Operation Of Unmanned Aerial Vehicles Research Articles

Enhancing UAV safety with an innovative anti-collision cage: Design, testing, and future prospects

The rapid evolution of Unmanned Aerial Vehicles (UAVs) or Unmanned Aircraft Systems (UAS) has revolutionized aviation across military and civilian domains with their pilotless flight capabilities. Despite their versatility, UAVs pose challenges in in-flight piloting precision, leading to potential mishaps. Addressing these concerns, this research introduces an innovative UAV Anti-Collision Cage designed to enhance drone safety. Constructed from C60-shaped carbon fiber rods and 3D-printed connectors, the cage resembles a footballs geometry, offering 360 protection. Experimental validations, including rolling, collision, and flying tests, were conducted to assess the cages performance. While the cage demonstrated resilience against minor impacts, significant impacts posed challenges. The study concludes with recommendations for future improvements, emphasizing geometric refinements, material choices, enhanced rolling abilities, sensor integration, and payload capacity. This research underscores the importance of safety mechanisms in UAV operations, paving the way for safer and more efficient drone operation in confined and complex environments.

Joint Optimization on Trajectory, Data Relay, and Wireless Power Transfer in UAV-Based Environmental Monitoring System

In environmental monitoring systems based on the Internet of Things (IoT), sensor nodes (SNs) typically send data to the server via a wireless gateway (GW) at regular intervals. However, when SNs are located far from the GW, substantial energy is expended in transmitting data. This paper introduces a novel unmanned aerial vehicle (UAV)-based environmental monitoring system. In the proposed system, the UAV conducts patrols in the designated area, and SNs periodically transmit the collected data to the GW or the UAV. This transmission decision is made while taking into account the respective distance between both the GW and the UAV. To ensure a high-quality environmental map, characterized by a consistent collection of a satisfactory amount of up-to-date data while preventing energy depletion in the SNs and the UAV, the UAV periodically decides on three types of UAV operations. These decisions involve deciding where to move, deciding whether to relay or aggregate the data from the SNs, and deciding whether to transfer energy to the SNs. For the optimal decisions, we introduce an algorithm, called DeepUAV, using deep reinforcement learning (DRL) to make decisions in UAV operations. In DeepUAV, the controller continually learns online and enhances the UAV’s decisions through trial and error. The evaluation results indicate that DeepUAV successfully gathers a substantial amount of the current data consistently while mitigating the risk of energy depletion in SNs and the UAV.

Autonomous drones and their influence on standardization of rules and regulations for operating–A brief overview

This paper meticulously examines, with a keen focus, the intricate correlation between global unmanned aerial vehicle (UAV) regulations and the dynamic evolution of autonomy levels. Through a comprehensive investigation of the foundational components of UAVs, encompassing motors, propellers, flight control systems, and communication protocols, the study unveal the complex interactions that shape regulatory frameworks on a global scale. Expanding on this thorough exploration, the paper delves deeply into the critical role played by cutting-edge vision and mapping technologies, such as cameras and LiDAR sensors, in the ongoing endeavour to push the boundaries of autonomy. Furthermore, it explores subtle nuances in regulatory approaches across diverse regions, shedding light on the various factors influencing the development and implementation of UAV regulations. The paper also highlights the collaborative efforts within the industry and academia to address emerging challenges, fostering a deeper understanding of the synergies required for sustained advancements. Additionally, it delves into the implications of evolving regulatory landscapes on the broader drone ecosystem, including potential impacts on innovation, safety, and societal acceptance. Moreover, the paper critically assesses the current state of international cooperation in establishing standardized guidelines for UAV operations, providing insights into the potential harmonization of regulations to facilitate seamless global integration. By weaving together these multifaceted aspects, the paper not only offers valuable perspectives on the dynamic progression of UAV regulations but also significantly contributes to our nuanced understanding of the continuous advancements in autonomous control technology for these aerial platforms.

