Reliable Unmanned Aerial Vehicles Research Articles

Channel Knowledge Map Construction Based on a UAV-Assisted Channel Measurement System

With the fast development of unmanned aerial vehicles (UAVs), reliable UAV communication is becoming increasingly vital. The channel knowledge map (CKM) is a crucial bridge connecting the environment and the propagation channel that may visually depict channel characteristics. This paper presents a comprehensive scheme based on a UAV-assisted channel measurement system for constructing the CKM in real-world scenarios. Firstly, a three-dimensional (3D) CKM construction scheme for real-world scenarios is provided, which involves channel knowledge extraction, mapping, and completion. Secondly, an algorithm of channel knowledge extraction and completion is proposed. The sparse channel knowledge is extracted based on the sliding correlation and constant false alarm rate (CFAR) approaches. The 3D Kriging interpolation is used to complete the sparse channel knowledge. Finally, a UAV-assisted channel measurement system is developed and CKM measurement campaigns are conducted in campus and farmland scenarios. The path loss (PL) and root mean square delay spread (RMS-DS) are measured at different heights to determine CKMs. The measured and analyzed results show that the proposed construction scheme can effectively and accurately construct the CKMs in real-world scenarios.

Research on UAV flight attitude control system based on simulink and genetic algorithm

With the evolution of networking and information technology, unmanned aerial vehicles (UAVs) have emerged as integral components in numerous fields, executing diverse and vital tasks. The accuracy and efficiency of UAV operations are greatly influenced by their attitude control systems, which dictate flight stability, speed, and functionality. However, traditional control methods for UAV flight stability and accuracy have become outdated, unable to meet the demands of modern applications. To address these challenges, this study introduces a novel attitude control approach based on genetic algorithms. This method utilizes the principles of natural selection and genetic traits to optimize the parameters of the attitude control system. The objective is to enhance the current control effectiveness, improve the UAV's maneuverability, and enable its versatile applications across multiple fields. Simulink tool simulations are conducted to evaluate the stability of the optimized results. This simulation tool allows for the replication of real-world flight conditions and the assessment of the attitude control system's performance under various scenarios. The results of these simulations provide valuable insights into the effectiveness of the genetic algorithm-based attitude control method. This study contributes to the advancement of UAV technology by improving the accuracy and stability of attitude control systems. It paves the way for more efficient and reliable UAV operations in various fields, from environmental monitoring to search-and-rescue missions, thus enhancing their operational capabilities and expanding their potential applications.

MANUFAKTUR UAV FIXED WING MENGGUNAKAN MATERIAL STYROFOAM DENGAN METODE LAY-UP (PELAPISAN) FIBERGLASS DAN RESIN

This research discusses the development of a manufacturing process for a fixed wing-based UAV (Unmanned Aerial Vehicle) using Styrofoam material as the structural core. The manufacturing method applied is lay-up of fiberglass and resin to improve the strength and aerodynamic performance of the UAV. This research aims to evaluate the potential of styrofoam material enhanced by such lay-up technique. The research method involves the steps of applying fiberglass and resin lay-up on Styrofoam structure. The results of the Fixed Wing UAV Manufacturing research using Styrofoam material with fiberglass resin lay-up method, the aircraft structure becomes lighter and stronger with the overall resulting load reaching 2750 Gram or 2.75 kg. The results showed that the application of the lay-up method significantly improved the structural strength of the UAV, while the use of fiberglass and resin materials made a positive contribution to its aerodynamic performance. The invention has the potential to improve the efficiency and durability of UAVs in monitoring and surveillance applications. By presenting alternative materials and manufacturing methods, this research can contribute to the development of more reliable and effective UAV.

