Management Of Unmanned Aerial Vehicles Research Articles

MAIN TRENDS IN THE USE OF UNMANNED AIRCRAFT IN THE RUSSIAN-UKRAINIAN WAR

One of the features of the russian-ukrainian war was the widespread and rapid use of unmanned aerial vehicles (UAVs). Today, the range of tasks solved with their help is constantly expanding. Mainly – during independent combat work with the possible use of them both in automated troop control systems and autonomously as means of reconnaissance, conducting radio-electronic warfare or for air strikes, as well as means of targeting for high-precision weapons systems, relaying in communication systems "link". The article reviews the development of technologies for the use of UAVs during the large-scale invasion of the armed forces of the russian federation on the territory of Ukraine. A brief description of some Ukrainian-made UAVs has been determined. The most important parameters that must be taken into account before flying a multi- rotor UAV and the methods of their calculation are specified. The further course of events on the battlefield is predicted, taking into account the development of both UAVs and means of radio-electronic warfare. It is suggested that when choosing a rational option for the use of UAVs by the Defense Forces, they should use suitability criteria and mathematical expectation of loss of the combat potential of the enemy's group of troops (forces), taking into account complex radio-electronic conditions. Further research should be directed to the solution of practical tasks of implementing group control in the management of UAVs, which leads to an increase in the efficiency of UAV use. The application of swarms of unmanned systems has extremely high potential for effective execution of reconnaissance and reconnaissance-strike tasks on the battlefield. An important issue in the practical application of UAV swarms is the creation of specialized software for management and control.

Improved unmanned aerial vehicle control for efficient obstacle detection and data protection

<p>The article centers on the research objectives and tasks associated with developing a swarm control system for unmanned aerial vehicles (UAVs) utilizing artificial intelligence (AI). A comprehensive literature review was undertaken to assess the effectiveness of the "swarm" method in UAV management and identify key challenges in this domain. Swarm algorithms were implemented in the MATLAB/Simulink environment for modeling and simulation purposes. The study successfully instantiated and simulated a UAV swarm control system adhering to fundamental principles and laws. Each UAV operates autonomously, following target-swarm principles inspired by the collective behavior of bees and ants. The collective movement and behavior of the swarm are controlled by an AI-based program. The system demonstrated effective obstacle detection and avoidance through computer simulations. Results obtained highlight key features contributing to success, including decentralized autonomy, collective intelligence, UAV coordination, scalability, and flexibility. The deployment of a local radio communication system in UAV swarm control and remote object monitoring is also discussed. The research findings hold practical significance as they enable the effective execution of complex tasks and have potential applications in various fields.</p>

Intersection Planning for Multilane Unmanned Aerial Vehicle Traffic Management

Unmanned aerial system (UAS) traffic management of airspace is a domain that demands strategic management of unmanned aerial vehicles (UAVs) for smooth and conflict-free movement in the uncharted low-altitude G airspace. In the context of the previously proposed CORRIDRONE structure, UAV traffic has to be organized in a shared volume of airspace connecting two or more corridors for a network of multilane corridors, resulting in the formation of aerial intersections. In this work, an intersection planning algorithm is proposed that aims to provide no-conflict paths to the UAVs inside the intersection volume. Paths are modeled as a function of the lanes involved in the transition, and conflict resolution is achieved by changing lanes. Optimized solutions are found among the conflicted UAV paths, such that only a few paths need modifying, optimizing the number of lane changes and time spent in the intersection. Simulation results, including random starting time intervals, various UAV sizes, corridor sizes, and differing numbers of lanes in intersecting corridors, are presented to demonstrate the concepts discussed in the paper.

APPLICATION OF THE KUBEFLOW TOOL FOR THE INTEGRATION OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN UNMANNED AERIAL VEHICLE

