajectory calculated using the LDV-measured velocity data was compared with the global navigation satellite system (GNSS)-recorded trajectory. The result shows that LDV can achieve an odometer accuracy of 4.8‰. This study has validated the feasibility of the laser Doppler velocimeter in drone navigation and positioning, providing a novel method for reliable and high-precision velocity measurement in autonomous drone navigation. Full article

(This article belongs to the Special Issue Drones Navigation and Orientation)

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28 pages, 30709 KiB

Open AccessArticle

Drone-Enabled AI Edge Computing and 5G Communication Network for Real-Time Coastal Litter Detection

by Sarun Duangsuwan and Phoowadon Prapruetdee

Drones 2024, 8(12), 750; https://doi.org/10.3390/drones8120750 - 12 Dec 2024

Abstract

Coastal litter is a severe environmental issue impacting marine ecosystems and coastal communities in Thailand, with plastic pollution posing one of the most urgent challenges. Every month, millions of tons of plastic waste enter the ocean, where items such as bottles, cans, and [...] Read more.

Coastal litter is a severe environmental issue impacting marine ecosystems and coastal communities in Thailand, with plastic pollution posing one of the most urgent challenges. Every month, millions of tons of plastic waste enter the ocean, where items such as bottles, cans, and other plastics can take hundreds of years to degrade, threatening marine life through ingestion, entanglement, and habitat destruction. To address this issue, we deploy drones equipped with high-resolution cameras and sensors to capture detailed coastal imagery for assessing litter distribution. This study presents the development of an AI-driven coastal litter detection system using edge computing and 5G communication networks. The AI edge server utilizes YOLOv8 and a recurrent neural network (RNN) to enable the drone to detect and classify various types of litter, such as bottles, cans, and plastics, in real-time. High-speed 5G communication supports seamless data transmission, allowing efficient monitoring. We evaluated drone performance under optimal flying heights above ground of 5 m, 7 m, and 10 m, analyzing accuracy, precision, recall, and F1-score. Results indicate that the system achieves optimal detection at an altitude of 5 m with a ground sampling distance (GSD) of 0.98 cm/pixel, yielding an F1-score of 98% for cans, 96% for plastics, and 95% for bottles. This approach facilitates real-time monitoring of coastal areas, contributing to marine ecosystem conservation and environmental sustainability. Full article

(This article belongs to the Special Issue Detection, Identification and Tracking of UAVs and Drones)

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22 pages, 6400 KiB

Open AccessArticle

A Novel Spherical Shortest Path Planning Method for UAVs

by Fan Liu, Pengchuan Wang, Aniruddha Bhattacharjya and Qianmu Li

Drones 2024, 8(12), 749; https://doi.org/10.3390/drones8120749 - 12 Dec 2024

Abstract

As a central subdivision of the low-altitude economy industry, industrial and consumer drones have broad market application prospects and are becoming the primary focus of the low-altitude economy; however, with increasing aircraft density, effective planning of reasonable flight paths and avoiding conflicts between [...] Read more.

As a central subdivision of the low-altitude economy industry, industrial and consumer drones have broad market application prospects and are becoming the primary focus of the low-altitude economy; however, with increasing aircraft density, effective planning of reasonable flight paths and avoiding conflicts between flight paths have become critical issues in UAV clustering. Current UAV path planning often concentrates on 2D and 3D realistic scenes, which do not meet the actual requirements of realistic spherical paths. This paper has proposed a Gradient-Based Optimization algorithm based on the State Transition function (STGBO) to address the spherical path planning problem for UAV clusters. The state transition function is applied on the scale of medium and high-dimensional cities, balancing the stability and efficiency of the algorithm. Through evolution and comparisons with many mainstream meta-heuristic algorithms, STGBO has demonstrated superior performance and effectiveness in solving Medium-Altitude Unmanned Aerial Vehicle (MUAV) path planning problems on three-dimensional spherical surfaces, contributing to the development of the low-altitude economy. Full article

(This article belongs to the Special Issue Emerging Technologies and Innovations in Unmanned Aerial Vehicle Control Systems)

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23 pages, 7072 KiB

Open AccessArticle

Prescribed-Time Cooperative Guidance Law for Multi-UAV with Intermittent Communication

by Wenhui Ma and Xiaowen Guo

Drones 2024, 8(12), 748; https://doi.org/10.3390/drones8120748 - 11 Dec 2024

Abstract

Networked cooperation of multi-unmanned-aerial-vehicle (UAV) is significant, but their cooperative guidance presents challenges due to weak cross-domain communication. Given the difficulties in information transmission security, this paper proposes a prescribed-time cooperative guidance law (PTCGL) for networked multiple UAVs with intermittent communication. Supported by [...] Read more.

