Development Of Unmanned Aerial Vehicles Research Articles

UAV control in autonomous object-goal navigation: a systematic literature review

Research interest in autonomous control of unmanned aerial vehicles (UAVs) has increased rapidly over the past decade. They are now widely used in civilian, military, and private areas. Applications include surveillance, search and rescue, and delivery tasks. More broadly, they excel at solving problems where a significant amount of space must be covered and traveled. However, using UAVs to solve navigation problems with full autonomy necessitates the mastering of complex subtasks. A solution that includes control, planning, localization, and mapping remains an open challenge. Object-goal navigation contains the same navigation problems where the main objective is to reach a target object. The search and identification of this target are central to the vehicle’s navigation. This requires an understanding of what it is and where it can be located to move around the scene. This paper presents a systematic literature review on object-goal navigation and its subtasks, using autonomous UAVs. Survey taxonomies were found for the tasks and methods behind navigation and target localization problems using UAVs. The review analyzed 67 articles found between 2011 and 2022. They were found in the ACM, IEEE Xplore, WebOfScience, Science Direct, and Scopus databases. This review revealed essential issues related to autonomous navigation task dependencies. Moreover, it highlighted gaps in UAV development and framework standardization. Open challenges for autonomous UAV control for object-goal navigation must address the research on finding methods for problems. For example, autonomy level and comparison metrics, considering safety, ethics, and legal implications.

A Study on Anti-Jamming Algorithms in Low-Earth-Orbit Satellite Signal-of-Opportunity Positioning Systems for Unmanned Aerial Vehicles

Low-Earth-Orbit (LEO) satellite Signal-of-Opportunity (SOP) positioning technology has gradually matured to meet the accuracy requirements for unmanned aerial vehicle (UAV) positioning in daily scenarios. Advancements in miniaturization technology for positioning terminals have also made this technology’s application to UAV positioning crucial for UAV development. However, in the increasingly complex electromagnetic environment, there remains a significant risk of degradation in positioning performance for UAVs in LEO satellite SOP positioning due to unintentional or malicious jamming. Furthermore, there is a lack of in-depth research from scholars both domestically and internationally on the anti-jamming capabilities of LEO satellite SOP positioning technology. Due to significant differences in the downlink signal characteristics between LEO satellites and Global Navigation Satellite System (GNSS) signals based on Medium Earth Orbit (MEO) or Geostationary Earth Orbit (GEO) satellites, the anti-jamming research results of traditional satellite navigation systems cannot be directly applied. This study addresses the narrow bandwidth and high signal-to-noise ratio (SNR) characteristics of signals from LEO satellite constellations. We propose a Consecutive Iteration based on Signal Cancellation (SCCI) algorithm, which significantly reduces errors during the model fitting process. Additionally, an adaptive variable convergence factor was designed to simultaneously balance convergence speed and steady-state error during the iteration process. Compared to traditional algorithms, simulation and experimental results demonstrated that the proposed algorithm enhances the effectiveness of jamming threshold settings under narrow bandwidth and high-power conditions. In the context of LEO satellite jamming scenarios, it improves the frequency-domain anti-jamming performance significantly and holds high application value for drone positioning.

Innovative Hybrid UAV Design, Development, and Manufacture for Forest Preservation and Acoustic Surveillance

The research described in this paper focuses on the development of an innovative unmanned aerial vehicle (UAV) tailored for a specific mission: detecting the acoustic signature emitted by chainsaws, identifying deforestation, and reporting its location for legality assessment. Various calculations were conducted to determine the optimal solution, resulting in the choice of a fixed-wing UAV. A comparative analysis between tri-rotor and quadcopter systems was performed, leading to the selection of the tri-rotor configuration. The primary objective of this study is to design an innovative hybrid UAV concept with key features including a fixed-wing design and integrated VTOL (vertical takeoff and landing) capability in the experimental model. The aircraft has been constructed using advanced materials such as fiber-reinforced polymer composites, manufactured using both conventional and advanced techniques like continuous fiber additive manufacturing and the use of a polymer matrix. Additionally, the aerodynamic configuration is optimized to achieve a cruise speed of approximately 50 km/h and a flight autonomy exceeding 3 h. The UAV has been equipped with payloads for mounting sensors to collect meteorological data, and crucially, the VTOL system has been optimized to vectorize thrust for improved performance during the transition from hover to cruise flight. This paper details the entire manufacturing and assembly process of the drone, covering both the structural framework and associated electrical installations. A dedicated sound detection system is incorporated into the drone to identify chainsaw noise, with the aim of preventing deforestation.