UAV Target Tracking Algorithm Based on Illumination Adaptation and Future Awareness in Low Illumination Scenes

Aiming at problems such as tracking failure caused by illumination changes often encountered during unmanned aerial vehicle (UAV) tracking, a target tracking algorithm with illumination adaptive and future-aware correlation filters is proposed based on the background-aware correlation filters (BACF) algorithm, which realizes reliable UAV tracking tasks at night. First, the dark scene is recognized, and an efficient image enhancement module is used to enhance the brightness of low illumination images. Then a future-aware module is constructed to train the tracking model using the contextual information of the target in the next frame for better robustness. Finally, the model updating stage involves adaptive filter updating and adaptive learning rate updating to enhance target tracking precision. The results of the comparison experiments with the state-of-the-art algorithms on the UAVDark135, UAVDT, and DTB70 datasets show that the algorithm in this paper outperforms the state-of-the-art tracking methods and has better tracking performance under light changes and fast motion (FM) scenarios. The tracking speed on a single Central processing unit (CPU) reaches 49 FPS, which satisfies the real-time requirement of UAV tracking.

İnsansız Hava Araçları (İHA) İçin Küresel Navigasyon Uydu Sistemi (GNSS) Bağımsız Navigasyon

The ability of Unmanned Aerial Vehicles (UAV) to perform autonomous navigation depends on the accurate determination of their positions provided by the Global Navigation Satellite System (GNSS). For position determination and environmental orientation during flight, UAVs are usually equipped with electronic equipment such as GNSS, Inertial Measurement Unit (IMU), gyroscope and accelerometer. However, the GNSS signal may be lost or distorted due to poor weather, obstacles or terrain, the unfavorable position of satellites, spoofing and jamming. In such cases of GNSS signal loss or deterioration, the IMU alone becomes unable to provide reliable UAV location information. Especially in cases where there is not enough visibility and the UAV cannot be brought to the take-off point by manual operation, the loss of the GNSS signal causes great losses. In this paper, GNSS independent flight and navigation studies are included. It is seen that the use of hybrid navigation solutions has great importance in GNSS independent UAV flights.

Deployment of a UAV swarm-based LiFi network in the obstacle-ridden environment: algorithms of finding the path for UAV placement

The subject of this study is unmanned aerial vehicle (UAV)-based wireless networks in an obstacle-ridden environment. The aim of this study is to develop methods and software to ensure reliable LiFi communication using swarm UAVs in an obstacle-ridden environment. The objectives are as follows: 1) to describe the problem of providing a reliable UAV swarm-based LiFi network, requirements for the composition and use of UAVs, and assumptions; 2) to develop the methodology for solving research tasks; 3) to develop the method and algorithms for solving the problem, considering the requirements, assumptions, and practical limitations; 4) to explore the algorithms by developing software for modeling and searching for rational UAV placement to ensure the required UAV-based LiFi network characteristics; 5) to provide experiments and illustrative examples of the developed tool’s application. The following results were obtained. 1) The requirements for the composition and use of UAVs for creating LiFi networks, as well as assumptions and limitations for the methodology development and research task solving. 2) An obstacle avoidance method using the left and right angles algorithm. 3) A method for obstacle avoidance using the controlled waterfall algorithm. 4) A software tool for modeling and searching for rational UAV placement to ensure the required LiFi network characteristics. The tool allows route construction under obstacles in 2D space and a comparison of the developed algorithms for various variants of obstacle placement. Conclusions. The main contribution of this research is a set of methods, algorithms, and software tools for providing communications between two points using LiFi technologies and a swarm of UAVs supporting these communications as transmitters in conditions of mechanical obstacles.

A secure and efficient UAV network defense strategy: Convergence of blockchain and deep learning

Unmanned Aerial Vehicles (UAVs) are highly versatile and efficient tools utilized across diverse industries for data collection. However, they are vulnerable to wireless communication and data exchange risks, including unauthorized access, data theft, and network attacks. To address these problems, we introduce a secure and reliable UAV network service architecture that incorporates blockchain and deep learning to provide more secure and efficient network services for UAVs. We propose a UAV cluster identity management module by combining blockchain, encryption algorithms, and digital signatures to enhance the security of UAV communication data transmission. Then, based on machine learning, deep learning, and malicious process detection technology, we propose a real-time secure situational awareness system for UAV cluster terminal devices to enhance the security of the operating environment for UAVs. Finally, we propose a data-trustworthy interconnection platform based on blockchain, smart contracts, and consensus algorithms to realize secure and efficient sharing and transmission of terminal data. The results of the experiments demonstrate the feasibility and effectiveness of our UAV network service architecture.