Networked cooperation of multi-unmanned-aerial-vehicle (UAV) is significant, but their cooperative guidance presents challenges due to weak cross-domain communication. Given the difficulties in information transmission security, this paper proposes a prescribed-time cooperative guidance law (PTCGL) for networked multiple UAVs with intermittent communication. Supported by the directed internal communication, the proposed PTCGL can ensure the simultaneous arrival in the case of the pinning external communication is time-triggered intermittent. In the first stage, PTCGL is designed to combine with a time-scaling function and second-order intermittent pinning consensus. With the desired convergence performance, the convergence time can be pre-specified independent of initial conditions and parameter tuning in theory. In the second stage, begin with the suitable initial conditions at the prescribed convergence time, the multiple UAVs are organized by proportional navigation guidance, so as to ensure the cooperation regardless of weak communication. Finally, simulations are conducted to verify the effectiveness and robustness of the proposed cooperative guidance law. Full article

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

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41 pages, 16704 KiB

Open AccessArticle

Fast and Intelligent Proportional–Integral–Derivative (PID) Attitude Control of Quadrotor and Dual-Rotor Coaxial Unmanned Aerial Vehicles (UAVs) Based on All-True Composite Motion

by Zhen Wang, Qi Yuan, Yi Zhu, Yifan Hu, Heng Chen, Xingbo Xie and Wenbin Gu

Drones 2024, 8(12), 747; https://doi.org/10.3390/drones8120747 - 10 Dec 2024

Abstract

The construction of a six-degree-of-freedom (6-DOF) model for the composite motion of the actual mechanical structure (defined as an all-true composite motion model) of unmanned aerial vehicles (UAVs) is a prerequisite for achieving stable control of rotorcraft UAVs. Therefore, this paper proposes a [...] Read more.

The construction of a six-degree-of-freedom (6-DOF) model for the composite motion of the actual mechanical structure (defined as an all-true composite motion model) of unmanned aerial vehicles (UAVs) is a prerequisite for achieving stable control of rotorcraft UAVs. Therefore, this paper proposes a construction approach for a nonlinear 6-DOF model of quadrotor and dual-rotor coaxial UAVs based on all-true composite motion. Two types of attitude–altitude control systems for rotorcraft based on a self-optimizing intelligent proportional–integral–derivative (PID) control method are constructed. Three-dimensional geometric models of the two rotorcraft types, incorporating their physical characteristics, are built. The attitude responses to different pulse width modulation (PWM) inputs are tested, thereby verifying the accuracy of the all-true composite model and analyzing the stability of the two types of UAVs. Furthermore, two types of attitude–altitude control inner loop controllers are designed, and the intelligent PID control algorithm is used to optimize the control parameters. Further verification of the robustness of the optimized parameters is carried out, and the designed attitude controllers are verified via experiment using a turntable. The simulation and experimental results show that the proposed all-true composite motion model and controller design method can accurately simulate the dynamic characteristics of the two types of UAVs and maintain stable attitude control, thus providing a valuable reference for the accurate attitude control of rotorcraft UAVs based on all-true composite motion. Full article

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14 pages, 5971 KiB

Open AccessArticle

Flight Altitude and Sensor Angle Affect Unmanned Aerial System Cotton Plant Height Assessments

by Oluwatola Adedeji, Alwaseela Abdalla, Bishnu Ghimire, Glen Ritchie and Wenxuan Guo

Drones 2024, 8(12), 746; https://doi.org/10.3390/drones8120746 - 10 Dec 2024

Abstract

Plant height is a critical biophysical trait indicative of plant growth and developmental conditions and is valuable for biomass estimation and crop yield prediction. This study examined the effects of flight altitude and camera angle in quantifying cotton plant height using unmanned aerial [...] Read more.