Autonomous predictive maintenance of quadrotor UAV with multi-actuator degradation

Abstract With the wide application of quadrotor unmanned aerial vehicles (UAVs), the requirements for their safety and reliability are becoming increasingly stringent. In this paper, based on the feedback of airframe performance health perception information and the predictive function control strategy, the autonomous maintenance of a quadrotor UAV with multi-actuator degradation is realised. Autonomous maintenance architecture is constructed by the predictive maintenance (PdM) idea and the Laguerre function model predictive pontrol (LF-MPC) strategy. Using the two-stage Kalman filter (TSKF) method, based on the established UAV degradation model, the aircraft state and actuator degradation state are predicted simultaneously. For the predictive perception of system health, on the one hand, the system health degree (HD) based on Mahalanobis distance is defined by the degree of airframe state deviation from the expected state, and then the failure threshold of the UAV is obtained. On the other hand, according to the degradation state of each actuator, a comprehensive degradation variable fused with different weight coefficients of multiple actuators degradation is used to obtain the probability density function (PDF) of remaining useful life (RUL) prediction. For the autonomous maintenance of system health, the LF-MPC weight matrixes are adjusted adaptively in real-time based on the HD evaluation, to achieve a compromise balance between UAV performance and control effect, and greatly extend the working time of UAV. Simulation results verified the effectiveness of the proposed method.

A novel small object detection algorithm for UAVs based on YOLOv5

Due to the advances in deep learning, artificial intelligence is widely utilized in numerous areas. Technologies frontier, including computer vision, represented by object detection, have endowed unmanned aerial vehicles (UAVs) with autonomous perception, analysis, and decision-making capabilities. UAVs extensively used in numerous fields including photography, industry and agriculture, surveillance, disaster relief, and play an important role in real life. However, current object detection algorithms encountered challenges when it came to detecting small objects in images captured by UAVs. The small size of the objects, with high density, low resolution, and few features make it difficult for the algorithms to achieve high detection accuracy and are prone to miss and false detections especially when detecting small objects. For the case of enhancing the performance of UAV detection on small objects, a novel small object detection algorithm for UAVs adaptation based on YOLOv5s (UA-YOLOv5s) was proposed. (1) To achieve effective small-sized objects detection, a more accurate small object detection (MASOD) structure was adopted. (2) To boost the detection accuracy and generalization ability of the model, a multi-scale feature fusion (MSF) approach was proposed, which fused the feature information of the shallow layers of the backbone and the neck. (3) To enhance the model stability properties and feature extraction capability, a more efficient and stable convolution residual Squeeze-and-Excitation (CRS)module was introduced. Compared with the YOLOv5s, mAP@0.5 was achieved an impressive improvement of 7.2%. Compared with the YOLOv5l, mAP@0.5 increased by 1.0%, and GFLOPs decreased by 69.1%. Compared to the YOLOv3, mAP@0.5 decreased by 0.2% and GFLOPs by 78.5%. The study’s findings demonstrated that the proposed UA-YOLOv5s significantly enhanced the object detection performance of UAVs campared to the traditional algorithms.

Electric and Magnetic Fields Analysis of the Safety Distance for UAV Inspection around Extra-High Voltage Transmission Lines

The deployment of small unmanned aerial vehicles (UAVs), or drones, for transmission line inspections, has brought attention to the potential impact of electromagnetic fields (EMFs) on UAV operations. This work describes a mathematical model based on the finite elements method (FEM), designed to examine the electric and magnetic fields produced by extra-high voltage (EHV) conductors. The current study extends the analysis to encompass both electric and magnetic fields and evaluates the safe distances for UAVs operating near 345 kV, 500 kV, and 765 kV transmission lines. The electromagnetic environment around these EHV transmission lines was simulated using electrostatic, magnetostatic, and transient magnetic modules within the QuickField software 6.6. Electric and magnetic profiles were estimated using 2D finite element analysis, including a numerical simulation for phase-to-phase fault EMFs for the above transmission lines. These results were then cross-verified with theoretical calculations at specific intervals and further validated using the EMFACDC analytical method developed by the International Telecommunication Union. This comprehensive assessment concludes that precise distance considerations are necessary to ensure UAV safety during power line inspections, mitigating potential risks from EMF interference.

