Capabilities Of Unmanned Aerial Vehicles Research Articles

АНАЛІЗ МОЖЛИВОСТЕЙ БЕЗПІЛОТНИХ ЛЕТАЛЬТНИХ АПАРАТІВПРОВІДНИХ КРАЇН СВІТУ

This work analyzes the capabilities of unmanned aerial vehicles (UAVs) of the leading countries of the world. It was noted that with the appearance of medium and small unmanned lethal vehicles, the tasks of countering them became significantly more relevant. Starting from the mid-2000s, reports began to circulate in the mass media about the danger of using small unmanned aerial vehicles in airport areas, and from the mid-2010s they began to be used to conduct unauthorized surveillance of important military objects, to conduct terrorist attacks. acts and diversions, for the transportation of prohibited goods (weapons, drugs and various contraband). In this work, it is noted that in the leading countries of the world, active scientific development of this direction of research has begun. At the same time, this problem is relatively new, because one of the first works on the use of unmanned aerial vehicles dates back to 2008. And mass scientific publications on their use refer to the years 20616-17. In the modern scientific literature of the leading countries of the world, the main directions of research in these scientific directions of research were introduced. Special terms were developed and implemented in scientific publications. At the initial stage of using UAVs (in the early 2000s), the task of analyzing their capabilities and countering them appeared. The task of countering UAVs was solved exclusively by means of defeating anti-aircraft missile systems of air defense (air defense air defense systems). Currently, military scientists have found out that the direct neutralization of a massive UAV raid by means of anti-aircraft defense systems is economically impractical, due to the unreasonably high cost of anti-aircraft defense means. This leads to the rapid consumption of the combat resource of the air defense system. In this regard, new means of countering UAVs are being widely researched, namely: the use of electronic warfare (ERW), the use of directed energy radiation — laser weapons, as well as the combined use of EW and air defense systems.

Energy-Efficient Task Offloading of Edge-Aided Maritime UAV Systems

This paper considers the autonomous detecting and tracking task of unmanned aerial vehicles (UAVs) in the maritime environment. Due to the high computational complexity of the image-processing task, the strict latency requirement of the tracking task, and the shortage of batteries and computational capability of UAVs, the UAVs can offload the computing-intensive task to the edge computing server (ECS) to reduce the task latency and energy consumption. Even though, the large latency and energy consumption brought by some high-definition image-processing task still make some visual tracking difficult to perform. We first investigate the impact of video frame resolution on computing task size and detection accuracy, and further on task latency and energy consumption. In addition, we optimize the transmission power, the local CPU frequency, the offloading rate of UAVs, and the bandwidth allocation between UAVs. We model the UAVs tracking system as an energy consumption optimization problem with the constraint of task latency. Although the proposed optimization problem is complex, we transform the optimization problem into several subproblems with decoupling and rigorous mathematical proof. By combining the convex optimization with the genetic algorithm, an offloading algorithm with low complexity and good performance is obtained, which is verified by comparing with some benchmark offloading strategies in the numerical results.

A Novel Strategic Caching and Availability Optimization for Wireless Unmanned Aerial Vehicle Communication Networks

The rowing complexity in the 5G technology has created a necessity for UAV(Unmanned Aerial Vehicle)-assisted cellular networks as base stations. This is helpful for a wider coverage with higher transmission rates as it addresses three critical issues, i.e. location, performance and bandwidth. Besides, the integration of caching into conventional UAV infrastructure has received significant attentions since it can bring contents and memory storage closer to a mobile device. In a dynamic resource caching for wireless mobile networks, drone’s settings contain important options for supporting a wide variety of applications and services, including the network access fee, quality of service (QoS) , number of cached contents, cache access fee and beaconing duration. A theoretic model based on game theory is developed to study the effect of competition among UAVs that have caching and sharing revenue model. Note that an optimal usage of UAV capabilities would thus lead to a cost-effective strategy for energy consumption and QoS requirements.