Plant height is a critical biophysical trait indicative of plant growth and developmental conditions and is valuable for biomass estimation and crop yield prediction. This study examined the effects of flight altitude and camera angle in quantifying cotton plant height using unmanned aerial system (UAS) imagery. This study was conducted in a field with a sub-surface irrigation system in Lubbock, Texas, between 2022 and 2023. Images using the DJI Phantom 4 RTKs were collected at two altitudes (40 m and 80 m) and three sensor angles (45°, 60°, and 90°) at different growth stages. The resulting images depicted six scenarios of UAS altitudes and camera angles. The derived plant height was subsequently calculated as the vertical difference between the apical region of the plant and the ground elevation. Linear regression compared UAS-derived heights to manual measurements from 96 plots. Lower altitudes (40 m) outperformed higher altitudes (80 m) across all dates. For the early season (4 July 2023), the 40 m altitude had r² = 0.82–0.86 and RMSE = 2.02–2.16 cm compared to 80 m (r² = 0.66–0.68, RMSE = 7.52–8.76 cm). Oblique angles (45°) yielded higher accuracy than nadir (90°) images, especially in the late season (24 October 2022) results (r² = 0.96, RMSE = 2.95 cm vs. r² = 0.92, RMSE = 3.54 cm). These findings guide optimal UAS parameters for plant height measurement. Full article

(This article belongs to the Special Issue Advances of UAV Remote Sensing for Plant Phenology)

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32 pages, 12104 KiB

Open AccessArticle

RBFNN-Based Adaptive Fixed-Time Sliding Mode Tracking Control for Coaxial Hybrid Aerial–Underwater Vehicles Under Multivariant Ocean Disturbances

by Mingqing Lu, Wei Yang, Zhenyu Xiong, Fei Liao, Shichong Wu, Yumin Su and Wenhua Wu

Drones 2024, 8(12), 745; https://doi.org/10.3390/drones8120745 - 10 Dec 2024

Abstract

In this study, the design of an adaptive neural network-based fixed-time control system for a novel coaxial trans-domain hybrid aerial–underwater vehicle (HAUV) is investigated. A radial basis function neural network (RBFNN) approximation strategy-based adaptive fixed-time terminal sliding mode control (AFTSMC) scheme is proposed [...] Read more.

In this study, the design of an adaptive neural network-based fixed-time control system for a novel coaxial trans-domain hybrid aerial–underwater vehicle (HAUV) is investigated. A radial basis function neural network (RBFNN) approximation strategy-based adaptive fixed-time terminal sliding mode control (AFTSMC) scheme is proposed to solve the problems of the dynamic nonlinearity, model parameter perturbation, and multiple external disturbances of coaxial HAUV trans-media motion. A complete six-degrees-of-freedom model for a continuous water–air cross-domain model is first established based on the hyperbolic tangent transition function, and, subsequently, based on a basic framework of FTSMC, a fixed-time and fast-convergence controller is designed to track the target position and attitude signals. To reduce the dependence of the control scheme on precise model parameters, an RBFNN approximator is integrated into the sliding mode controller for the online model identification of the aggregate uncertainties of the coaxial HAUV, such as nonlinear unmodeled dynamics and external disturbances. At the same time, an adaptive technique is used to approximate the upper bound of the robust switching term gain in the controller, which further offsets the estimation error of the RBFNN and effectively attenuates the chattering effect. Based on Lyapunov stability theory, it is proven that the tracking error can converge in a fixed time. The effectiveness and superiority of the proposed control strategy are verified by several sets of simulation results obtained under typical working conditions. Full article

(This article belongs to the Special Issue Advances in Autonomous Underwater Drones)

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23 pages, 7403 KiB

Open AccessArticle

Integrating Drone-Based LiDAR and Multispectral Data for Tree Monitoring

by Beatrice Savinelli, Giulia Tagliabue, Luigi Vignali, Roberto Garzonio, Rodolfo Gentili, Cinzia Panigada and Micol Rossini

Drones 2024, 8(12), 744; https://doi.org/10.3390/drones8120744 - 10 Dec 2024

Abstract

Forests are critical for providing ecosystem services and contributing to human well-being, but their health and extent are threatened by climate change, requiring effective monitoring systems. Traditional field-based methods are often labour-intensive, costly, and logistically challenging, limiting their use for large-scale applications. Drones [...] Read more.

