Capabilities Of Unmanned Aerial Vehicles Research Articles

Assessing the contribution of RGB VIs in improving building extraction from RGB-UAV images

Buildings are a fundamental component of the built environment, and accurate information regarding their size, location, and distribution is vital for various purposes. The ever-increasing capabilities of unmanned aerial vehicles (UAVs) have sparked an interest in exploring various techniques to delineate buildings from the very high-resolution images obtained from UAV photogrammetry. However, the limited spectral information in UAV images, particularly the number of bands, can hinder the differentiation between various materials and objects. This setback can affect the ability to distinguish between different materials and objects. To address this limitation, vegetative ındices (VIs) have been employed to enhance the spectral strength of UAV orthophotos, thereby improving building classification. The objective of this study is to evaluate the contribution of four specific VIs: the green leaf index (GLI), red-green-blue vegetation index (RGBVI), visual atmospherically resistant index (VARI), and triangular greenness index (TGI). The significance of this contribution lies in assessing the potential of each VI to enhance building classification. The approach utilized the geographic object-based image analysis (GeoBIA) approach and a random forest classifier. To achieve this aim, five datasets were created, with each dataset comprising the RGB-UAV image and a corresponding RGB VI. The experimental results on the test dataset and a post-classification assessment indicated a general improvement in the classification when the VIs were added to the RGB orthophoto.

About the possibility of protecting UAVs from suppression of control signals

Unmanned aerial vehicles (UAVs) are widely used in a wide variety of sectors, both in the national economy and for military purposes. In the agricultural industry, they are used to monitor farmland, analyze soil samples, and even for livestock grazing. In search and rescue operations, UAVs are used to rescue people from life-threatening situations, and each time this technology proves its usefulness more than ever. UAVs are gradually finding their application in retail trade, transportation, entertainment, home security and even in construction using 3D printers. Simultaneously with the growth of the capabilities of UAVs, methods and means were developed to counteract them. The most affordable method of combating an UAV is to suppress the radio signals used to control it. As a result, the UAV loses contact with the operator, which leads to malfunctions in its operation. Therefore, the relevance of developing methods and means of combating suppression of control signals cannot be doubted and is becoming more and more necessary. The purpose of the work is to develop a scheme of the device (protection unit) that protects the UAV from interference radio signals in the operating mode. The peculiarity of the developed device is that in the absence of interference signals on the standard frequency of the UAV, the protection unit receives the operator's control signal and transmits it to the input of the UAV receiver, which moves in the operating mode and performs the tasks assigned to it. In the event of the appearance of interference radio signals on the standard frequency, it independently detects them and, using the UAV transmitter, issues a command to the operator to change the operating frequency to an additional frequency. At the same time, the output frequency of the protection unit remains constant and is equal to the standard frequency of the UAV. Thus, the protection unit is a simple and inexpensive device that detects interference signals and automatically switches the control of the UAV to an additional operating frequency. A functional diagram of the receiving device was developed, and a detailed description of its operation was provided.

Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network

Pine wood nematode disease, commonly referred to as pine wilt, poses a grave threat to forest health, leading to profound ecological and economic impacts. Originating from the pine wood nematode, this disease not only causes the demise of pine trees but also casts a long shadow over the entire forest ecosystem. The accurate identification of infected trees stands as a pivotal initial step in developing effective prevention and control measures for pine wilt. Nevertheless, existing identification methods face challenges in precisely determining the disease status of individual pine trees, impeding early detection and efficient intervention. In this study, we leverage the capabilities of unmanned aerial vehicle (UAV) remote sensing technology and integrate the VGG classical small convolutional kernel network with U-Net to detect diseased pine trees. This cutting-edge approach captures the spatial and characteristic intricacies of infected trees, converting them into high-dimensional features through multiple convolutions within the VGG network. This method significantly reduces the parameter count while enhancing the sensing range. The results obtained from our validation set are remarkably promising, achieving a Mean Intersection over Union (MIoU) of 81.62%, a Mean Pixel Accuracy (MPA) of 85.13%, an Accuracy of 99.13%, and an F1 Score of 88.50%. These figures surpass those obtained using other methods such as ResNet50 and DeepLab v3+. The methodology presented in this research facilitates rapid and accurate monitoring of pine trees infected with nematodes, offering invaluable technical assistance in the prevention and management of pine wilt disease.