At the current stage of information technology development, machine learning (ML) and artificial intelligence (AI) are becoming one of the main tools for solving complex applied problems in various fields of activity. Various processes and technologies are used for develop, test, and maintain the infrastructure of the data system. The application of tools for the integration of ML and AI in the management of unmanned aerial vehicles (UAVs) is especially relevant today. An overview of the ML concept and processes (Machine Learning and Operation, MLOps) was made, which is a set of techniques for implementation and automatic continuous integration, as well as delivery to the product environment and model learning. The concept of MLOps is considered in terms of Kubeflow tools, which work on the Kubernetes platform. The possibilities of using modern MLOps solutions to improve the development processes of ML information systems have investigated. An AI-based information system with the possibility of continuous learning has designed. The concept of using the MLOps pipeline to solve the applied problem of classifying objects from the video of reconnaissance UAVs was presented. The results of the operation of the model in the Kubeflow arsenal have been checked using such improvement factors as: speed of development, implementation of changes, reduction of time to search for problems, recovery after global interruptions, reduction of the number of errors in the model. A publicly available model was deployed in a Kubeflow cluster using the Seldon Core Serving application manifest for practical analysis. The conducted research showed that Kubeflow consists of a set of various open source components that have a high level of integration with each other through the Kubernetes platform. At the same time, Kubeflow uses the Kubernetes pattern of operators for ML objects extremely efficiently. It has shown that writing model code is a small part of ML tasks, which affects the need for automation. The concept of a full-fledged information solution based on the continuous integration pipeline, which is the foundation of the implementation of the concept of continuous learning, has formed. Representing abstractions in the form of separate platform resources allows you to reduce the entry threshold for the end user.

The use of drones in emergency medicine

In the 21st century, we are experiencing the widespread use of new technologies that are designed to make work and daily life easier. The emergence of unmanned aerial vehicles (UAVs) has also opened up new opportunities for medical rescue support. This paper explores the various aspects of using medical drones to aid rescue operations. These devices can move at high speeds, traverse difficult terrain that ground vehicles cannot, and have many capabilities depending on their equipment. Currently, the use of UAVs is limited to rescue operations of an extreme nature, such as searching for missing individuals in dangerous or vast areas, or providing support during mass events.The paper describes the potential possibilities of using drones in rescue operations, such as delivering critical medicines, first aid equipment, or collecting information on hazards relevant to rescue planning. It is important to note that UAVs are still a relatively new technology that requires reliable safety systems, especially in the context of medical use. Possible threats, such as hacking attacks, collision risks in different environments, and the level of training of personnel involved in UAVs management, were also presented. Additionally, the paper addresses current legal and systemic issues in Poland and the European Union. Lastly, the results of public opinion polls on confi¬dentiality and consent to the use of medical drones were mentioned.

FFP: A Force Field Protocol for the tactical management of UAV conflicts

In recent years, we have seen a tremendous growth in the adoption of Unmanned Aerial Vehicles (UAVs). Nowadays, UAVs are used in many different industries such as agriculture, inspection (bridges, pipelines, etc.), parcel delivery, etc. In the near future, this will lead to a substantial increase of aircraft in our airspace, especially in urban areas. Many existing collision avoidance approaches rely on heavy and/or expensive sensors, which limits its use for real UAVs due to increased costs, weight and complexity. Hence, to address this problem, in this paper we present a solution for the tactical management (i.e. in-flight) of UAV conflicts outdoors that introduces minimal requirements: a wireless interface and a GPS module. Specifically, we provide a collision avoidance algorithm based on artificial potential fields to provide flight safety. Our solution, called Force Field Protocol (FFP), allows the UAVs to autonomously detect each other using wireless communications, and to maintain a safe distance between them without the intervention of any central service. Experiments performed in our multi-UAV simulator ArduSim show that, with our approach, collisions between two UAVs are completely avoided in a wide set of scenarios, while introducing low disturbances to the original flight plans. Specifically, in the scenarios that we tested, the additional flight time introduced will be only 7 s longer in the worst case; in addition, it is able to improve upon previous approaches by reducing flight time by up to 54 s. We have shown experimentally that our approach can be scaled easily up to 100 UAVs, and that the probability of a collision is very low (< 0.06) despite flying in a small area (2.5 km × 2.5 km).