Forests are critical for providing ecosystem services and contributing to human well-being, but their health and extent are threatened by climate change, requiring effective monitoring systems. Traditional field-based methods are often labour-intensive, costly, and logistically challenging, limiting their use for large-scale applications. Drones offer advantages such as low operating costs, versatility, and rapid data collection. However, challenges remain in optimising data processing and methods to effectively integrate the acquired data for forest monitoring. This study addresses this challenge by integrating drone-based LiDAR and multispectral data for forest species classification and health monitoring. We developed the methodology in Ticino Park (Italy), where intensive field campaigns were conducted in 2022 to collect tree species compositions, the leaf area index (LAI), canopy chlorophyll content (CCC), and drone data. Individual trees were first extracted from LiDAR data and classified using spectral and textural features derived from the multispectral data, achieving an accuracy of 84%. Key forest traits were then retrieved from the multispectral data using machine learning regression algorithms, which showed satisfactory performance in estimating the LAI (R² = 0.83, RMSE = 0.44 m² m⁻²) and CCC (R² = 0.80, RMSE = 0.33 g m⁻²). The retrieved traits were used to track species-specific changes related to drought. The results obtained highlight the potential of integrating drone-based LiDAR and multispectral data for cost-effective and accurate forest health monitoring and change detection. Full article

(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)

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18 pages, 7740 KiB

Open AccessArticle

Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study

by Younes Zidane, José Silvestre Silva and Gonçalo Tavares

Drones 2024, 8(12), 743; https://doi.org/10.3390/drones8120743 - 10 Dec 2024

Abstract

This study explores the use of electronic countermeasures to disrupt communications systems in Unmanned Aerial Vehicles (UAVs), focusing on the protection of airspaces and critical infrastructures such as airports and power stations. The research aims to develop a low-cost, adaptable jamming device using [...] Read more.

This study explores the use of electronic countermeasures to disrupt communications systems in Unmanned Aerial Vehicles (UAVs), focusing on the protection of airspaces and critical infrastructures such as airports and power stations. The research aims to develop a low-cost, adaptable jamming device using Software Defined Radio (SDR) technology, targeting key UAV communication links, including geolocation, radio control, and video transmission. It applies jamming techniques that successfully disrupt UAV communications. GPS spoofing techniques were also implemented, with both static and dynamic spoofing tested to mislead the drones’ navigation systems. Dynamic spoofing, combined with no-fly zone enforcement, proved to be particularly effective in forcing drones to land or exhibit erratic behavior. The conclusions of this study highlight the effectiveness of these techniques in neutralizing unauthorized UAVs, while also identifying the need for future research in countering drones that operate on alternative frequencies, such as 4G/5G, to enhance the system’s robustness in evolving drone environments. Full article

(This article belongs to the Collection Drones for Security and Defense Applications)

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28 pages, 7259 KiB

Open AccessArticle

Improving Racing Drones Flight Analysis: A Data-Driven Approach Using Motion Capture Systems

by Jose M. Castiblanco Quintero, Sergio Garcia-Nieto, Raul Simarro and Dmitry I. Ignatyev

Drones 2024, 8(12), 742; https://doi.org/10.3390/drones8120742 - 9 Dec 2024

Abstract

The publication of the previous study, titled “Experimental Study on the Dynamic Behaviour of Drones Designed for Racing Competitions”, highlighted the increasing interest in employing scientific methods for their design and analysis. That study examined the flight data of 15 racing drones within [...] Read more.

The publication of the previous study, titled “Experimental Study on the Dynamic Behaviour of Drones Designed for Racing Competitions”, highlighted the increasing interest in employing scientific methods for their design and analysis. That study examined the flight data of 15 racing drones within a large flight area, using Doppler-type sensors for data collection. Building on these findings and seeking to enhance them, the current work introduces an upgraded data acquisition system utilising optical sensors, thereby improving measurement accuracy. These enhanced flight data facilitate the development of updated quality indices and conclusions, offering a more precise and definitive analysis than was previously possible. Full article

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

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19 pages, 30513 KiB

Open AccessArticle

From Detection to Action: A Multimodal AI Framework for Traffic Incident Response

by Afaq Ahmed, Muhammad Farhan, Hassan Eesaar, Kil To Chong and Hilal Tayara

Drones 2024, 8(12), 741; https://doi.org/10.3390/drones8120741 - 9 Dec 2024

Abstract

With the rising incidence of traffic accidents and growing environmental concerns, the demand for advanced systems to ensure traffic and environmental safety has become increasingly urgent. This paper introduces an automated highway safety management framework that integrates computer vision and natural language processing [...] Read more.