İNSANSIZ HAVA ARAÇLARI İÇİN U-NET TABANLI ARAÇ TESPİT YÖNTEMİ

With the developments in computer hardware technology, studies in the fields of computer vision and artificial intelligence has accelerated. However, the number of areas where autonomous systems are used has also increased. Among these areas are unmanned aerial vehicles, which are one of the most important parameters of today's military technology. In this study, which includes two different scenarios, we aimed to improve the vision capabilities of unmanned aerial vehicles based on artificial intelligence. Within the scope of Scenario-1, the U-Net model suitable for binary semantic segmentation method was trained with the help of images taken by unmanned aerial vehicle camera. Within the scope of Scenario-2, which is designed for moving or stationary vehicle detection, the U-Net model is trained in accordance with multi-class semantic segmentation method. In all these training processes, a publicly available dataset was used. The model trained for Scenario-1 reached mean Intersection over Union (mIoU) value of 84.3%, while the model trained for Scenario-2 reached 79.7% mIoU. In this study, approaches were shared about the use of high-resolution images in model training and testing stages. Applying such studies in the field can help improve precision and reliability in arms industry.

Joint User Scheduling, Power Configuration and Trajectory Planning Strategy for UAV-Aided WSNs

The future sixth-generation (6G) communication system is promising to provide differentiated communication services for massive users worldwide. To achieve this goal, wireless sensor networks (WSNs), regarded as the critical component of the 6G system, will be widely deployed in large-scale non-urban areas such as mountain villages, forests, and oceans. To ensure the high efficiency and stability of WSNs without the support of ground facilities, unmanned aerial vehicles (UAVs) are dispatched to provide assistance for WSNs. Nevertheless, considering the limitations of both the mobility and communication capability of UAVs, the diversified communication demands pose huge challenges for UAV-aided WSNs in large-scale application scenarios, such as how to schedule the SNs served in each time slot, how to optimize their working parameters, and how to plan the UAV trajectory. Therefore, a joint user scheduling, power configuration, and trajectory planning strategy is proposed in this article, which is formulated as an optimization problem suffering from non-convexity and complication. The successive optimization scheme is adopted here to efficiently solve this problem, which divides the original problem into three sub-problems and solves them iteratively. The simulation results prove the effectiveness and efficiency of the proposed strategy.

A Probabilistic–Geometric Approach for UAV Detection and Avoidance Systems

This paper proposes a collision avoidance algorithm for the detection and avoidance capabilities of Unmanned Aerial Vehicles (UAVs). The proposed algorithm aims to ensure minimum separation between UAVs and geofencing with multiple no-fly zones, considering the sensor uncertainties. The main idea is to compute the collision probability and to initiate collision avoidance manoeuvres determined by the differential geometry concept. The proposed algorithm is validated by both theoretical and numerical analysis. The results indicate that the proposed algorithm ensures minimum separation, efficiency, and scalability compared with other benchmark algorithms.

Sınır Güvenliği ve Müdahale Görevi Yapan İHA’ların ÇKKV Yöntemleri ile Değerlendirilmesi