Deep reinforcement learning based computing offloading in unmanned aerial vehicles for disaster management

Abstract The emergence of Internet of Things enabled with mobile computing has the applications in the field of unmanned aerial vehicle (UAV) development. The development of mobile edge computational offloading in UAV is dependent on low latency applications such as disaster management, Forest fire control and remote operations. The task completion efficiency is improved by means of using edge intelligence algorithm and the optimal offloading policy is constructed on the application of deep reinforcement learning (DRL) in order to fulfill the target demand and to ease the transmission delay. The joint optimization curtails the weighted sum of average energy consumption and execution delay. This edge intelligence algorithm combined with DRL network exploits computing operation to increase the probability that at least one of the tracking and data transmission is usable. The proposed joint optimization significantly performs well in terms of execution delay, offloading cost and effective convergence over the prevailing methodologies proposed for UAV development. The proposed DRL enables the UAV to real-time decisions based on the disaster scenario and computing resources availability.

Analysis of UAV data communication stability method in extreme environment

In order to get in touch with the affected area smoothly after the disaster, this paper uses drones as air base stations. Their high mobility and sensitivity make them apt for such critical roles. However, during extreme weather conditions, these unmanned aerial vehicles (UAVs) encounter various challenges that can impair their stability and, consequently, their ability to serve as reliable air base stations. As a result, signal transmission can be interrupted or severely limited. Among the common factors affecting UAV stability are strong winds, temperature fluctuations, heavy rainfall, and electromagnetic interference. These factors can cause the UAVs to deviate from their intended flight paths, drop in altitude, or even lose connection with the ground control. As a consequence, the reliability of communication, so critical in emergency situations, gets compromised. To address the problem of degraded UAV stability, several solutions have been proposed and implemented. One approach involves enhancing the drone's onboard stabilization systems, incorporating advanced algorithms that allow it to autonomously adjust to environmental changes. Another strategy is the deployment of a network of drones, ensuring redundancy. If one drone faces difficulties, another can take over, maintaining the communication link. Furthermore, advances in materials science have led to the development of UAVs with more robust structures, capable of withstanding the rigors of adverse weather conditions. Lastly, the use of ground-based boosters or repeaters can amplify signals, ensuring that even if a UAV's transmission is weak, the communication remains uninterrupted.

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.

IMPLEMENTACIÓN DE UN SISTEMA DE COMUNICACIÓN Y VIDEOVIGILANCIA INTEGRADO EN UN UAV PARA MISIONES ESTRATEGICAS

Unmanned Aerial Vehicles (UAVs) have been widely used in the military for monitoring and national defence activities. One of the main advantages of using UAVs is that they reduce loss of life, as they can be controlled remotely or with the help of algorithms, allowing high-risk manoeuvres to be carried out without fatal consequences. One of the objectives of using UAV systems is to reduce the costs of acquiring and operating conventional aircraft. One of the main problems in the development of UAVs the design and dimensioning of communication systems. This paper presents the development of a communication system for strategic missions with the validation of the communication link calculated through the implementation of a video surveillance system integrated in a UAV. The equipment of the communication and video transmission system was based on free commercial hardware and free software that allows the sending of high-resolution images. The UAV operation tests have allowed maintaining the telemetry link and high-quality video transmission at approximately 5 km. Key Words: energy efficiency, historic districts, 3D cities, building typology, categorization tool

A Survey of Object Detection for UAVs Based on Deep Learning

With the rapid development of object detection technology for unmanned aerial vehicles (UAVs), it is convenient to collect data from UAV aerial photographs. They have a wide range of applications in several fields, such as monitoring, geological exploration, precision agriculture, and disaster early warning. In recent years, many methods based on artificial intelligence have been proposed for UAV object detection, and deep learning is a key area in this field. Significant progress has been achieved in the area of deep-learning-based UAV object detection. Thus, this paper presents a review of recent research on deep-learning-based UAV object detection. This survey provides an overview of the development of UAVs and summarizes the deep-learning-based methods in object detection for UAVs. In addition, the key issues in UAV object detection are analyzed, such as small object detection, object detection under complex backgrounds, object rotation, scale change, and category imbalance problems. Then, some representative solutions based on deep learning for these issues are summarized. Finally, future research directions in the field of UAV object detection are discussed.