With the rising incidence of traffic accidents and growing environmental concerns, the demand for advanced systems to ensure traffic and environmental safety has become increasingly urgent. This paper introduces an automated highway safety management framework that integrates computer vision and natural language processing for real-time monitoring, analysis, and reporting of traffic incidents. The system not only identifies accidents but also aids in coordinating emergency responses, such as dispatching ambulances, fire services, and police, while simultaneously managing traffic flow. The approach begins with the creation of a diverse highway accident dataset, combining public datasets with drone and CCTV footage. YOLOv11s is retrained on this dataset to enable real-time detection of critical traffic elements and anomalies, such as collisions and fires. A vision–language model (VLM), Moondream2, is employed to generate detailed scene descriptions, which are further refined by a large language model (LLM), GPT 4-Turbo, to produce concise incident reports and actionable suggestions. These reports are automatically sent to relevant authorities, ensuring prompt and effective response. The system’s effectiveness is validated through the analysis of diverse accident videos and zero-shot simulation testing within the Webots environment. The results highlight the potential of combining drone and CCTV imagery with AI-driven methodologies to improve traffic management and enhance public safety. Future work will include refining detection models, expanding dataset diversity, and deploying the framework in real-world scenarios using live drone and CCTV feeds. This study lays the groundwork for scalable and reliable solutions to address critical traffic safety challenges. Full article

(This article belongs to the Special Issue Intelligent Processing and Application of UAV Remote Sensing Image Data)

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22 pages, 5995 KiB

Open AccessArticle

Research on 3D Localization of Indoor UAV Based on Wasserstein GAN and Pseudo Fingerprint Map

by Junhua Yang, Jinhang Tian, Yang Qi, Wei Cheng, Yang Liu, Gang Han, Shanzhe Wang, Yapeng Li, Chenghu Cao and Santuan Qin

Drones 2024, 8(12), 740; https://doi.org/10.3390/drones8120740 - 9 Dec 2024

Abstract

In addition to outdoor environments, unmanned aerial vehicles (UAVs) also have a wide range of applications in indoor environments. The complex and changeable indoor environment and relatively small space make indoor localization of UAVs more difficult and urgent. An innovative 3D localization method [...] Read more.

In addition to outdoor environments, unmanned aerial vehicles (UAVs) also have a wide range of applications in indoor environments. The complex and changeable indoor environment and relatively small space make indoor localization of UAVs more difficult and urgent. An innovative 3D localization method for indoor UAVs using a Wasserstein generative adversarial network (WGAN) and a pseudo fingerprint map (PFM) is proposed in this paper. The primary aim is to enhance the localization accuracy and robustness in complex indoor environments. The proposed method integrates four classic matching localization algorithms with WGAN and PFM, demonstrating significant improvements in localization precision. Simulation results show that both the WGAN and PFM algorithms significantly reduce localization errors and enhance environmental adaptability and robustness in both small and large simulated indoor environments. The findings confirm the robustness and efficiency of the proposed method in real-world indoor localization scenarios. In the inertial measurement unit (IMU)-based tracking algorithm, using the fingerprint database of initial coarse particles and the fingerprint database processed by the WGAN algorithm to locate the UAV, the localization error of the four algorithms is reduced by 30.3% on average. After using the PFM algorithm for matching localization, the localization error of the UAV is reduced by 28% on average. Full article

(This article belongs to the Topic Target Tracking, Guidance, and Navigation for Autonomous Systems, 2nd Edition)

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21 pages, 6103 KiB

Open AccessArticle

UAVs-Based Visual Localization via Attention-Driven Image Registration Across Varying Texture Levels

by Yan Ren, Guohai Dong, Tianbo Zhang, Meng Zhang, Xinyu Chen and Mingliang Xue

Drones 2024, 8(12), 739; https://doi.org/10.3390/drones8120739 - 9 Dec 2024

Abstract

This study investigates the difficulties associated with image registration due to variations in perspective, lighting, and ground object details between images captured by drones and satellite imagery. This study proposes an image registration and drone visual localization algorithm based on an attention mechanism. [...] Read more.