Ultrasonic- and IMU-Based High-Precision UAV Localization for the Low-Cost Autonomous Inspection in Oil and Gas Pressure Vessels

With the increasing demands for unmanned aerial vehicle (UAV) based autonomous inspections in the oil and gas industry, one of the challenging issues for 3D UAV positioning has emerged due to the satellite signal blocking. Considering the existing characteristics of the ultrasonic based technique, such as the low cost, extremely lightweight and high positioning accuracy, it can be promising as the potential solution. Nevertheless, the low position update rate and vulnerable positioning performance to the changing environment still limit its applications on UAV. Therefore, in this article, an ultrasonic and inertial measurement unit (IMU) based localisation algorithm and low cost UAV autonomous inspection system are presented. With the incorporation of the IMU, the position update rate, accuracy and stability of the algorithm can all be significantly improved. This is done by the adaptively estimated noise covariance matrices through the proposed adaptive extended Kalman filter (AEKF) algorithm and the added weighting factors. Followed by, an additional virtual observation process is presented to overcome the unavailability of the observation information for further performance improvement. Finally, extensive numerical results and field tests demonstrate that the proposed algorithm and system can achieve the high update rate, reliable, accurate and precision UAV positioning in oil and gas pressure vessels and are feasible for the UAV autonomous inspection in these environments.

Design and experiment of a binocular vision-based canopy volume extraction system for precision pesticide application by UAVs

When unmanned aerial vehicles (UAVs) are used for orchard chemicals application, accurate measurement of the canopy volume can provide decision support for determining pesticide dosages, flight parameters, and droplet sizes. Using binocular camera ranging, this study presents a novel canopy segmentation algorithm that preprocesses light detection ranging data to extract sub-grid canopy volumes. A binocular vision-based canopy volume extraction system for UAV chemical application was developed. The system utilizes multi-degree-of-freedom adaptive balance technology to ensure that the binocular camera can still vertically detect the canopy even when the flight attitude changes. Performance experiments were conducted using artificial fruit trees with different leaf densities and regular cardboard box as measurement targets. The canopy volume measurements indicate that the new model accurately detects target contours. When flying at 2 m/s, the maximum errors between system-measured and actual volumes were 6.58 and 9.37 % for the rectangular and triangular, respectively. Increasing speeds and attitudes lead to increased errors and measurement variations. However, the position of the system relative to the target does not cause significant differences in results. The maximum measurement errors between system-measured and actual LiDAR values were 6.44 and 9.17 % for high- and low-density canopies, respectively. These results demonstrate that the proposed system has high measurement accuracy and provides a reliable precision UAV pesticide-spraying control system for plant protection based on real-time canopy detection.

A comprehensive security and performance assessment of UAV authentication schemes

AbstractIn the past few years, unmanned aerial vehicles (UAVs) have significantly gained attention and popularity from industry, government, and academia. With their rapid development and deployment into the civilian airspace, UAVs play an important role in different applications, including goods delivery, search‐and‐rescue, and traffic monitoring. Therefore, providing secure communication through authentication models for UAVs is necessary for a successful and reliable flight mission. To satisfy such requirements, numerous authentication mechanisms have been proposed in the literature. However, the literature lacks a comprehensive study evaluating the security and performance of these solutions. In this article, we analyze the security and performance of 27 recent UAV authentication works by considering ten different key metrics. First, in the performance analysis, we show that the majority of UAV authentication schemes are lightweight in their communication cost. However, the storage overhead or the energy consumption is not reported by many authentication studies. Then, we reveal in the security analysis the widely employed formal models (i.e., abstract description of an authentication protocol through a mathematical model), while most of the studies lack coverage of many attacks that can target UAV systems. Afterwards, we highlight the challenges that need to be addressed in order to design and implement secure and reliable UAV authentication schemes. Finally, we summarize the lessons learned on the authentication strategies for UAVs to motivate promising direction for further research.

