Potential Of Unmanned Aerial Vehicles Research Articles

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. These quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Open-Pit Granite Mining Area Extraction Using UAV Aerial Images and the Novel GIPNet

The ability to rapidly and accurately delineate open-pit granite mining areas is pivotal for effective production planning and environmental impact assessment. Over the years, advancements in remote sensing techniques, including the utilization of satellite imagery, LiDAR technology and unmanned aerial vehicles, have revolutionized the way mining areas are monitored and managed. Simultaneously, in the context of the open-pit mining area extraction task, deep learning-based automatic recognition is gradually replacing manual visual interpretation. Leveraging the potential of unmanned aerial vehicles (UAVs) for real-time, low-risk remote sensing, this study employs UAV-derived orthophotos for mining area extraction. Central to the proposed approach is the novel Gather–Injection–Perception (GIP) module, designed to overcome the information loss typically associated with conventional feature pyramid modules during feature fusion. The GIP module effectively enriches semantic features, addressing a crucial information limitation in existing methodologies. Furthermore, the network introduces the Boundary Perception (BP) module, uniquely tailored to tackle the challenges of blurred boundaries and imprecise localization in mining areas. This module capitalizes on attention mechanisms to accentuate critical high-frequency boundary details in the feature map and synergistically utilizes both high- and low-dimensional feature map data for deep supervised learning. The suggested method demonstrates its superiority in a series of comparative experiments on a specially assembled dataset of research area images. The results are compelling, with the proposed approach achieving 90.67% precision, 92.00% recall, 91.33% F1-score, and 84.04% IoU. These figures not only underscore the effectiveness of suggested model in enhancing the extraction of open-pit granite mining areas but also provides a new idea for the subsequent application of UAV data in the mining scene.

An Efficient Rep-Style Gaussian–Wasserstein Network: Improved UAV Infrared Small Object Detection for Urban Road Surveillance and Safety

Owing to the significant application potential of unmanned aerial vehicles (UAVs) and infrared imaging technologies, researchers from different fields have conducted numerous experiments on aerial infrared image processing. To continuously detect small road objects 24 h/day, this study proposes an efficient Rep-style Gaussian–Wasserstein network (ERGW-net) for small road object detection in infrared aerial images. This method aims to resolve problems of small object size, low contrast, few object features, and occlusions. The ERGW-net adopts the advantages of ResNet, Inception net, and YOLOv8 networks to improve object detection efficiency and accuracy by improving the structure of the backbone, neck, and loss function. The ERGW-net was tested on a DroneVehicle dataset with a large sample size and the HIT-UAV dataset with a relatively small sample size. The results show that the detection accuracy of different road targets (e.g., pedestrians, cars, buses, and trucks) is greater than 80%, which is higher than the existing methods.

Assessing road roughness using UAV-derived dense point clouds

The quality and safety of road networks are of paramount importance in modern transportation infrastructure. Road surface conditions, particularly road roughness, significantly impact vehicular travel safety, user comfort, vehicle operating costs, and overall road infrastructure maintenance. Traditional methods for road roughness analysis, such as manual inspections or image annotation, often present limitations in terms of data completeness, efficiency, and cost-effectiveness, especially for extensive road networks. This study investigates the potential of Unmanned Aerial Vehicles (UAVs) equipped with Structure-from-Motion (SfM) derived point clouds to transform road roughness assessment. By leveraging the capabilities of UAVs, including rapid data acquisition and high-resolution imagery, and employing SfM to generate detailed point clouds, this research aims to provide a comprehensive analysis of road surface conditions. The study, conducted on a road segment within the Harran University Osmanbey campus, systematically examines road roughness at different kernel sizes: 30 cm (smaller), 50 cm (moderate), and 75 cm (larger). Through this investigation, insights are gained into how different scales of analysis influence roughness measurements. The findings highlight the potential of UAV-derived point clouds as a promising avenue for road roughness analysis, offering transportation authorities and road administrators an efficient and cost-effective means of maintaining and enhancing road networks. The integration of this technology could lead to the development of safer, more efficient, and economically sustainable road transportation systems, benefiting both road users and infrastructure managers. As research and technological advancements in UAV-based road roughness assessment continue to progress, the potential for revolutionizing road management practices becomes increasingly apparent, ultimately leading to improved road quality and enhanced travel experiences for road users.