This study investigates the difficulties associated with image registration due to variations in perspective, lighting, and ground object details between images captured by drones and satellite imagery. This study proposes an image registration and drone visual localization algorithm based on an attention mechanism. Initially, an improved Oriented FAST and Rotated BRIEF (ORB) algorithm incorporating a quadtree-based feature point homogenization method is designed to extract image feature points, providing support for the initial motion estimation of UAVs. Following this, we combined a convolutional neural network with an attention mechanism and the inverse-combined Lucas-Kanade method to further extract image features. This integration facilitates the efficient registration of drone images with satellite tiles. Finally, we utilized the registration results to correct the initial motion of the drone and accurately determine its location. Our experimental findings indicate that the proposed algorithm achieves an average absolute positioning error of less than 40 m for low-texture flight paths and under 10 m for high-texture paths. This significantly mitigates the positioning challenges that arise from inconsistencies between drone images and satellite maps. Moreover, our method demonstrates a notable improvement in computational speed compared to existing algorithms. Full article

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22 pages, 10279 KiB

Open AccessArticle

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

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, 717 KiB

Open AccessArticle

Research on Enhanced Belief Propagation List Decoding Algorithm for Polar Codes in UAV Communications for 6G

by Chuanji Zhu, Yuanzhi He and Zheng Dou

Drones 2024, 8(12), 737; https://doi.org/10.3390/drones8120737 - 6 Dec 2024

Abstract

The introduction of sixth-generation mobile communication technology (6G) poses new requirements for the capacity, rate, latency, and reliability of communication systems. As a vital component of 6G technology, unmanned aerial vehicle (UAV) communications also face various challenges, such as noise interference and limited [...] Read more.

The introduction of sixth-generation mobile communication technology (6G) poses new requirements for the capacity, rate, latency, and reliability of communication systems. As a vital component of 6G technology, unmanned aerial vehicle (UAV) communications also face various challenges, such as noise interference and limited hardware resources. To meet the high demands of 6G, advanced channel coding techniques need to be adopted. Polar codes, due to their theoretically achievable Shannon limit performance, have potential applications in UAV communication systems. Constructing reliable polar decoding schemes is currently a research hotspot in the field of communications. The Belief Propagation List (BPL) decoding algorithm for polar codes can effectively enhance the accuracy of polar code BP decoding. However, existing BPL decoding algorithms for polar codes face issues such as high hardware resource consumption and unsatisfactory decoding accuracy. Addressing the aforementioned issues, this paper proposes a BPL decoding algorithm for polar codes based on information geometry. An information geometry framework is constructed, where the soft information output by the BP decoder is treated as points on a statistical manifold, and their geometric properties are calculated. By introducing the concept of the soft information centroid and a path selection criterion based on the soft information centroid, combined with geometric distance as a weight, the decoding performance is improved, and hardware overhead is reduced. Simulation results show that under the conditions of a maximum of 60 iterations and 5 decoders, the proposed algorithm reduces the bit error rate by 16.2–74.9% compared to the classic BPL algorithm, providing strong technical support for the application of polar codes in scenarios such as UAV communications. Full article

(This article belongs to the Special Issue Space–Air–Ground Integrated Networks for 6G)

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16 pages, 21810 KiB

Open AccessArticle

Enhancing Direct Georeferencing Using Real-Time Kinematic UAVs and Structure from Motion-Based Photogrammetry for Large-Scale Infrastructure

by Soohee Han and Dongyeob Han

Drones 2024, 8(12), 736; https://doi.org/10.3390/drones8120736 - 5 Dec 2024

Abstract

The growing demand for high-accuracy mapping and 3D modeling using unmanned aerial vehicles (UAVs) has accelerated advancements in flight dynamics, positioning accuracy, and imaging technology. Structure from motion (SfM), a computer vision-based approach, is increasingly replacing traditional photogrammetry through facilitating the automation of [...] Read more.