UAV Target Detection under Complex Sky Background

&lt;p class="15"&gt;At present, unmanned aerial vehicles (UAVs) are widely used in various fields, and the management of UAVs is very important to solve the problems in the field of low-altitude safety. Due to the low flying height, small radar cross section, and inconspicuous characteristic signals of UAVs, the detection of UAVs based on video frames taken by fixed cameras cannot meet the existing requirements in terms of tracking speed and recognition accuracy. This paper proposes a multi-sensor fusion model. Firstly, the UAV target signal is improved by spatial filtering and improved Sobel operator edge detection algorithm, and then Gaussian filter is used to denoise, and finally the UAV small target is extracted based on the maximum inter-class variance method threshold segmentation algorithm. Experimental results show that this method can effectively enhance the UAV target signal in a complex environment, and the threshold segmentation method also has good adaptability, and can effectively screen UAVs under a complex sky background.&lt;/p&gt;

Multi-Agent Deep Reinforcement Learning for Task Offloading in UAV-Assisted Mobile Edge Computing

Mobile edge computing can effectively reduce service latency and improve service quality by offloading computation-intensive tasks to the edges of wireless networks. Due to the characteristic of flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed as assisted edge clouds (ECs) for large-scale sparely-distributed user equipment. Considering the limited computation and energy capacities of UAVs, a collaborative mobile edge computing system with multiple UAVs and multiple ECs is investigated in this paper. The task offloading issue is addressed to minimize the sum of execution delays and energy consumptions by jointly designing the trajectories, computation task allocation, and communication resource management of UAVs. Moreover, to solve the above non-convex optimization problem, a Markov decision process is formulated for the multi-UAV assisted mobile edge computing system. To obtain the joint strategy of trajectory design, task allocation, and power management, a cooperative multi-agent deep reinforcement learning framework is investigated. Considering the high-dimensional continuous action space, the twin delayed deep deterministic policy gradient algorithm is exploited. The evaluation results demonstrate that our multi-UAV multi-EC task offloading method can achieve better performance compared with the other optimization approaches.

Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks.

The rapid growth of mobile data traffic will lead to the deployment of Ultra–Dense Networks (UDN) in the near future. Various networks must overlap to meet the massive demands of mobile data traffic, causing an increase in the number of handover scenarios. This will subsequently affect the connectivity, stability, and reliability of communication between mobile and serving networks. The inclusion of Unmanned Aerial Vehicles (UAVs)—based networks will create more complex challenges due to different mobility characterizations. For example, UAVs move in three–dimensions (3D), with dominant of line–of–sight communication links and faster mobility speed scenarios. Assuring steady, stable, and reliable communication during UAVs mobility will be a major problem in future mobile networks. Therefore, this study provides an overview on mobility (handover) management for connected UAVs in future mobile networks, including 5G, 6G, and satellite networks. It provides a brief overview on the most recent solutions that have focused on addressing mobility management problems for UAVs. At the same time, this paper extracts, highlights, and discusses the mobility management difficulties and future research directions for UAVs and UAV mobility. This study serves as a part of the foundation for upcoming research related to mobility management for UAVs since it reviews the relevant knowledge, defines existing problems, and presents the latest research outcomes. It further clarifies handover management of UAVs and highlights the concerns that must be solved in future networks.

Kentsel Hava Sahasında İnsansız Hava Aracı Sistemleri Trafik Yönetimi için Verilmesi Gereken Hizmetler ve Kullanılabilecek Bazı Teknolojiler

İnsansız hava araçları, günümüzde eğlence ve hobi amacıyla tercih edilmesinin yanı sıra, tarımsal faaliyetlerde, güvenlik amaçlı gözetlemede, arama-kurtarma faaliyetlerinde ve daha birçok alanda etkin bir şekilde kullanılmaya başlanmıştır. Yaşanan gelişmeler başta ulaşım ve havacılık sektörü olmak üzere diğer birçok sektörün de ilgisini çekmiştir. Dronlar, uzaktan pilotlu hava aracı sistemleri (UPHAS), otonom hava araçları, henüz konsept teknolojiler olarak karşımıza çıkan sürücülü ve sürücüsüz kişisel hava araçları (KHA) gelecekte günlük yaşantımızın bir parçası olarak insanların yaşadığı yerleşim yerleri ve civarında ciddi bir kentsel hava hareketliliği (KHH) oluşturacaktır. Kentsel hava sahasında gerçekleşecek olan bu hareketliliğin, mevcut hava trafik yönetimi (HTY) ile uyumlu bir şekilde yönetilmesi büyük önem arz etmektedir. İnsansız hava aracı sistemleri trafik yönetiminin (İTY) ihtiyaçları mevcut hava trafik yönetiminden farklıdır. Ancak mevcut teknolojilerle beraber yeni teknolojiler kullanılarak insansız hava araçları trafik yönetimi için çözümler sunulabilir. Bu çalışmada, kentsel hava sahasını kullanacak insansız hava araçlarının emniyetli ve düzenli trafik yönetimi için verilmesi gereken hizmetler ve kullanılabilecek bazı teknolojiler önerilerek kentsel hava hareketliliği sistem altyapısı için temel bir model oluşturulmaya çalışılmıştır.