Resilient Multi-Sensor UAV Navigation with a Hybrid Federated Fusion Architecture

Future UAV (unmanned aerial vehicle) operations in urban environments demand a PNT (position, navigation, and timing) solution that is both robust and resilient. While a GNSS (global navigation satellite system) can provide an accurate position under open-sky assumptions, the complexity of urban operations leads to NLOS (non-line-of-sight) and multipath effects, which in turn impact the accuracy of the PNT data. A key research question within the research community pertains to determining the appropriate hybrid fusion architecture that can ensure the resilience and continuity of UAV operations in urban environments, minimizing significant degradations of PNT data. In this context, we present a novel federated fusion architecture that integrates data from the GNSS, the IMU (inertial measurement unit), a monocular camera, and a barometer to cope with the GNSS multipath and positioning performance degradation. Within the federated fusion architecture, local filters are implemented using EKFs (extended Kalman filters), while a master filter is used in the form of a GRU (gated recurrent unit) block. Data collection is performed by setting up a virtual environment in AirSim for the visual odometry aid and barometer data, while Spirent GSS7000 hardware is used to collect the GNSS and IMU data. The hybrid fusion architecture is compared to a classic federated architecture (formed only by EKFs) and tested under different light and weather conditions to assess its resilience, including multipath and GNSS outages. The proposed solution demonstrates improved resilience and robustness in a range of degraded conditions while maintaining a good level of positioning performance with a 95th percentile error of 0.54 m for the square scenario and 1.72 m for the survey scenario.

Privacy-Aware Remote Identification for Unmanned Aerial Vehicles: Current Solutions, Potential Threats, and Future Directions

The Federal Aviation Administration (FAA) recently introduced a new standard, namely, remote identification, to improve accountability for unmanned aerial vehicles (UAVs) operations. This rule requires UAV operators to broadcast messages revealing sensitive data, such as identity and location on the wireless channel. However, this leads to security and privacy concerns among UAV operators. Unauthorized parties may easily discover the location and identity of a UAV flying in a specific area and launch attacks on it such as using wireless jamming or tracking its activity. This review investigates and systematizes the main weaknesses affecting the Remote ID capability required of modern UAVs, and the approaches through which attackers can exploit these weaknesses to disrupt safety and accountability. Moreover, this article analyzes current solutions that mitigate privacy issues associated with Remote ID. Finally, we identify multiple challenges that require to be addressed by both industry and academia, and we propose future research directions to improve the security and privacy of UAVs.

An efficient Method for Evaluation of Unmanned Aerial Vehicles: A Case Study in Livestock

The increasing popularity and ease of use of unmanned aerial vehicles (UAVs) have made them a thriving technology in various fields. One of their applications is in the animal revolution, where they are used to count, monitor, and detect livestock accurately, contributing to the development of the animal revolution. However, with so many types of UAVs available, it can be challenging to decide which one is most appropriate for the task at hand due to their varying characteristics. To address this issue, the suggested model is constructed utilizing neutrosophic sets to effectively manage uncertainty and deal with multi-criteria decision-making (MCDM) situations with several conflicting criteria and options. The proposed model integrates Stepwise Weight Assessment Ratio Analysis (SWARA) and the ARAS methods for evaluating the performance of UAVs in livestock based on diverse criteria and their importance, along with single-valued neutrosophic sets (SVNSs). The SWARA method is used for calculating the weight of criteria, and the ARAS method is used for ranking alternatives. An experimental case study has been established for choosing the best UAV to detect livestock using the data extracted from the thermal and multispectral UAV images.