The growing demand for high-accuracy mapping and 3D modeling using unmanned aerial vehicles (UAVs) has accelerated advancements in flight dynamics, positioning accuracy, and imaging technology. Structure from motion (SfM), a computer vision-based approach, is increasingly replacing traditional photogrammetry through facilitating the automation of processes such as aerial triangulation (AT), terrain modeling, and orthomosaic generation. This study examines methods to enhance the accuracy of SfM-based AT through real-time kinematic (RTK) UAV imagery, focusing on large-scale infrastructure applications, including a dam and its entire basin. The target area, primarily consisting of homogeneous water surfaces, poses considerable challenges for feature point extraction and image matching, which are crucial for effective SfM. To overcome these challenges and improve the AT accuracy, a constraint equation was applied, incorporating weighted 3D coordinates derived from RTK UAV data. Furthermore, oblique images were combined with nadir images to stabilize AT, and confidence-based filtering was applied to point clouds to enhance geometric quality. The results indicate that assigning appropriate weights to 3D coordinates and incorporating oblique imagery significantly improve the AT accuracy. This approach presents promising advancements for RTK UAV-based AT in SfM-challenging, large-scale environments, thus supporting more efficient and precise mapping applications. Full article

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17 pages, 7592 KiB

Open AccessArticle

Parametric Design Approach for Wireless Power Transfer System: UAV Applications

by Mohammed Terrah, Mostafa-Kamel Smail, Lionel Pichon and Mohamed Bensetti

Drones 2024, 8(12), 735; https://doi.org/10.3390/drones8120735 - 5 Dec 2024

Abstract

Adopting Wireless Power Transfer (WPT) technology to an Unmanned Aerial Vehicle (UAV) involves adding extra components, which may impact the drone’s overall weight and performance. This paper aims to enhance UAV performance by designing a lightweight WPT system through a parametric design approach. [...] Read more.

Adopting Wireless Power Transfer (WPT) technology to an Unmanned Aerial Vehicle (UAV) involves adding extra components, which may impact the drone’s overall weight and performance. This paper aims to enhance UAV performance by designing a lightweight WPT system through a parametric design approach. This method explores novel perspectives by identifying the most suitable combination of parameters in terms of efficiency, weight, and feasibility. Various parameters such as the compensation topology, number of turns of coils, and frequency were studied. The system was analyzed through a coupled simulation approach, where electromagnetic modeling of the coupler using the finite element method (FEM) was combined with electrical circuit simulations, providing a more accurate assessment of the overall system efficiency and behavior considering variations in the coupling factor due to misalignment. A prototype of the resulting configuration was designed and tested experimentally versus misalignment at reduced power using a specific test bench. The results show a 70% efficiency level with SP compensation that was improved to 80% with SS compensation. Full article

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

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21 pages, 6639 KiB

Open AccessArticle

Autonomous Yield Estimation System for Small Commercial Orchards Using UAV and AI

by Sergejs Kodors, Imants Zarembo, Gunārs Lācis, Lienīte Litavniece, Ilmārs Apeināns, Marks Sondors and Antons Pacejs

Drones 2024, 8(12), 734; https://doi.org/10.3390/drones8120734 - 4 Dec 2024

Abstract

In the context of precision horticulture, decision support tools play a significant role in providing fruit growers with insights into orchard conditions, facilitating informed decisions regarding orchard management practices. This study presents the development of an autonomous yield estimation system designed to provide [...] Read more.

In the context of precision horticulture, decision support tools play a significant role in providing fruit growers with insights into orchard conditions, facilitating informed decisions regarding orchard management practices. This study presents the development of an autonomous yield estimation system designed to provide decision support to small commercial orchards. Autonomous yield estimation is based on the application of UAVs and AI. AI is used to identify and quantify fruitlets and fruits in photographs collected by UAV. In this article, we present our prototype of an autonomous yield estimation system. The adapted “4+1” architecture was applied to design a system with a holistic approach analyzing software, hardware, and ecosystem requirements. Six datasets are presented, which contain the images of fruitlets and fruits of apples, pears, and cherries. Three CNN models were trained: YOLOv8m, YOLOv9m, and YOLOv10m. The experiment showed that the most accurate was YOLOv9m, which achieved mean accuracies of 0.896 mAP@50 and 0.510 mAP@50:95 for all datasets. Full article

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

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