Deep-Reinforcement-Learning-Based Collision Avoidance in UAV Environment

Unmanned aerial vehicles (UAVs) have recently attracted both academia and industry representatives due to their utilization in tremendous emerging applications. Most UAV applications adopt visual line of sight (VLOS) due to ongoing regulations. There is a consensus between industry for extending UAVs&#x2019; commercial operations to cover the urban and populated area-controlled airspace beyond VLOS (BVLOS). There is ongoing regulation for enabling BVLOS UAV management. Regrettably, this comes with unavoidable challenges related to UAVs&#x2019; autonomy for detecting and avoiding static and mobile objects. An intelligent component should either be deployed onboard the UAV or at a multiaccess-edge computing (MEC) that can read the gathered data from different UAV&#x2019;s sensors, process them, and then make the right decision to detect and avoid the physical collision. The sensing data should be collected using various sensors but not limited to Lidar, depth camera, video, or ultrasonic. This article proposes probabilistic and deep-reinforcement-learning (DRL)-based algorithms for avoiding collisions while saving energy consumption. The proposed algorithms can be either run on top of the UAV or at the MEC according to the UAV capacity and the task overhead. We have designed and developed our algorithms to work for any environment without a need for any prior knowledge. The proposed solutions have been evaluated in a harsh environment that consists of many UAVs moving randomly in a small area without any correlation. The obtained results demonstrated the efficiency of these solutions for avoiding the collision while saving energy consumption in familiar and unfamiliar environments.

Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles

We present a three-dimensional (3D) intersection traffic management platform for small autonomous Unmanned Aerial Vehicles (UAVs), particularly quadcopters, in urban airspace. Assuming many autonomous UAVs are approaching a shared airspace, where UAVs have varying sources and destinations, we propose a system model for a 3D intersection that aims to provide safe and systematic management of UAVs. We also devised a scheduling scheme to ensure that the intersection is efficiently utilized and that there are no collisions among the UAVs in the intersection. The scheduling scheme applies the reservation-based approach, which is sensitive to the sequence of the UAVs in scheduling, thus genetic algorithm is used to determine the best sequence of the UAVs. Simulations were performed to evaluate the efficiency of the system. We also show through the simulations that our scheduling scheme reduces the UAVs’ average time in the system by 27 percent compared with when the UAVs are scheduled in a first-come, first-served manner for the highly crowded intersection.

Blockchain-Based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions

Recently, unmanned aerial vehicles (UAVs) have gained attention due to increased use-cases in healthcare, monitoring, surveillance, and logistics operations. UAVs mainly communicate with mobile base stations, ground stations (GS), or networked peer UAVs, known as UAV swarms. UAVs communicate with GS, or UAV swarms, over wireless channels to support mission-critical operations. Communication latency, bandwidth, and precision are of prime importance in such operations. With the rise of data-driven applications, fifth-generation (5G) networks would face bottlenecks to communicate at near-real-time, at low latency and improved coverage. Thus, researchers have shifted towards network designs that incorporate beyond 5G (B5G) networks for UAV designs. However, UAVs are resource-constrained, with limited power and battery, and thus centralized cloud-centric models are not suitable. Moreover, as exchanged data is through open channels, privacy and security issues exist. Federated learning (FL) allows data to be trained on local nodes, preserving privacy and improving network communication. However, sharing of local updates is required through a trusted consensus mechanism. Thus, blockchain (BC)-based FL schemes for UAVs allow trusted exchange of FL updates among UAV swarms and GS. To date, limited research has been carried out on the integration of BC and FL in UAV management. The proposed survey addresses the gap and presents a solution taxonomy of BC-based FL in UAVs for B5G networks due to the open problem. This paper presents a reference architecture and compares its potential benefits over traditional BC-based UAV networks. Open issues and challenges are discussed, with possible future directions. Finally, a logistics case study of BC-based FL-oriented UAVs in 6G networks is presented. The survey aims to aid researchers in developing potential UAV solutions with the key integrating principles over a diverse set of application verticals.