İnsansız Hava Aracı (İHA), kendisini kullanan bir insan taşımayan, kaldırma kuvvetini oluşturabilmek amacıyla aerodinamik kuvvetleri kullanabilen, kendi kendine uçabilen veya uzaktan yönetilebilen, tekrar kullanılabilen veya kullanılamayan ve ölümcül veya ölümcül olmayan faydalı yükler taşıma kabiliyetine sahip motorlu hava aracıdır. Teknolojinin yükselen bir hızla gelişmesiyle beraber İHA’ların kabiliyetleri de aynı oranda artmış ve birçok askeri görevde kullanılmaya başlanmıştır. Fakat, askeri başarı elde edebilmek yalnızca bu modern gelişmiş hava araçlarına sahip olmak yeterli değildir. Görevlerin ifa oranlarını arttırmak ve alan hakimiyetini yakalayarak savaşı kazanmak için görev tipine uygun sistemlerin kullanım planlamasının yapılması şarttır. Bu tür planlamalarla görevlerdeki başarı oranı artacak ve kullanan taraf üstünlük elde edecektir. Bu çalışmada gelişmiş orduların envanterinde bulunan farklı türlerdeki 9 İHA’nın sınır güvenliği ve müdahale görevlerini yerine getirmesi için çok kriterli karar verme (ÇKKV) yöntemleri ile değerlendirilmesi yapılmıştır. Bu 9 İHA büyüklük, tür, kabiliyet vb. yönlerden birbirinden ayrılmaktadır. Bu çalışmanın amacı, belirlenen görev için uygun İHA’nın sıralamasının tespit edilmesidir. Çalışmada kullanılan kriterler İHA’lar konusunda uzman kişilerle değerlendirilip 8 ana başlıkta gruplandırılmıştır. Kriterlerin ağırlıklandırılması için ikili kıyaslamalar yapılarak, kriter ağırlıkları AHP yöntemiyle ortaya konmuştur. İHA’ların sıralamaları ve seçimini yapabilmek için literatüre son yıllarda kazandırılmış olan ÇKKV yöntemlerinden ARAS, EDAS ve WASPAS metotları ile literatüre daha eski yıllarda kazandırılmış sıkça kullanılan MAUT, TOPSIS ve VIKOR yöntemleri kullanılmıştır. Böylece hem eski yöntemlerin hem de yeni yöntemlerin kullanımı ile farklı bir bakış açısına sahip çözüm geliştirilmeye çalışılmıştır. Uygulanan yöntemlerden bulunan önceliklendirme sonuçları analiz edilmiş ve sonuçlar normalize edilip ortalamaları bulunarak her İHA’ya ait nihai bütünleşik önceliklendirme puanı (NBÖP) elde edilmiştir. Böylece önceliklendirme değerlerinin güvenilirlik, doğruluk ve tutarlılık düzeylerinin arttırılması amaçlanmıştır. Uygulanan yöntemler sonucu bulunan sonuçlar yorumlanmıştır.

SEIS: A spatiotemporal-aware event investigation framework for social airborne sensing in disaster recovery applications

Social airborne sensing (SAS) is taking shape as a new integrated sensing paradigm that melds the human wisdom derived from social data platforms (e.g., Twitter, Facebook) with the empirical sensing capabilities of unmanned aerial vehicles (UAVs) for providing multifaceted information acquisition and situation awareness services in disaster recovery applications. A crucial task in the aftermath of a disaster is to determine the veracity of the reported events alongside assessing their underlying urgency that can possibly facilitate appropriate parties in their disaster mitigation and recovery efforts. For example, identifying a falsely reported event early on thats claims fatalities could help divert alleviation efforts to genuinely critical events. However, existing SAS schemes are limited to deducing only the veracity of the reports on social data platforms and are unable to infer the underlying urgency of the events. In this paper, we explore the opportunity to develop a spatiotemporal-aware event investigation framework for SAS that can jointly determine the veracity of reported events as well as infer their underlying urgency and deadlines. However, constructing such an integrated system introduces a few new technical challenges. The first challenge is handling the predominant data sparsity in the incoming social signals. The second challenge is optimizing the UAV deployment and event veracity estimation processes by scrutinizing the highly dynamic and latent correlations among event characteristics. The third challenge is carefully extracting and analyzing latent semantic features embedded in the social media data to infer the event urgency. To address the above challenges, we introduce the Spatiotemporal-aware Event Investigation for SAS (SEIS) framework that harnesses techniques from natural language processing (NLP), deep learning, and spatial–temporal correlation modeling for deducing the veracity, urgency and deadlines of the underlying events. Experiments through a real-world disaster recovery dataset demonstrate that SEIS achieves better event veracity estimation, event urgency inference, and deadline hit rate compared to state-of-the-art baselines.

Monocular-Vision-Based Precise Runway Detection Applied to State Estimation for Carrier-Based UAV Landing.