Reliability Theory and Practice for Unmanned Aerial Vehicles

Due to rapid advancements on the Internet of Things (IoT), unmanned aerial vehicles (UAVs), also known as drones, are transforming numerous military and civil application areas. UAVs aim to enhance the production efficiency, ensure safety, and reduce risk, particularly protecting the human workforce in the case of harsh and dangerous environments. Due to the mission-critical, business-critical, or safety-critical nature of the UAV applications, it is pivotal that UAVs perform reliably to deliver the required service during the intended mission time. Therefore, reliability is one of the essential requirements for designing and operating UAVs. This article presents a critical review of UAV reliability literature in both theoretical and practical research, pinpointing failure causes and reliability challenges of UAV systems, classifying and reflecting on the reliability modeling, analysis, and design methods for UAV systems and key subsystems. Some open research problems and opportunities are also discussed to highlight potential new challenges for designing reliable and resilient UAVs and UAV-assisted IoT systems.

Passive IR-UWB Localization System for UAV-Based Electric Facility Inspection During GPS Outage

A practical localization system is proposed for a substation inspection unmanned aerial vehicle (UAV) performing its mission under global positioning system (GPS) signal blockage. The sensor hardware consists of a single transmitter and a cruciform receiver array, which produces the range difference (RD) information used for UAV positioning. The use of impulse-radio ultra wide-band (IR-UWB) devices secures the robustness against RF interference often caused by an exogenous electromagnetic field. With this hardware configuration, the UAV localization can be formulated as the state estimation problem for an uncertain linear state space model and be solved by using the computationally efficient robust weighted least squares (RWLS) estimator. Since the standard RWLS estimator could be sensitive to the imperfectness of prior knowledge on the noise statistic, a geometric constraint expressed in terms of a UAV position is exploited to secure the reliable localization performance in practice. Through experimental results, it is verified that the proposed solution provides reliable UAV positioning performance despite using the imperfect measurement noise statistics for the localization system.

Efficient SfM for Large-Scale UAV Images Based on Graph-Indexed BoW and Parallel-Constructed BA Optimization

Structure from Motion (SfM) for large-scale UAV (Unmanned Aerial Vehicle) images has been widely used in the fields of photogrammetry and computer vision. Its efficiency, however, decreases dramatically as well as with the memory occupation rising steeply due to the explosion of data volume and the iterative BA (bundle adjustment) optimization. In this paper, an efficient SfM solution is proposed to solve the low-efficiency and high memory consumption problems. First, an algorithm is designed to find UAV image match pairs based on a graph-indexed bag-of-words (BoW) model (GIBoW), which treats visual words as vertices and link relations as edges to build a small-world graph structure. The small-world graph structure can be used to search the nearest-neighbor visual word for query features with extremely high efficiency. Reliable UAV image match pairs can effectively improve feature matching efficiency. Second, a central bundle adjustment with object point-wise parallel construction of the Schur complement (PSCBA) is proposed, which is designed as the combination of the LM (Levenberg–Marquardt) algorithm with the preconditioned conjugate gradients (PCG). The PSCBA method can dramatically reduce the memory consumption in both error and normal equations, as well as improve efficiency. Finally, by using four UAV datasets, the effectiveness of the proposed SfM solution is verified through comprehensive analysis and comparison. The experimental results show that compared with Colmap-Bow and Dbow2, the proposed graph index BoW retrieval algorithm improves the efficiency of image match pair selection with an acceleration ratio ranging from 3 to 7. Meanwhile, the parallel-constructed BA optimization algorithm can achieve accurate bundle adjustment results with an acceleration ratio by 2 to 7 times and reduce memory occupation by 2 to 3 times compared with the BA optimization using Ceres solver. For large-scale UAV images, the proposed method is an effective and reliable solution.