A Dynamic Visual SLAM System Incorporating Object Tracking for UAVs

The capability of unmanned aerial vehicles (UAVs) to capture and utilize dynamic object information assumes critical significance for decision making and scene understanding. This paper presents a method for UAV relative positioning and target tracking based on a visual simultaneousocalization and mapping (SLAM) framework. By integrating an object detection neural network into the SLAM framework, this method can detect moving objects and effectively reconstruct the 3D map of the environment from image sequences. For multiple object tracking tasks, we combine the region matching of semantic detection boxes and the point matching of the optical flow method to perform dynamic object association. This joint association strategy can prevent trackingoss due to the small proportion of the object in the whole image sequence. To address the problem ofacking scale information in the visual SLAM system, we recover the altitude data based on a RANSAC-based plane estimation approach. The proposed method is tested on both the self-created UAV dataset and the KITTI dataset to evaluate its performance. The results demonstrate the robustness and effectiveness of the solution in facilitating UAV flights.

Autonomous Landing Spot Detection for Unmanned Aerial Vehicles Based on Monocular Vision

Abstract. Enhancing the autonomous landing capability of unmanned aerial vehicles (UAVs) is of great significance for improving their operational efficiency and field survival capabilities. To this end, we propose a real-time autonomous landing spot detection method for UAVs. Firstly, the pose of the UAV at any given time and the initial three-dimensional point cloud of the scene are estimated using simultaneous localization and mapping (SLAM) techniques. Then, since the initial point cloud is sparse and cannot be used for terrain analysis, we generate a voxel-based elevation map of the scene, which can establish interconnectivity among the points. Finally, we propose a shift-box strategy to comprehensively analyze various terrain factors in the elevation map, determine the landing spot for UAVs, and update the landing spot in real time. The UAV flight experiments conducted in the real world have demonstrated the effectiveness and real-time performance of the proposed method.

Extended state observer-based continuous finite time control for a fixed-wing vertical take-off and landing unmanned aerial vehicle

Dual system Vertical Take-Off and Landing (VTOL) fixed-wing Unmanned Aerial Vehicles (UAVs) combine the advantages of both the rotorcraft and fixed-wing UAV, characterizing horizontal and vertical flight capabilities. However, they face challenges such as aerodynamic coupling between propulsion systems, model uncertainty, and external disturbances, significantly deteriorating the tracking accuracy and robustness. This paper proposes an Extended State Observer based Continuous Finite-Time Control (ESO-CFTC) scheme to solve the attitude and trajectory tracking problem of such a VTOL fixed-wing UAV. Initially, the Nonsingular Fast Terminal Sliding Mode (NFTSM) controller guarantees the tracking errors reach a prescribed manifold in finite time with a continuous reaching law, then converge to the origin in finite time while avoiding singularities. To suppress the inherent chattering problem of the sliding mode control, an ESO technique is incorporated into the ESO-CFTC scheme, which is completely decoupled with the basic NFTSM controller. The supremum of the estimation residual set is derivated theoretically. The scheme forces the tracking errors to converge into the neighborhood of the origin in finite time without requiring prior knowledge of lumped disturbance supremum values. Finally, the numerical simulation results in a Matlab/Simulink environment demonstrate that the presented method outperforms existing benchmark in terms of disturbance estimation, tracking accuracy, convergence rate, and robustness.

Image fusion algorithm for the collaborative use of unmanned aerial vehicles

Currently, the applicable scope of unmanned aircraft application is increasingly expanding. The promising field of unmanned aircraft enhancement is the implementation of some collaborative actions during controlled flight. This paper considers some issues of the group application of unmanned aerial vehicles (UAV) related to the coordinated planning and control of UAVs performing surveillance missions. Performing aerial search operations is technically complicated by the requirement to recognize a search object in arbitrary conditions, which can be both simple and severe environment. The search area is limited by the UAV capabilities, so, in order to improve the efficiency of search operations, UAVs are combined into groups. An algorithm for solving the problem of object search in arbitrary conditions by a group of unmanned aircraft is proposed. The advantage of search by a group of unmanned aircraft is the coverage of the larger search area in a conventional unit of time. This paper addresses the unmanned aircraft configuration, containing both the means of collaborative flight operation and a synthetic vision system. The image obtained by the synthetic vision system is both a source of navigation information and a means which reliably determines the result of search operations. Depending on the conditions of search operations, the image obtained by the synthetic vision system may require additional processing to use as intended. A fusion algorithm is proposed, which is characterized by adaptive adjustment of parameters in each frame individually for different image fragments. Based on the results obtained, it is planned to create a new product for commercial operation of unmanned aircraft.