Improving the level of autonomy during the landing phase helps promote the full-envelope autonomous flight capability of unmanned aerial vehicles (UAVs). Aiming at the identification of potential landing sites, an end-to-end state estimation method for the autonomous landing of carrier-based UAVs based on monocular vision is proposed in this paper, which allows them to discover landing sites in flight by using equipped optical sensors and avoid a crash or damage during normal and emergency landings. This scheme aims to solve two problems: the requirement of accuracy for runway detection and the requirement of precision for UAV state estimation. First, we design a robust runway detection framework on the basis of YOLOv5 (you only look once, ver. 5) with four modules: a data augmentation layer, a feature extraction layer, a feature aggregation layer and a target prediction layer. Then, the corner prediction method based on geometric features is introduced into the prediction model of the detection framework, which enables the landing field prediction to more precisely fit the runway appearance. In simulation experiments, we developed datasets applied to carrier-based UAV landing simulations based on monocular vision. In addition, our method was implemented with help of the PyTorch deep learning tool, which supports the dynamic and efficient construction of a detection network. Results showed that the proposed method achieved a higher precision and better performance on state estimation during carrier-based UAV landings.

Energy-Aware Blockchain and Federated Learning-Supported Vehicular Networks

The aerial capabilities and flexibility in movement of Unmanned Aerial Vehicles (UAVs) has enabled them to adaptively provide both traditional and more contemporary services. In this article, we introduce a solution that integrates the capabilities of both UAVs and Unmanned Ground Vehicles (UGVs) to provide both intelligent connectivity and services to both aerial and ground connected devices. A cooperative solution is adopted that considers nodes’ power and movement constraints. The UAV and UGV cooperative process ensures continuous power availability to UAVs to support seamless and continuous service availability to end-devices. A Federated Learning (FL) approach is adopted at the edge to ensure accurate and up-to-date service provisioning in accordance with the surrounding environment and network constraints. Moreover, Blockchain technology is used to decentralize the provisioning and control aspects, and ensure authenticity and integrity. Extensive simulations are conducted to test the soundness and applicability of the proposed solution. Results show significant improvement in terms of connectivity, service availability, and UAV energy enhancements when compared to traditional mobile and vehicular communication techniques.

Modern possibilities of using unmanned aerial vehicles by Police authorities and units: Analysis of foreign and Ukrainian experience

The relevance of the research subject is conditioned upon the scientific originality and practical significance of using modern capabilities of unmanned aerial vehicles by police authorities and units. The relevance of the research issue is that the provisions of the current legislation defining the legal basis for using unmanned aerial vehicles by police authorities and units are mostly formally defined. Based on this, and considering that the law enforcement system has encountered new challenges which constantly require the introduction of the latest methods and means of countering crime, including using modern achievements of digital, technological, scientific and technical progress, the purpose of this research is to analyse the foreign experience of using unmanned aerial systems by law enforcement agencies, and based on this, to develop proposals for improving the current legislation in the part concerning using UAVs. The research methodology includes a combination of general scientific and special methods that allow for defining assumptions and drawing conclusions. The research examines the relevant issues of the day concerning using the technical capabilities of unmanned aerial systems in the course of performing the tasks assigned to police agencies and units. In particular, the author examines the international experience of some technologically advanced countries (the USA, Great Britain, Germany, France, China and Israel) in using modern capabilities of unmanned aerial vehicles by law enforcement agencies. Attention is devoted to the development of the aviation industry of Ukraine in terms of the design of Ukrainian unmanned aerial vehicles, and the prospects for their implementation in the activities of the National Police. The author outlines the main prospects for using unmanned aerial vehicles in the activities of police agencies and units. In particular, those related to the protection of public order, road safety, detection, suppression and counteraction to criminal and administrative offences, and protection and defence of human rights and freedoms, life and health. The scientific originality and practical significance of the research are that it highlights the current possibilities of using unmanned aerial vehicles by police authorities and units, outlines some issues of a working nature which require resolution, and, based on international experience, identifies the areas for improvement of the current legislation on using unmanned aerial vehicles by the National Police

Networks of UAVs of Low Complexity for Time-Critical Localization

Future networks of unmanned aerial vehicles (UAVs) will be tasked to carry out ever-increasing complex operations that are time-critical and that require accurate localization performance, such as tracking the position of a malicious user. Since there is the need to preserve low UAV complexity while tackling the challenging goals of missions in effective ways, one key aspect is the UAV intelligence. The UAV's intelligence includes the UAV's capability to process information and make decisions, e.g., to decide where to sense and whether to delegate some tasks to other network entities. This article overviews some existing signal processing techniques for distributed estimation and autonomous navigation of UAVs of low complexity. To this end, we show some of the needs of the UAVs for running efficient localization operations for time-limited missions, performed either as a team or individually. Furthermore, we focus on different network configurations, which possibly include assistance with edge computing. We also discuss open problems and future perspectives for these settings.