Maritime Emission Monitoring: Development and Testing of a UAV-Based Real-Time Wind Sensing Mission Planner Module

Maritime emissions contribute significantly to global pollution, necessitating accurate and efficient monitoring methods. Traditional methods for tracking ship emissions often face limitations in real-time data accuracy, with wind measurement being a critical yet challenging aspect. This paper introduces an innovative mission planner module for unmanned aerial vehicles (UAVs) that leverages onboard wind sensing capabilities to enhance maritime emission monitoring. The module’s primary objective is to assist operators in making informed decisions by providing real-time wind data overlays, thus optimizing flight paths and data collection efficiency. Our experimental setup involves the testing of the module in simulated maritime environments, demonstrating its efficacy in varying wind conditions. The real-time wind data overlays provided by the module enable UAV operators to adjust their flight paths dynamically, reducing unnecessary power expenditure and mitigating the risks associated with low-battery scenarios, especially in challenging maritime conditions. This paper presents the implementation of real-time wind data overlays on an open-source state-of-the-art mission planner as a C# plugin that is seamlessly integrated into the user interface. The factors that affect performance, in terms of communication overheads and real-time operation, are identified and discussed. The operation of the module is evaluated in terms of functional integration and real-time visual representation of wind measurements, and the enhanced situational awareness that it can offer to mission controllers is demonstrated. Beyond presenting a novel application of UAV technology in environmental monitoring, we also provide an extensive discussion of how this work will be extended in the context of complete aerial environmental inspection missions and the future directions in research within the field that can potentially lead to the modernization of maritime emission monitoring practices.

AI-Embedded UAV System for Detecting and Pursuing Unwanted UAVs

In recent years, the use of unmanned aerial vehicle (UAV) platforms in civil and military applications has surged, highlighting the critical role of artificial intelligence (AI) embedded UAV systems in the future. This study introduces the Autonomous Drone (Vechür-SIHA), a novel AI-embedded UAV system designed for real-time detection and tracking of other UAVs during flight sequences. Leveraging advanced object detection algorithms and an LSTM-based tracking mechanism, our system achieves an impressive 80% accuracy in drone detection, even in challenging conditions like varying backgrounds and adverse weather.
 Our system boasts the capability to simultaneously track multiple drones within its field of view, maintaining flight for up to 35 minutes, making it ideal for extended missions that require continuous UAV tracking. Moreover, it can lock onto and track other UAVs in mid-air for durations of 4-10 seconds without losing contact, a feature with significant potential for security applications.
 This research marks a substantial contribution to the development of AI-embedded UAV systems, with broad implications across diverse domains such as search and rescue operations, border security, and forest fire prevention. These results provide a solid foundation for future research, fostering the creation of similar systems tailored to different applications, ultimately enhancing the efficiency and safety of UAV operations. The novel approach to real-time UAV detection and tracking presented here holds promise for driving innovations in UAV technology and its diverse applications.

Design and Analysis of UAV Propeller for Composite Materials

Unmanned Aerial Vehicles (UAVs) have witnessed significant advancements and growth in recent years, where the efficiency and performance of propellers play a vital role. This study focuses on the design and analysis of UAV propellers utilizing ANSYS software. The propeller's structural behavior and aerodynamic effects are investigated through advanced engineering approaches. This study involves using ANSYS software to conduct a comprehensive analysis of UAV propellers, focusing on a comparative study of three distinct materials: Carbon Fiber Reinforced Polymer (CFRP), Glass Fiber Reinforced Polymer (GFRP), and aluminum alloy. A comparative study is conducted by analyzing various results such as maxim stress, maximum deformation and also the fluid flows. By considering the advantages and limitations of CFRP, GFRP, and aluminum alloy, this research contributes to the advancement of unmanned aerial technology, driving innovation and efficiency in UAV design and performance optimization.