A low-altitude public air route network for UAV management constructed by global subdivision grids.

With an increasing number of unmanned aerial vehicles (UAVs), the difficulty of UAV management becomes more challenging, especially for low-altitude airspace due to complicated issues of security, privacy and flexibility. Existing management approaches to UAV flights include implementing registration of flight activity for supervision purposes, limiting the maximum flight height, setting different zones for different flight activities and prohibiting flights. In this research, we proposed a new air traffic management method for UAVs based on global subdivision theory. We designed four types of low-altitude air routes from grids, which correspond to grid sizes of 1.85 km, 128 m, 64 m and 32 m. Utilization of the subdivision grids transforms the complex spatial computation problem into a query process in the spatial database, which provides a new approach to UAV management in the fifth-generation (5G) era. We compared the number and data size of stored track records using longitude and latitude and different grid levels, computed time consumption for air route trafficability and simulated UAV flight to verify the feasibility of constructing this type of air traffic highway system. The amount of data storage and time consumption for air route trafficability can be substantially reduced by subdivision. For example, the data size using traditional expressions of latitude and longitude is approximately 1.5 times that of using a 21-level grid, and the time consumption by coordinates is approximately 1.5 times that of subdivision grids at level 21. The results of the simulated experiments indicate that in the 5G environment, gridded airspace can effectively improve the efficiency of UAV trajectory planning and reduce the size of information storage in the airspace environment. Therefore, given the increasing number of UAVs in the future, gridded highways have the potential to provide a foundation for various UAV applications.

The Rise of Drones in Internet of Things: A Survey on the Evolution, Prospects and Challenges of Unmanned Aerial Vehicles

The recent years have seen a rise in the incorporation and integration of new smart connected devices and platforms such as Unmanned Aerial Vehicles (UAVs) to the ubiquitous network of Internet of Things (IoT). UAVs not only offer new means of delivering value-added IoT services through a wide range of applications ranging from monitoring and surveillance to on-demand last-mile delivery and people transport, but they also promise a pragmatic solution to the limitations of fixed terrestrial IoT infrastructure. Owning to their potential, UAVs are expected to soon be an integral part of our cities, dominating the shared low-altitude airspace. This introduces new research challenges in privacy, security and most notably in the safe management of UAVs’ operation under high traffic demands. To this end, this work presents a holistic study on the current state-of-the-art of UAVs and low-altitude airspace traffic management. This work additionally explores the technical standardisation landscape and highlights synergies between scientific research and standardisation efforts towards enabling safe UAV operations, while taking into consideration additional IoT inherent challenges such as security, data protection and privacy.

Dual-Source Detection and Identification System Based on UAV Radio Frequency Signal

With the rapid development of the technology industry, low, slow, and small civilian Unmanned aerial vehicle (UAV) have been popularized, bringing fun and convenience to life and work. However, with the popularization of UAV, all kinds of illegal flying behaviors have appeared successively, posing a threat to people’s life and national security. To solve this problem, this article proposes a method and designs a system to detect and identify the UAV through the radio frequency (RF) signal emitted by the UAV itself. First, the RF front-end scans and intercepts the radio signals in the target airspace, then analyzes and identifies them by algorithms, and then implements the algorithm by field-programmable gate array (FPGA). Among them, algorithm recognition includes UAV flight control signal (FCS) detection and recognition, UAV image transmission signal (ITS) blind detection, and accurate recognition. Through the algorithm recognition result, it can be judged whether there are UAVs in the protected area. Finally, the hierarchical experimental verification method is used to demonstrate that the system can identify the number, type, and frequency band of UAVs in urban complex electromagnetic environment by using dual source detection and recognition algorithm, which provides a good foundation for UAV management and control.

Transmission power and altitude design for energy‐efficient mission completion of small‐size unmanned aerial vehicle

Owing to the rapid and facile deployment of unmanned aerial vehicles (UAVs), the third-generation partnership programme has recently shown considerable interest in UAVs for assisting current terrestrial base stations. Hence, the feasibility of using UAVs has been vigorously investigated for enhancing the performance of traditional ground-based communications, e.g. coverage and quality-of-service enhancement under natural disaster or overwhelming data requirement conditions. Here, however, the energy-efficient operation of the UAV is necessary to obtain the essential benefits. To strengthen the base of operation and management of UAVs in cellular systems, the authors consider in this Letter a single mission in which a small-size UAV transmits a given data volume to a cell boundary user. They design the transmit power and altitude of the UAV to complete the mission with high-energy efficiency. From the numerical results, it is verified that the UAV transmitting data with the designed transmit power at the designed altitude can complete the mission with near-optimal, i.e. maximum, energy efficiency.