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network.

Recent developments in unmanned aerial vehicles (UAVs) have led to the introduction of a wide variety of innovative applications, especially in the Mobile Edge Computing (MEC) field. UAV swarms are suggested as a promising solution to cope with the issues that may arise when connecting Internet of Things (IoT) applications to a fog platform. We are interested in a crucial aspect of designing a swarm of UAVs in this work, which is the coordination of swarm agents in complicated and unknown environments. Centralized leader–follower formations are one of the most prevalent architectural designs in the literature. In the event of a failed leader, however, the entire mission is canceled. This paper proposes a framework to enable the use of UAVs under different MEC architectures, overcomes the drawbacks of centralized architectures, and improves their overall performance. The most significant contribution of this research is the combination of distributed formation control, online leader election, and collaborative obstacle avoidance. For the initial phase, the optimal path between departure and arrival points is generated, avoiding obstacles and agent collisions. Next, a quaternion-based sliding mode controller is designed for formation control and trajectory tracking. Moreover, in the event of a failed leader, the leader election phase allows agents to select the most qualified leader for the formation. Multiple possible scenarios simulating real-time applications are used to evaluate the framework. The obtained results demonstrate the capability of UAVs to adapt to different MEC architectures under different constraints. Lastly, a comparison is made with existing structures to demonstrate the effectiveness, safety, and durability of the designed framework.

3D trajectory planning based on the Rapidly-exploring Random Tree–Connect and artificial potential fields method for unmanned aerial vehicles

This research proposes a multifaceted approach of three-dimensional trajectory planning based on the combination of Rapidly-exploring Random Tree–Connect algorithm and artificial potential field method to improve the path search ability and dynamic obstacles avoidance capability of unmanned aerial vehicles. Firstly, an improved method of the target gravity is developed by controlling the sampling range to reduce invalid sampling and speed up the convergence speed of the algorithm so as to lessen the restriction of low efficiency and random sampling of the Rapidly-exploring Random Tree–Connect algorithm. Moreover, the regulation factor is introduced into the artificial potential field method to deal with the problem of target unreachable in the trajectory planning. Then the improved Rapidly-exploring Random Tree–Connect algorithm is implemented to plan the global path in a complex environment. This step is carried out via selecting the local target point on the global path found in the global plan, dividing the complex environment into simple environment and utilizing the artificial potential field method to achieve the effect of avoiding unknown dynamic obstacles in the simple environment. Finally, cubic B-spline is employed to smoothing of the planned trajectory. The simulation results demonstrate that the combination of two improved algorithms improves the path search ability and dynamic barrier avoidance capability of the unmanned aerial vehicles.

ОСОБЛИВОСТІ ВИЯВЛЕННЯ ОПЕРАТИВНО-ТАКТИЧНИХ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ РАДІОЛОКАТОРАМИ РАДІОТЕХНІЧНИХ ВІЙСЬК

The range of aircraft is expanding rapidly and it should currently include objects from hypersonic aircraft to small unmanned aerial vehicles. Accordingly, airspace control must be carried out on all types of aircraft. The article discusses the features of radar detection of very complex objects for active location - unmanned aerial vehicles of tactical and operative-tactical level. Their tactical and technical characteristics, design features and tactics of application allow such aircraft to perform combat missions sometimes without detection of the enemy's air defense system. The article discusses the factors and features that contribute to the untimely detection of objects such as unmanned aerial vehicles by radar stations of radio troops or lead to difficulties in their observation. Ways to improve radar surveillance of unmanned aerial vehicles are noted. The analysis of the experience of combat use of unmanned aerial vehicles in the area of the Joint Forces operation is carried out in the east of Ukraine in 2014–2022 the Syrian military conflict, the second Karabakh war of 2020 and the modern Russian-Ukrainian war. There are more important factors that contributed to the effective use of unmanned aerial vehicles. The generalized information of change of features of the military conflicts which were formed thanks to possibilities of unmanned aerial vehicles is resulted. It is shown that modern reconnaissance and combat capabilities of unmanned aerial vehicles are rapidly changing not only the tactics of using weapons and military equipment of the Armed Forces, but also changing the strategy of warfare. Much attention is paid to the analysis of the mass use of unmanned aerial vehicles of various types in the current Russian-Ukrainian military conflict. Keywords: unmanned aerial vehicle, low-visibility detection, passive interference, radio technical troops.