Spring-Damped Underactuated Swashplateless Rotor on a Bicopter Unmanned Aerial Vehicle

The stabilisation capabilities of unmanned aerial vehicles (UAVs) with bicopter underactuated swashplateless rotors are highly sensitive to motor-induced vibration. Due to the requirement of the active control of underactuated swashplateless rotors, conventional designs are limited in reducing vibration through control optimisation. A solution with customized passive spring-damping structures on a unique underactuated swashplateless rotor of a tiltrotor bicopter platform is presented. The implementation of this structure effectively reduces the self-coherent vibration in flights. As a result, a higher level of control authority has been achieved without setting excessive low-pass filtering for vibration. Experimentally obtained inertial measurement unit (IMU) data, rotor speed, rotor tilt angle, and the cyclic stator response are presented for comparison with Simulink model predictions.

Enhancing two-stage object detection models via data-driven anchor box optimization in UAV-based maritime SAR

The high-altitude imaging capabilities of Unmanned Aerial Vehicles (UAVs) offer an effective solution for maritime Search and Rescue (SAR) operations. In such missions, the accurate identification of boats, personnel, and objects within images is crucial. While object detection models trained on general image datasets can be directly applied to these tasks, their effectiveness is limited due to the unique challenges posed by the specific characteristics of maritime SAR scenarios. Addressing this challenge, our study leverages the large-scale benchmark dataset SeaDronesSee, specific to UAV-based maritime SAR, to analyze and explore the unique attributes of image data in this scenario. We identify the need for optimization in detecting specific categories of difficult-to-detect objects within this context. Building on this, an anchor box optimization strategy is proposed based on clustering analysis, aimed at enhancing the performance of the renowned two-stage object detection models in this specialized task. Experiments were conducted to validate the proposed anchor box optimization method and to explore the underlying reasons for its effectiveness. The experimental results show our optimization method achieved a 45.8% and a 10% increase in average precision over the default anchor box configurations of torchvision and the SeaDronesSee official sample code configuration respectively. This enhancement was particularly evident in the model’s significantly improved ability to detect swimmers, floaters, and life jackets on boats within the SeaDronesSee dataset’s SAR scenarios. The methods and findings of this study are anticipated to provide the UAV-based maritime SAR research community with valuable insights into data characteristics and model optimization, offering a meaningful reference for future research.

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data.

Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem.

A power prediction approach for a solar-powered aerial vehicle enhanced by stacked machine learning technique

This study aims to enhance the solar energy harvesting capabilities of Unmanned Aerial Vehicles (UAVs), with a focus on integrating solar power to improve overall energy harvesting systems. The proposed method combines two independent renewable systems to extract electricity from the environment. UAV wings equipped with solar panels capture solar energy, employing optimal power point tracking for increased efficiency. Simulation results utilize an ensemble machine learning algorithm, incorporating environmental variables and UAV data to predict solar power output. A comparative analysis involving various machine learning algorithms provides additional insights gleaned from the UAV dataset.

Detection of Potential Faults in the Electricity Distribution Network Using Unmanned Aerial Vehicles and Thermal Cameras Through Deep Learning Methods

In this article, a software-based unmanned aerial vehicle (UAV) prototype has been developed for exploration purposes, replacing the current manual inspection of energy transmission lines (ETLs) by human teams. The UAV's capabilities address the limitations of human viewing angles, ensuring more efficient exploration activities. Real-time exploration is now achieved through data collected with UAVs and processed using Convolutional Neural Networks (CNNs) and image processing techniques. The UAV’s path and detected objects are autonomously processed in the Cartesian coordinate system. The autonomous conductor tracking system provides bidirectional tracking of conductors in the distribution network through color space range and Faster R-CNN conductor detection. Incorporating a proportional-integral-derivative (PID) design, the UAV is capable of operating in adverse weather conditions. An additional overheating warning system protects equipment in the electricity distribution network (EDN) from potential issues related to uneven load distribution, economic life, and labor errors. Through modeling studies with convolutional neural networks and field data, impressive F-1 scores have been achieved for detecting various anomalies: 77% for trees in contact with the line, 67% for concrete equipment exceeding its economic life, 88% for bird nests, 55% for cracked porcelain insulators, 89% for foreign objects, and 97% for nonuse of original fuses.