Review of Key Technologies of Rotary-Wing Mars UAVs for Mars Exploration

The sparse atmosphere on the surface of Mars provides the necessary flight conditions for Mars unmanned aerial vehicles (UAVs) to perform low-altitude flights. This work presents a comprehensive overview of key technologies in the development of Mars UAVs, with a specific focus on rotary-wing Mars UAVs. It summarizes prototypes of rotary-wing Mars UAVs developed by various global research institutions. It reviews essential technologies in rotary-wing Mars UAV research, including the Mars near-surface atmospheric environment, aerodynamic characteristics, and principles of low-pressure flight control. This work also summarizes various experimental setups and ground test results for rotary-wing Mars UAVs. Furthermore, it discusses the future development trends of rotary-wing Mars UAVs.

Detection of Pine Wood Nematode Infestation Based on Improved YOLOv5

Pine wilt disease is a destructive forest disease caused by the parasitism of pine wood nematodes inside pine trees. Once infected, pine trees quickly wither and die due to the inability to drain water, hence it is also known as pine tree withering disease. It is mainly spread by pine sawyer beetles, with the characteristics of fast dissemination, rapid onset, and high mortality rate, making it a significant global plant disease. Failure to effectively control the pine wilt disease in a timely manner will result in the massive death of pine trees in a short period of time in forest areas. With the continuous development and popularization of artificial intelligence and UAV (Unmanned Aerial Vehicle) technology, combining various methods can timely detect diseased trees, and performing timely treatment and protection can greatly save the time and personnel costs of biological pest control. While it is also conducive to promoting the work of multiple related disciplines, such as bioengineering and greening engineering. In this paper, we first conducted a large number of aerial surveys on the forest area through UAV, collected relevant data, and preprocessed the obtained data, including data augmentation, cleaning, filtering, deduplication, formatting, etc., to ensure data quality and accuracy. Then, we labeled the data, and made certain improvements to the original YOLOv5. We added a new RRAM (Recurrent Residual Attention Module) to the original network model, which enables the network to timely focus on important information in redundant data, thus improving the network’s performance. Compared with the original YOLOv5, our network has a stronger performance.

Innovative research on the application of TIRZ theory to multi-mode UAVs

According to the current development of Unmanned Aerial Vehicles (UAVs), it can be known that there are no cases where underwater drones and aerial drones are used in parallel. They are all affected by air and water resistance and are easily disturbed in various environments, making it difficult to move effectively. This research uses TRIZ theory to create a multi-modal unmanned vehicle to innovate and improve the existing situation. The type of equipment produced through this research can effectively move in the air, land, and water. The scope includes TRIZ theory, bionic technology, multi-modal innovative design, research, and application of UAV innovation, and monitoring the improvement of its operational resistance through fluid analysis. The output of this research will effectively promote the development of Multi-mode UAVs and improve the problems of existing UAVs.

Simulation of tilt-rotor UAV flight dynamics in horizontal flight

Tilt-rotor unmanned aerial vehicles (UAV) have gained significant importance in the aeronautical industry due to their ability to transition between vertical and horizontal flight. One of the important steps in the development of such UAVs is to assess their performance and stability characteristics. Simulation of flight dynamics is an essential tool that enables the designers to test and optimize the flight characteristics of these UAVs. This paper presents a complete procedure for developing a 6-degree-of-freedom flight dynamics model of a tilt-rotor UAV in fixed-wing mode using a physics-based modeling approach. All necessary sub-models are developed and integrated into the Simulink simulation environment to allow for predicting the UAV’s dynamic response. The developed model is verified for correct operation by simulating horizontal steady-level flight and exciting its natural longitudinal modes.

QUAV attitude control based on linear active disturbance rejection control

Unmanned Aerial Vehicle (UAV) are currently gaining popularity. This paper proposes a method to apply Linear Active Disturbance Rejection Control (LADRC) to Quadrotor Unmanned Aerial Vehicle (QUAV) controller to optimize the traditional PID controller. Firstly, the application and shortcomings of traditional PID control in UAV are introduced, and the LADRC method is proposed. Then linear simplification and Parameter Setting of ADRC are carried out. In the Simulink environment, according to the mathematical model of the QUAV, a QUAV dynamics simulation platform is established. Finally, according to different control channels, different control algorithms are designed, and tracking models are introduced in various attitudes to simulate and verify the control effect of LADRC. The results show that the LADRC controller is effective, the LADRC can be effectively combined with the traditional PID control. The application in the QUAV can realize more precise QUAV speed tracking control and the stable flight of the QUAV. Compared with the traditional PID controller, under the experimental conditions of this paper, the LADRC controller has more precise control accuracy and more efficient control efficiency. Finally, this paper summarizes the design of LADRC and makes a brief outlook on the development of UAVs.

Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review

Real-time object detection based on UAV remote sensing is widely required in different scenarios. In the past 20 years, with the development of unmanned aerial vehicles (UAV), remote sensing technology, deep learning technology, and edge computing technology, research on UAV real-time object detection in different fields has become increasingly important. However, since real-time UAV object detection is a comprehensive task involving hardware, algorithms, and other components, the complete implementation of real-time object detection is often overlooked. Although there is a large amount of literature on real-time object detection based on UAV remote sensing, little attention has been given to its workflow. This paper aims to systematically review previous studies about UAV real-time object detection from application scenarios, hardware selection, real-time detection paradigms, detection algorithms and their optimization technologies, and evaluation metrics. Through visual and narrative analyses, the conclusions cover all proposed research questions. Real-time object detection is more in demand in scenarios such as emergency rescue and precision agriculture. Multi-rotor UAVs and RGB images are of more interest in applications, and real-time detection mainly uses edge computing with documented processing strategies. GPU-based edge computing platforms are widely used, and deep learning algorithms is preferred for real-time detection. Meanwhile, optimization algorithms need to be focused on resource-limited computing platform deployment, such as lightweight convolutional layers, etc. In addition to accuracy, speed, latency, and energy are equally important evaluation metrics. Finally, this paper thoroughly discusses the challenges of sensor-, edge computing-, and algorithm-related lightweight technologies in real-time object detection. It also discusses the prospective impact of future developments in autonomous UAVs and communications on UAV real-time target detection.

Toward Optimal Deployment of UAV Relays in UAV-Assisted Internet of Vehicles

The past decade has witnessed an explosive growth of unmanned aerial vehicles (UAVs), which have attracted great attention in assisting Internet of Vehicles (IoV) communication systems thanks to the advantages of cost-effectiveness, high maneuverability, and flexible deployment. Yet several issues, such as UAVs' transmit power and energy resources as well as the high-speed mobility of IoV user equipment (VUE), pose challenges to the development of UAVs in UAV-assisted IoV networks. To provide reliable and high-throughput wireless links for VUEs in a given ground area, this paper considers a scenario in which a ground VUE is facing difficulties in receiving service from a ground roadside unit (RSU) owing to possible sudden RSU malfunction or poor signal coverage. In this case, UAVs are generally deployed in the air, serving as relays to establish communication links between uncovered VUE and the neighboring RSU. To maximize the system capacity, we investigate the deployment problem of UAV relays under single-UAV relaying and multi-UAV relaying cases upon considering VUE mobility, and exploit the optimal deployment strategy of UAV relays under fixed total power consumption. Comprehensive simulation results verify that the proposed algorithm can outperform benchmark methods on significant critical indicators such as the average achievable user rate, and the outage probability. Also, evaluations and analyses have highlighted the effects of key system parameters (e.g., altitude, number, and transmit power of UAV relays) on optimal UAV relay deployment position and system performance.

Coverage Optimization Based on Airborne Fog Computing for Internet of Medical Things

The ubiquitous usage of Internet of Medical Things (IoMT) enables real-time health monitoring in daily life. However, a huge amount of generated medical data causes great trouble to IoMT devices, owing to their power-constrained and computation-limited features. On the other hand, unmanned aerial vehicles (UAVs) can achieve a seamless wireless connection to IoMT devices. In this article, we leverage UAVs as the aerial fog nodes to provide computational resources to the IoMT devices. We aim to minimize the number of UAVs for covering the IoMT devices on the ground. This problem is modeled as a discrete nonlinear combinatory optimization problem with NP-hardness. A particle swarm optimization (PSO)-based strategy is put forward to solve this problem. In particular, we introduce greedy heuristic to speed up the searching process. The experimental results show that the proposed algorithms GASM_a and GASM_b can achieve better performance compared to the greedy and random approaches in terms of optimal values. In addition, the enhanced algorithm GASM_b can obtain the better result than GASM_a. Meanwhile, the execution time is also averagely increased by 16.51% compared to GASM_a. Increasingly, low hardware prices enable large-scale deployment of UAVs for IoMT in healthcare systems. Our study shows the advantages in wireless coverage for ground patients by UAVs in terms of efficiency, and cost that will contribute to the development of UAV assisted healthcare systems.