Психологические профессионально важные качества операторов беспилотных летательных аппаратов

The work analyzes the current state of affairs in the field of selection of unmanned aerial vehicle operators. Subject of research: psychophysiological characteristics of UAV operators. Purpose of the study: to identify significant professionally important psychological qualities of UAV operators; assessment of the influence of these qualities on the effectiveness of professional activities. The study consisted of three parts: studying the characteristics of operators’ activities and technical documentation; organizing and conducting research using a psychographic questionnaire; conducting research using a battery of tests. The study involved 47 men aged 20 to 36 years old. The lag of this industry in the Russian Federation and the need for its development and research are shown. Further, significant psychological professionally important qualities of UAV operators were identified. These include responsibility, attention span, emotional stability, spatial orientation, balance of excitation and inhibition processes, reaction speed, long-term memory, volitional qualities, speed of thought processes, internal locus of control, orientation to achieving success, high level of attention distribution. A method of professional psychological selection was also presented and tested, which included the following battery of tests: Red-black tables, Amthauer test, Landolt rings, Clocks, Compasses, multifactor personality questionnaire "Adaptivity", Aizen test adapted by Shmelev A.G., scale Spielberger's anxiety in the adaptation of Khanin Yu.L., ANNETTE. Thus, based on the conducted research, proposals for professional psychological selection were formed, and significant psychological professionally important qualities of UAV operators were identified. An analysis of the current situation in the development of a scientific problem was also carried out, based on which several proposals were made for further research. The results of the study can be used in organizing professional psychological selection.

Quantum-Inspired Neural Network Model of Optical Illusions

Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design and train a deep neural network model to simulate human perception of the Necker cube, an ambiguous drawing with several alternating possible interpretations. Defining the weights of the neural network connection using a quantum generator of truly random numbers, in agreement with the emerging concepts of quantum artificial intelligence and quantum cognition, we reveal that the actual perceptual state of the Necker cube is a qubit-like superposition of the two fundamental perceptual states predicted by classical theories. Our results finds applications in video games and virtual reality systems employed for training of astronauts and operators of unmanned aerial vehicles. They are also useful for researchers working in the fields of machine learning and vision, psychology of perception and quantum–mechanical models of human mind and decision making.

Aktualna regulacja użytkowania dronów w prawie polskim – ocena i postulaty de lege ferenda

The article highlights the problem of the lack of drone regulations in Polish aviation law. The authors identify areas of use of unmanned aerial vehicles and propose possible solutions to problems related to drones. They also specify particular de lege ferenda postulates in the area concerning the operation, exploitation, and use of unmanned aerial vehicles.

FTESO-adaptive neural network based safety control for a quadrotor UAV under multiple disturbances: algorithm and experiments

PurposeQuadrotors have been applied in various fields. However, because the quadrotor is subject to multiple disturbances, consisting of external disturbances, actuator faults and parameter uncertainties, it is difficult to control the unmanned aerial vehicle (UAV) to achieve high-precision tracking performance. This paper aims to design a safety controller that uses observer and neural network method to improve the tracking performance of UAV under multiple disturbances. The experiments prove that this method is effective.Design/methodology/approachFirst, to actively estimate and compensate the synthetic uncertainties of the system, a finite-time extended state observer is investigated, and the disturbances are transformed into the extended state of the system for estimation. Second, an adaptive neural network controller that does not accurately require the dynamic model knowledge is designed based on the estimated value, where the weights of the neural network can be dynamically adjusted by the adaptive law. Furthermore, the finite-time bounded convergence of the proposed observer and the stability of the system are proved through homogeneous theory and Lyapunov method.FindingsThe figure-“8” climbing flight simulation and real flight experiments illustrate that the proposed safety control strategy has good tracking performance.Originality/valueThis paper proposes the safety control structure of the UAV, which combines the extended state observer with the neural network method. Numerical simulation results and actual flight experiments demonstrate the effectiveness of the proposed control strategy.

Cognitive performance of air personnel following sleep deprivation.