The application of artificial intelligence in aerospace engineering

In recent years, there has been considerable interest in applying Artificial Intelligence (AI) in the field of aerospace engineering. However, the existing literature on this topic is not sufficiently comprehensive. This paper is purposed to solve this problem by providing a thorough analysis and overview of the current state of AI in aerospace engineering. The paper is divided into four sections. Firstly, the use of AI in autonomous navigation and flight control is explored, focusing on advanced algorithms and sensor technologies that enable highly autonomous and efficient aircraft navigation. Secondly, the application of AI in image recognition and computer vision is discussed, highlighting its significance in remote sensing and aerospace component quality inspection. The third section examines the integration of AI in unmanned aerial vehicles (UAV), covering the control system and the utilization of machine learning techniques for improved UAV capabilities. Lastly, the paper explores the impact of AI on data analysis and prediction in the aerospace industry, encompassing weather forecasting, resource allocation, and decision-making processes. Finally, this paper gives a general overview of the nowadays application of AI in aerospace engineering.

Reconfigurable UAV-aided 3D sustainable surveillance in classified air-spaces

The recent advancement of smart UAVs (Unmanned Aerial Vehicles) allows its applicability to wide-range fields covering time-critical reports, emergent item delivery, virtual emotion services and so on. In particular, the surveillance can be supported by a fleet of smart UAVs in cooperation with various system components including sensors, mobile robots, autonomous vehicles and so on. Additionally, it is crucial to consider the reconfigurable capabilities of smart UAVs leveraging for rapid movement in disregarded airspace. In this paper, we introduce a reconfigurable UAV-enabled 3D sustainable surveillance in classified air-spaces to provide energy-efficient monitoring and security surveillance enforcement. We formally define a problem to maximize the reconfigurable UAV-aided sustainable surveillance efficiency with optimal height in 3D classified air-spaces avoiding excessive resource consumption and waste. To solve this issue, two different schemes of Lined-Up and Zig-Zag are proposed. Furthermore, we analyze their performances based on the gained numerical results through expanded simulations with detailed discussions.

Rule-Based Verification of Autonomous Unmanned Aerial Vehicles

Automation enhances the capabilities of unmanned aerial vehicles (UAVs) by enabling self-determined behavior, while reducing the need for extensive human involvement. Future concepts envision a single human operator commanding multiple autonomous UAVs with minimal supervision. Despite advances in automation, there remains a demand for a “human in command” to assume overall responsibility, driven by concerns about UAV safety and regulatory compliance. In response to these challenges, a method for runtime verification of UAVs using a knowledge-based system is introduced. This method empowers human operators to identify unsafe behavior without assuming full control of the UAV. Aspects of automated formalization, updating and processing of knowledge elements at runtime, coupled with an automatic reasoning process, are considered. The result is an ontology-based approach for runtime verification, addressing the growing complexity of UAVs and the need to ensure safety in the context of evolving aviation regulations.

Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments.

Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning. However, due to limited satellite coverage or communication disruptions, UAVs may lose signals for positioning. In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs. However, most of the existing datasets are developed for the geo-localization task of the objects captured by UAVs, rather than UAV self-positioning. Furthermore, the existing UAV datasets apply discrete sampling to synthetic data, such as Google Maps, neglecting the crucial aspects of dense sampling and the uncertainties commonly experienced in practical scenarios. To address these issues, this paper presents a new dataset, DenseUAV, that is the first publicly available dataset tailored for the UAV self-positioning task. DenseUAV adopts dense sampling on UAV images obtained in low-altitude urban areas. In total, over 27K UAV- and satellite-view images of 14 university campuses are collected and annotated. In terms of methodology, we first verify the superiority of Transformers over CNNs for the proposed task. Then we incorporate metric learning into representation learning to enhance the model's discriminative capacity and to reduce the modality discrepancy. Besides, to facilitate joint learning from both the satellite and UAV views, we introduce a mutually supervised learning approach. Last, we enhance the Recall@K metric and introduce a new measurement, SDM@K, to evaluate both the retrieval and localization performance for the proposed task. As a result, the proposed baseline method achieves a remarkable Recall@1 score of 83.01% and an SDM@1 score of 86.50% on DenseUAV. The dataset and code have been made publicly available on https://github.com/Dmmm1997/DenseUAV.