Air Pollution Monitoring in Remote Areas using Smart UAV based System

Real time monitoring of air pollution and measurement of pollutant gases is very challenging task and needs more numbers of sensors and lots of observation. In this paper, Unmanned Aerial Vehicle (UAV) based system equipped with different micro sensor, is introduced for monitoring of air quality. It will offer a new approach in environmental pollution assessment instead of ground based monitoring system. Air pollution concentration data is collected by different sensors present in UAV, are effectively monitored in personal computer or mobile devices. The main objective of this paper is to elaborate the performance capability of UAV for effective monitoring of air pollution and measure health hazard air pollutants with high sensitivities in a particular area where human cannot reach. As it is quite recent field, a fruitful effort has dedicated to develop an integrated sensing system and optimization of its crucial features as dimension, weight and energy autonomy. The effectiveness of the developed system is evaluated by performing some field experiments using a hardware prototype UAV model.

HISTORY, CURRENT STATE, AND PROSPECTS OF DEVELOPMENT OF UNMANNED AERIAL VEHICLES AS A TECHNICAL AND FORENSIC TOOL AND OBJECT OF FORENSIC RESEARCH

The relevance of the study is conditioned by the scientific originality and practical significance of using modern capabilities of unmanned aerial vehicles by law enforcement agencies. The purpose of the study is to investigate the history, current state, and prospects of development of the unmanned aerial vehicle as a technical and forensic tool and object of forensic science. The study is based on the dialectical method of cognition of social and legal phenomena and concepts. Along with it, such general scientific and special research methods as historical, comparative legal, system and structural, sociological, statistical, logical, and other modern approaches were used, allowing the study to formulate assumptions and draw conclusions. Based on the findings, an analysis of the history of the development of an unmanned aerial vehicle was carried out, where the author identifies five main stages, namely: the period of the late 19th - early 20th century, the period of the First World War and the post-war period, the period of the Second World War, the period of the Cold War and local military conflicts of that time, and the modern period, which was characterised by the involvement of modern unmanned aerial systems not only in the military sphere, but also in various spheres of human life, including criminal activities, and in the activities of law enforcement agencies to disclose, investigate, and prevent criminal offences. Based on the intended purpose, technical characteristics and parameters, the classification of unmanned aerial vehicles is given, depending on their types, size, weight, power plant, and control method. In this regard, attention is focused on unmanned aircraft systems related to multicopters, as the most promising unmanned aerial vehicles that can be implemented in the work of law enforcement agencies. Certain aspects and features of using an unmanned aerial vehicle as a technical and forensic tool and an object of forensic research are considered. In this regard, problematic issues related to definition of a unified legal terminology for systems and elements of an unmanned aviation complex. Priority areas of introduction of unmanned aerial vehicles in the activities of law enforcement agencies are highlighted. Considering international standards in the aviation sector, the main areas are proposed for improving Ukrainian legislation on registration, licensing, and certification of unmanned aerial vehicles, and the grounds and procedure for its use by relevant law enforcement agencies in the detection, investigation, and prevention of criminal offences. The practical significance of the study is conditioned by the fact that it examines topical issues of the history of development, the current state, and prospects for improving Ukrainian legislation on issues related to the practical use of an unmanned aerial vehicle as a technical and forensic tool and the object of forensic research

Detection of Object (Weapons) With Deep Learning Algorithms From İmages Obtained By Unmanned Aerial Vehicles