Air forces have developed several methods for reducing fatigue-related accidents. In the Israeli Air Force, the "Dead Tired" workshop was developed with the purpose of presenting aircrew with their objective performance under sleep deprivation conditions. The aim of this study was to examine the cognitive abilities of both aircrew and unmanned aerial vehicle operators, both objectively and subjectively. All Israeli aircrew and unmanned aerial vehicle operators participated in a "Dead Tired" workshop. During the workshop, the participants performed the Psychomotor Vigilance Task, a task that tests their attention abilities, while gathering information on their subjective sleepiness in the form of a Karolinska Sleepiness Scale. Data of 366 participants (25 females), of whom 187 were unmanned aerial vehicle operators and 179 were aircrew, were obtained; and the mean age was 21.8 ± 1.2 years (range 19-26 years). A significant decline in task performance was seen following 20 hr of wakefulness in both unmanned aerial vehicle operators and aircrew (p < 0.001). Unmanned aerial vehicle operators' performance was significantly better throughout the majority of the workshop (p < 0.001). Recovery after the full-night's sleep was seen for unmanned aerial vehicle operators, but not for aircrew (p = 0.008). A high correlation was seen between Psychomotor Vigilance Task performance and Karolinska Sleepiness Scale responses (correlation coefficient = 0.93). Sleep deprivation negatively impacted the performance of both groups of participants. Unmanned aerial vehicle operators were found to be more resilient to the effects of sleep deprivation and were quicker to recover in comparison to aircrew.

Overview and Energy Power Analysis of Composite Solar Unmanned Aerial Vehicles

In this paper, based on the existing research progress and challenges in unmanned aerial vehicles (UAVs), efforts are made to enhance the performance of UAVs in terms of endurance and range by optimizing the energy and power systems. We propose the concept of a composite UAV and further introduce the concept of a solar-powered UAV. The paper outlines the project's design philosophy and innovative aspects, followed by an exploration of potential applications. Energy control calculations are then conducted, leading to the proposal of a gravity energy storage strategy. Then a preliminary flight control scheme is presented. This paper concludes with a conclusion and prospects for future work.

Assessment of 5G Connectivity for UAV Operations in Urban Environments: An Analysis of Network Performance and UAV Communication Architecture

This article presents an in-depth analysis of the integration of 5G connectivity for unmanned aerial vehicle (UAV) operations in urban environments, focusing on network performance and the architectural framework necessary for optimal UAV communication. As UAVs become increasingly prevalent in urban applications, from surveillance to delivery services, the demand for robust and reliable communication systems has become paramount. This study explores the unique challenges posed by urban settings, such as signal obstruction and high-density network traffic, and examines how 5G technology offers solutions through advanced features like beamforming. By detailing the setup and evaluation of a UAV communication architecture that incorporates 5G modules, including the use of the NVIDIA Jetson AGX Orin Developer Kit for real-world testing, this article highlights the significant improvements in data rate, latency, and reliability achieved with 5G networks compared to traditional communication technologies. The findings underscore the transformative potential of 5G connectivity in enhancing UAV operational efficiency and reliability in complex urban landscapes. Through case studies and practical insights, the article also provides a glimpse into the future of urban UAV applications, paving the way for further research and development in this dynamic field.

КВАЛІФІКАЦІЙНІ ВИМОГИ ТА МЕТОДИЧНІ ПІДХОДИ ДО НАВЧАННЯ ОПЕРАТОРІВ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ ДЛЯ ПІДРОЗДІЛІВ ДЕРЖАВНОЇ ПРИКОРДОННОЇ СЛУЖБИ УКРАЇНИ

Based on the generalization of the experience of participation of military units of the State Border Guard Service of Ukraine in performing tasks related to the defense of the state, the article formulates requirements and proposes methodological approaches to training of operators of mini-class unmanned aerial vehicles (FPV-drones), which will make it possible to organize targeted training of FPV-drone operators not only in the training centers of the border guard agencies, but also directly in the units and on the basis of relevant volunteer organizations.