A taxonomy of blockchain-enabled softwarization for secure UAV network

The recent advancements in unmanned aerial vehicles (UAVs) upsurges its usages in commercial and civilian applications such as surveillance, rescue, and crowdsensing. UAVs are vulnerable to being destroyed, lost, or stolen in case of security breaches of its network. The network management of UAVs is a crucial task due to its high mobility, which necessitates UAV network softwarization. Then, it becomes indispensable that allows the separation of control functions (i.e., control plane data) from hardware for smooth execution of complex operations. Further, UAV uses the Internet (an open channel) for communication in its complex system that raises a network security concern. The well-known softwarization techniques, i.e., software-defined networking (SDN) and network function virtualization (NFV) can be used to accomplish the secure network services on less expense. Conversely, these softwarization techniques may suffer from various threats like access control, user authentication, controller hijacking, and many more attack. The existing solutions are using a centralized system, which is having a single point of failure issue and vulnerable to security threats. Motivated from these facts, we present a comprehensive and systematic survey on the blockchain-based softwarization for a secure UAV network. Then, we propose a blockchain-enabled UAV softwarization architecture for secure communication and network management. It provides dynamic, flexible, and on-the-fly decision capabilities for communication services over the UAV network. Eventually, we analyzed the open research issues and challenges for future research directions in this emerging area.

Assessing the effect of spatial resolution on the delineation of management zones for smallholder farming in southern Brazil

Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delineate management zones for smallholder farming in southern Brazil. In this study, we investigated the spatial resolution influence on the AGB estimates of rye, calculated from regression relationships with the Normalized Difference Vegetation Index (NDVI), and on the subsequent delineation of management zones using the Management Zone Analyst (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were compared with observations from the PlanetScope (PS) satellite constellation (3 m) and the Operational Land Imager (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green vegetation, soil, and shadow) was applied to surface reflectance data of these instruments for evaluating the cover-crop development at two dates in August 2017. Because of the differences in the technical specifications of the sensors, we resampled the UAV dataset into four levels of spatial resolution (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, we obtained confusion matrices for the original and resampled data. The results showed that the increasing amounts of rye AGB from the beginning to the end of August promoted significant changes in surface reflectance and in soil-green vegetation fractions calculated at variable spatial resolution. The performance of the regression models to estimate cover-crop AGB was approximately similar in the transition from the sub-metric (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV camera to the PS data. For all datasets, the MZA detected two management zones with zone 2 having higher cover-crop AGB than zone 1. When compared to the UAV management zone map (reference), the PS map had a moderate-to-substantial agreement, while the OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial agreements with the reference map were observed at simulated 1 m and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 and overall accuracies of 86.40% and 81.40%, respectively. We conclude that the 3 m spatial resolution data of the PS comprise an alternative to delineate management zones for smallholder farming in southern Brazil when compared to the very-high spatial resolution observations of the UAV cameras.

Multiple hierarchy risk assessment with hybrid model for safety enhancing of unmanned subscale BWB demonstrator flight test

To explore the low-speed characteristics of the Blended-Wing-Body (BWB) configuration for future civil aircraft, a series of unmanned subscale demonstrators have been developed and tested by our research team. During this process, specific safety risks deriving from uncertain design features, system unreliability, and insufficient personnel experience caused continuous flight test mishaps and the risk mechanism was not clear. Local and trial-and-error learning driven safety improvements took few effects on mishap prevention, so our focus was turned to look for systematic safety strategies. This paper establishes a systems theory based hybrid model to integrate the physical system reliability analysis techniques with the system dynamics method for illustrating the multiple risk interactions of the demonstrator flight test involving organizational, human resource and technical system factors. Using the prior BB-5 demonstrator as a case, the hybrid model simulation represents its historical risk evolution process, which verifies the model rationality. Derived risk control strategies reduced the mishap rate of a new demonstrator called BB-6 Sprit. The paper also shows the extended hybrid model can be applied on safety management of unmanned aerial vehicles from the initial period of vehicle development.