Today, the rapid development of Artificial Intelligence technologies is effective in the success of deep learning algorithms in different application areas. In addition to these applications, it detects many objects that cannot be noticed even with the human eye in object detection in video and images with deep learning algorithms. In this study, it was aimed to detect weapons by using images obtained from UAV using deep learning algorithms. Regional Based Convolutional Neural Networks and Residual Network were used and the performance evaluation of the architecture of these algorithms was presented. Performance evaluation of algorithms was made using Loss plots, Precision-Recall, and mAP curves. In this study, 200 images of different angles and heights were obtained from the unmanned aerial vehicle. Two-thirds of the images we obtained were divided into training and one-third test images. Resnet and Region Based Convolutional neural networks used in test images have been successful in object detection. Regional based neural networks and Residual Network that we use for object detection are used. Images from different angles and heights obtained from the unmanned aerial vehicle are trained using regional-based neural networks and residual network algorithms. The feature maps extracted from the images used for training were compared with the test images. By bringing these compared images closer to the desired images, the desired image detection was detected at a rate of 99%. The performance evaluation of the detected images is discussed and the success of the deep learning algorithms used in object detection is presented. Considered in the performance evaluation of deep learning algorithms, Loss Charts (Error), Precision-Recall curves show the accuracy of detection in a short time. Algorithms that will increase the possibilities and capabilities of unmanned aerial vehicles used especially in border security and internal security are considered to increase in the future.

A Cascaded and Adaptive Visual Predictive Control Approach for Real-Time Dynamic Visual Servoing

In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation capability of UAVs is aggravated in GPS-deprived areas such as indoors. As a result, vision-based control and guidance methods are sought. In this paper, a vision-based target-tracking problem is formulated in the form of a cascaded adaptive nonlinear Model Predictive Control (MPC) strategy. The proposed algorithm takes the kinematics/dynamics of the system, as well as physical and image constraints into consideration. An Extended Kalman Filter (EKF) is designed to estimate uncertain and/or time-varying parameters of the model. The control space is first divided into low and high levels, and then, they are parameterised via orthonormal basis network functions, which makes the optimisation- based control scheme computationally less expensive, therefore suitable for real-time implementation. A 2-DoF model helicopter, with a coupled nonlinear pitch/yaw dynamics, equipped with a front-looking monocular camera, was utilised for hypothesis testing and evaluation via experiments. Simulated and experimental results show that the proposed method allows the model helicopter to servo toward the target efficiently in real-time while taking kinematic and dynamic constraints into account. The simulation and experimental results are in good agreement and promising.

System identification of cropped delta UAVs from flight test methods using particle Swarm-Optimisation-based estimation

AbstractIn the era of Unmanned Aerial Systems (UAS), an onboard autopilot occupies a prominent place and is inevitable for many of their modern applications. The efficacy of autopilot heavily relies upon the accuracy of the sensors employed and the capability of the onboard flight controller. In general, aerodynamic behaviour and flight dynamic capabilities of Unmanned Aerial Vehicles (UAVs) govern the selection and the design of flight controllers. Precise modeling of linear aerodynamic characteristics from flight data can be achieved using many of the existing classical parameter estimation techniques such as Output Error Method (OEM), Equation Error Method (EEM), and Filter Error Method (FEM). However, all the classical methods may not be readily applicable for aerodynamic modeling in nonlinear flight envelopes. The current manuscript is an attempt to exploit the capabilities of the Artificial Intelligence (AI) technique, named Particle Swarm Optimisation (PSO), in combination with Least Squares (LS) cost function to perform linear as well as nonlinear aerodynamic parameter estimation. The aforementioned task is accomplished by considering flight data from manoeuvers pertaining to linear angles of attack, moderate and near stall flight envelopes of two different UAVs with cropped delta planform geometry. Parameters estimated using the proposed LS-PSO method are consistent with minimum standard deviation and are on a par with OEM estimates. The proposed LS-PSO method enhances the capabilities of LS-based EEM while estimating stall characteristic parameters, which was not possible with LS alone. The longitudinal and lateral-directional static parameters estimated from the full-scale wind tunnel testing of the two UAVs were also used to corroborate the results obtained from the flight data using the LS-PSO method.