ТАКСОНОМІЧНИЙ МЕТОД ВИБОРУ СПОСОБУ ВЕКТОРИЗАЦІЇ ПОТОЧНОГО ЗОБРАЖЕННЯ ДЛЯ ОПТИКО-ЕЛЕКТРОННОГО МОДУЛЯ БЕЗПІЛОТНОГО ЛІТАЛЬНОГО АПАРАТА

In the article, a taxonomic method for selecting the optimal vectorization approach for processing current images in the optical-electronic module of unmanned aerial vehicles (UAVs) was developed. The primary goal of this method is to optimize UAV navigation systems, which is particularly relevant due to the need to improve data processing efficiency in conditions of limited access to satellite navigation systems. This is critically important for ensuring high autonomy and accuracy of UAVs during the performance of various tasks. The need for such a method arose from the specific challenges faced by UAVs when operating in complex and unpredictable conditions. Analysis has shown that the use of vector images can significantly reduce the time required for information processing. This is possible due to the smaller volume of data that needs to be processed compared to traditional raster images. Reducing information processing time is critically important for the operational capability of UAVs to respond to dynamic changes in the operational environment, especially in critical situations. The taxonomic approach applied in the study allows for systematic classification and evaluation of different vectorization methods in terms of their processing speed, accuracy of results, and data retention efficiency. This enables the identification of the most suitable method for the specific operational conditions in which the UAV is used, thereby optimizing the overall system performance. Furthermore, the results of this research are significant not only in the scientific field but also in practical applications. By improving autonomous navigation systems for UAVs, the study directly contributes to enhancing their effectiveness in complex and unpredictable conditions. This is particularly relevant for missions that require a high level of precision and reliability, such as those performed by military UAVs and emergency response. Enhanced navigation capabilities allow these UAVs to perform complex maneuvers and make real-time decisions based on processed vectorized images, thereby reducing the risk of errors and increasing mission success rates. An improved vectorization method could lead to significant enhancements in UAV navigation systems, making them more resistant to failures and capable of operating in less structured environments without heavy reliance on satellite signals. This enhancement is vitally important for both military and civilian UAV applications, where operational flexibility and reliability are extremely important.

Automated Flight Technology for Integral Path Planning and Trajectory Tracking of the UAV

In view of the problem that path planning and trajectory tracking are rarely solved simultaneously in the current research, which hinders their practical implementation, this paper focuses on enhancing the autonomous flight planning capability of unmanned aerial vehicles (UAVs) by investigating integrated path planning and trajectory tracking technologies. The autonomous flight process is divided into two sub-problems: waypoint designing/optimizing and waypoint tracking. Firstly, an improved DB-RRT* algorithm is proposed for waypoint planning to make the algorithm have higher planning efficiency, better optimization results, and overcome the defects of accidental and low reliability of single RRT* planning results. Secondly, the scheme of “offline design + online flight” is adopted to lead the UAV to fly online according to the waypoints’ instructions by using the sliding mode guidance based on angle constraint with finite-time convergence so that it can fly to the destination autonomously. In order to check the performance of the proposed algorithm, a variety of simulations are conducted to verify the feasibility of the proposed algorithm.

Інтеграція БпЛА зі нейронною мережею у завданнях із виявлення наземних мін та мінних полів

One of the newest stages in the improvement of unmanned aerial vehicles (UAVs) is the integration of such systems with the neural networks, which, in turn, is not a novelty, but provides such systems with a further level of practical application. Having conducted a meta-analysis of the results of previous studies and available information on this topic, it was found that in the modern period, in addition to successful practical implementations of the integration of artificial intelligence with UAVs, there is already a certain classification of such processes according to the principles of optimal improvement of UAV capabilities and by areas of society. In addition to the publicly available and well-known information about the successful use of drones in the military and logistics sectors of human activity, UAVs successfully perform tasks in such sectors as agriculture, engineering, search, etc. The main purpose of the article is to analyze, review, study and systematize existing information on the positive effectiveness and feasibility of using the principles, approaches and integration of unmanned aerial vehicles with machine learning technologies to improve the efficiency of solving the problems of locating and detecting landmines and minefields, which is a major humanitarian problem for civil society located in the territory where military conflicts are currently taking place or in the territories where military clashes or conflicts have occurred in the past. In this article, a small study was conducted to develop a prototype neural network that can be further integrated with UAVs for landmine and minefield detection tasks. The described neural network was trained on an open dataset, trained using the algorithms chosen in the study, and has a fairly good final result in terms of detection accuracy, which is 1.5% higher than the accuracy of publicly available neural networks in a review of similar developments or studies.

Towards complex scenes: A deep learning-based camouflaged people detection method for snapshot multispectral images

Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO (MS-YOLO), which utilizes the SPD-Conv and SimAM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset (MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.