Mobile Networks’ Support for Large-Scale UAV Services

The services based on Unmanned Aerial Vehicles (UAVs) have started to be used in many countries but not on a large scale yet. The paper describes the present status of UAV services and a concept of a solution for large-scale deployment of safe and reliable UAV services that use the 5G network for communication with UAVs. Based on the Standards Developing Organizations and related industry fora activities, unmanned traffic and airspace regulations, especially the commonly supported concept of Unmanned Aircraft Systems Traffic Management (UTM), UAV-related communication requirements and 5G System (5GS) features, we have developed an alternative approach to the integration of UAV and 5GS. The proposed concept includes a set of enablers that can be provided by Mobile Network Operators in order to not only support but also leverage UAV services. As work on many items is still in progress, we identify a list of open issues and challenges and present them at the end of the paper with the main focus on the MNO–UTM provider relationship and safe flights.

Reliable UAV Monitoring System Using Deep Learning Approaches

In recent years, unmanned aerial vehicles (UAV) or drones have become ubiquitous in our daily lives, bringing great convenience to our lives and playing a pivotal role in future wireless networks and the Internet of things. One of the major problems associated with the UAV is the heterogeneous nature of such deployments; this heterogeneity poses many challenges, particularly in the areas of security and privacy. The key to solving these problems is to accurately identify and authenticate drones. In this article, a reliable UAV identify framework based on radio frequency fingerprint is proposed. First, we established a wireless signal label architecture and systematically collected, analyzed, and recorded the radio frequency signals of different UAVs in different flight modes and different distances in the telemetry link, and established UAV signal datasets. Then, the intelligent algorithm and anti-UAV system are designed by using the collected dataset, and the feasibility of the developed dataset for detecting and identifying UAVs is verified by using machine learning and deep learning. The simulation results show that under the condition of Gaussian white noise, the method based on deep learning achieves high reliability, and when the SNR is not less than 5dB, the model achieves more than 95% of the monitoring and recognition accuracy. Finally, we discussed the possible applications of the dataset in the future.

Tracker Meets Night: A Transformer Enhancer for UAV Tracking

Most previous progress in object tracking is realized in daytime scenes with favorable illumination. State-of-the-arts can hardly carry on their superiority at night so far, thereby considerably blocking the broadening of visual tracking-related unmanned aerial vehicle (UAV) applications. To realize reliable UAV tracking at night, a spatial-channel Transformer-based low-light enhancer (namely SCT), which is trained in a novel task-inspired manner, is proposed and plugged prior to tracking approaches. To achieve semantic-level low-light enhancement targeting the high-level task, the novel spatial-channel attention module is proposed to model global information while preserving local context. In the enhancement process, SCT denoises and illuminates nighttime images simultaneously through a robust non-linear curve projection. Moreover, to provide a comprehensive evaluation, we construct a challenging nighttime tracking benchmark, namely DarkTrack2021, which contains 110 challenging sequences with over 100 K frames in total. Evaluations on both the public UAVDark135 benchmark and the newly constructed DarkTrack2021 benchmark show that the task-inspired design enables SCT with significant performance gains for nighttime UAV tracking compared with other top-ranked low-light enhancers. Real-world tests on a typical UAV platform further verify the practicability of the proposed approach. The DarkTrack2021 benchmark and the code of the proposed approach are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/vision4robotics/SCT</uri> .

Citizen-Science with off-the-shelf UAV for Coastal Monitoring

Accurate and repetitive observation and quantification of the shoreline position and the coastal feature are essential aspects of coastal management and planning. Commonly, the dataset associated with coastal observation and quantification is obtained with in-situ coastal surveys. The current methods are mostly quite expensive, time-consuming, and require trained individuals to do the task. With the availability of the off-the-shelf low cost, lightweight, and reliable Unmanned Aerial Vehicle (UAV) with the advances of the algorithms such as structure-from-motion (SfM), UAV-based measurement becomes a promising tool. Open SfM initiative, open topographical database, and UAV communities are the enablers that make it possible to collect accurate and frequent coastal monitoring and democratize data. This paper provides a review and discussions that highlight the possibility of conducting scientific coastal monitoring or collaborating with the public. Literature was examined for the advances in coastal monitoring, challenges, and recommendations. We identified and proposed the use of UAV along with the strategies and systems to encourage citizen-led UAV observation for coastal monitoring while attaining the quality.