A Review on UAV-Based Applications for Plant Disease Detection and Monitoring

Remote sensing technology is vital for precision agriculture, aiding in early issue detection, resource management, and environmentally friendly practices. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Given the growing body of scholarly research centered on UAV-based disease detection, a comprehensive review and analysis of current studies becomes imperative to provide a panoramic view of evolving methodologies in plant disease monitoring and to strategically evaluate the potential and limitations of such strategies. This study undertakes a systematic quantitative literature review to summarize existing literature and discern current research trends in UAV-based applications for plant disease detection and monitoring. Results reveal a global disparity in research on the topic, with Asian countries being the top contributing countries (43 out of 103 papers). World regions such as Oceania and Africa exhibit comparatively lesser representation. To date, research has largely focused on diseases affecting wheat, sugar beet, potato, maize, and grapevine. Multispectral, reg-green-blue, and hyperspectral sensors were most often used to detect and identify disease symptoms, with current trends pointing to approaches integrating multiple sensors and the use of machine learning and deep learning techniques. Future research should prioritize (i) development of cost-effective and user-friendly UAVs, (ii) integration with emerging agricultural technologies, (iii) improved data acquisition and processing efficiency (iv) diverse testing scenarios, and (v) ethical considerations through proper regulations.

A GPS-Adaptive Spoofing Detection Method for the Small UAV Cluster

The small UAV (unmanned aerial vehicle) cluster has become an important trend in the development of UAVs because it has the advantages of being unmanned, having a small size and low cost, and ability to complete many collaborative tasks. Meanwhile, the problem of GPS spoofing attacks faced by submachines has become an urgent security problem for the UAV cluster. In this paper, a GPS-adaptive spoofing detection (ASD) method based on UAV cluster cooperative positioning is proposed to solve the above problem. The specific technical scheme mainly includes two detection mechanisms: the GPS spoofing signal detection (SSD) mechanism based on cluster cooperative positioning and the relative security machine optimal marking (RSOM) mechanism. The SSD mechanism starts when the cluster enters the task state, and it can detect all threats to the cluster caused by one GPS signal spoofing source in the task environment; when the function range of the mechanism is exceeded, that is, there is more than one spoofing source and more than one UAV is attacked by different spoofing sources, the RSOM mechanism is triggered. The ASD algorithm proposed in this work can detect spoofing in a variety of complex GPS spoofing threat environments and is able to ensure the cluster formation and task completion. Moreover, it has the advantages of a lightweight calculation level, strong applicability, and high real-time performance.

Creation of a Vivaldi-Based Circular Antenna Array for Broadband Relay Channels

Introduction. Unmanned aerial vehicles (UAVs) are rapidly gaining in popularity. The UVA development requires active antenna systems capable of forming a narrow beam of the main lobe of the radiation pattern. Although numerous studies have considered specialized antenna arrays with a wide range of scanning angles, the location of such systems on UAVs remains under-investigated. The development of such arrays for UAVs will enable the creation of a secure relay broadband channel with a few repeaters.Aim. Development of a broadband antenna array for UAVs with the possibility of setting the main lobe of the radiation pattern in any direction of the azimuthal plane, for use in relay tasks.Materials and methods. An emitter model and a circular antenna array on its basis was developed in the Ansys HFSS electromagnetic modeling package.Results. The dependence of the directional coefficient and the gain for an array consisting of 8 and 16 elements was shown. Voltage standing-wave ratio dependences and directivity patterns that satisfy the conditions of retransmission in a wide-frequency band using UAVs were obtained. Recommendations on the number of active elements in a circular antenna array that ensure the maximum gain (directivity) were formulated.Conclusion. Technical solutions that can be used in the development of UAVs are proposed. The system can be further improved by optimizing the antenna array elements and using a cylindrical or hemispherical array.

UAV sensor data applications with deep neural networks: A comprehensive survey

The use of Unmanned Aerial Vehicles (UAVs) has become increasingly popular in recent years, leading to a surge in research on this topic that is widely represented in the literature. They would be examined in accordance with the requirements of the sensors they house, their operating limits, and the output information they give. Since perception, planning, localization, and control are the major tasks of UAVs, an outstanding problem solver “Deep learning” is recently used in these systems. It is ideally suited for UAV applications because of its great capacity for learning representations from the complicated data received in actual situations. This paper provides an extensive review and in-depth analysis of current advancements in UAVs that have applications with DNN, as well as a quick summary of research and development during the previous ten years. In terms of UAVs flight monitoring, remote sensing and vision capability, and energy modeling, this survey provides a roadmap for understanding the sequential development of sophisticated UAVs by reviewing 173 retrieved papers. For this purpose, first of all, the main titles of the studies carried out for UAV applications were gathered under a taxonomy, and then information about the studies carried out in recent years related to these main research fields was given. In the last part of the study, attention was drawn to the still challenging points, such as autonomous fault detection, path planning, and onboard event detection, which are anticipated to be the future trends involving UAV applications.