A Novel Link Failure Detection and Switching Algorithm for Dissimilar Redundant UAV Communication

Unmanned Aerial Vehicles (UAVs) used for humanitarian applications require simple, accessible and reliable components. For example, a communication system between UAV and the Ground Control Station (GCS) is essential in order to monitor UAV status; various communication protocols are available in the industry. Such systems must be simple for non-technical personnel (e.g., healthcare workers) to operate. In this study, a novel link failure detection and switching algorithm was proposed for a dissimilar redundant UAV communication system designed for long-range vaccine delivery in rural areas. The algorithm would ease the workload of the operators and address a research gap in the design of such algorithms. A two-layer design is proposed: A baseline layer using the heartbeat method, and optimisations to speed up local failure detection. To dynamically tune the heartbeat timeout for the algorithm’s baseline without intervention from ground operators, the modified Jacobson’s algorithm was used. Lab simulations found that the algorithm was generally accurate in converging to an optimal value, but has less satisfactory performance at poor or unpredictable connectivity, or when link switches get triggered frequently. Improvements have been suggested for the algorithm. This study contributes to ongoing research on ensuring reliable UAV communication for humanitarian purposes.

Enabling Reliable UAV Control by Utilizing Multiple Protocols and Paths for Transmitting Duplicated Control Packets.

In the last ten years, supported by the advances in technologies for unmanned aerial vehicles (UAVs), UAVs have developed rapidly and are utilized for a wide range of applications. To operate UAVs safely, by exchanging control packets continuously, operators should be able to monitor UAVs in real-time and deal with any problems immediately. However, due to any networking problems or unstable wireless communications, control packets can be lost or transmissions can be delayed, which causes the unstable drone control. To overcome this limitation, in this paper, we propose MuTran for enabling reliable UAV control. MuTran considers the packet type and duplicates only control packets, not data packets. After that, MuTran transmits the original and duplicate packets through multiple protocols and paths to improve the reliability of control packet transmissions. We designed MuTran and conducted a lot of theoretical analyses to demonstrate the validity of MuTran and analyze it from various aspects. We implemented MuTran on real devices and evaluated MuTran using the devices. We conducted experiments to verify the limitations of the existing systems and demonstrate that control packets can be transmitted more stably by using MuTran. Through the analysis and experimental results, we confirmed that MuTran reduces the control packet transfer delay, which improves the reliability and stability of controlling UAVs.

High-Quality UAV-Based Orthophotos for Cadastral Mapping: Guidance for Optimal Flight Configurations

During the past years, unmanned aerial vehicles (UAVs) gained importance as a tool to quickly collect high-resolution imagery as base data for cadastral mapping. However, the fact that UAV-derived geospatial information supports decision-making processes involving people’s land rights ultimately raises questions about data quality and accuracy. In this vein, this paper investigates different flight configurations to give guidance for efficient and reliable UAV data acquisition. Imagery from six study areas across Europe and Africa provide the basis for an integrated quality assessment including three main aspects: (1) the impact of land cover on the number of tie-points as an indication on how well bundle block adjustment can be performed, (2) the impact of the number of ground control points (GCPs) on the final geometric accuracy, and (3) the impact of different flight plans on the extractability of cadastral features. The results suggest that scene context, flight configuration, and GCP setup significantly impact the final data quality and subsequent automatic delineation of visual cadastral boundaries. Moreover, even though the root mean square error of checkpoint residuals as a commonly accepted error measure is within a range of few centimeters in all datasets, this study reveals large discrepancies of the accuracy and the completeness of automatically detected cadastral features for orthophotos generated from different flight plans. With its unique combination of methods and integration of various study sites, the results and recommendations presented in this paper can help land professionals and bottom-up initiatives alike to optimize existing and future UAV data collection workflows.