Застосування БПЛА для медичної евакуації пацієнтів

The subject of the research in this article is advanced methods and means of emergency evacuation of the injured from the scene to a medical facility, requiring urgent assistance from professionals. In comparison with the traditional method of patient transport by helicopter, particular attention is given to unmanned aerial vehicles (UAVs), which boast an impressive list of advantages and are specially adapted for such functions. Their technical capabilities and effectiveness in transporting the injured to a medical facility are thoroughly examined, making them an attractive alternative in situations requiring urgent medical assistance. The current dynamic development of technology provides the opportunity for significant expansion of the use of drones in the field of medical aviation transportation, becoming the most relevant direction in medical monitoring. The research aims to justify the broader use of drones in the healthcare system, including monitoring diseases, rescue operations, patient transport, medical equipment, medications, and other equipment. The possibilities of providing quick and effective medical assistance in difficult-to-reach places or emergencies are discussed, where drones can ensure rapid and safe transportation. The objectives of this study include determining the role and advantages of drones in medical evacuation, the structural features of drones in terms of their application in aviation transport for medical purposes, and revealing the potential of unmanned aerial vehicles in ensuring safety and optimizing costs. The scientific and practical novelty of the obtained results lies in the comprehensive advantages of drones in the context of medical evacuation and their technical data and capabilities, with particular attention to the selection of optimal power plant options, which are crucial for providing the necessary power and flight range, especially in medical transport missions. This allows for improving the functionality of drones, enhancing safety, and cost savings in performing medical aviation transportation.

UAV-Assisted RSUs for V2X Connectivity Using Voronoi Diagrams in 6G+ Infrastructures

Sixth-generation networks and vehicular Ad hoc networks advancement brought us to the cusp of a new era. Autonomous Vehicles with a plethora of advanced applications require a substantially enhanced vehicle-to-everything communication network. The infrastructure should efficiently support hyper-fast, ultra-reliable, and low-latency massive data exchange. Roadside units were initially exploited as a promising communication solution to overcome this challenge. However, the challenging integration with the infrastructure led to the investigation of additional solutions. Unmanned aerial vehicles are one of the most dominant assistive solutions due to their inherent advantage of mobility. Moreover, air-to-air and ground-to-air networks are more efficient than ground-to-ground. Nevertheless, it is a prerequisite to leverage the potential of unmanned aerial vehicles to attain nationwide Vehicle-to-Everything connectivity. Therefore, we focused our research orientation on developing a strategy to optimize the network’s coverage within the intelligent transportation systems framework. In particular, we have deployed an innovative algorithmic technique that constructs Voronoi diagrams using circles. Besides, we applied the poison point process to determine the optimum locations of the transceivers’ establishment. Simulation results illustrate full network coverage for the tested area after the required iterations. Also, time complexity evaluation proved the simplicity of the proposed algorithm.

Evaluating the feasibility and potential of unmanned aerial vehicles to monitor implementation of forestry best management practices in the coastal plain of the southeastern United States

Compared to undisturbed areas, timber harvest activities, including road construction, skid trails, and stream crossings, exhibit the potential for causing erosion and sedimentation if forestry best management practices (BMPs) are not implemented. Forestry BMP monitoring serves as a tool to evaluate BMP implementation status. Conventional on-the-ground BMP monitoring has contributed substantially to assessing BMP implementation. However, conventional on-the-ground surveys can be time consuming. Unmanned aerial vehicles (UAVs) have been rapidly emerging as a new tool for local-scale monitoring. In this study, we evaluated the feasibility and potential of UAVs for monitoring forestry BMPs in the coastal plain of southeastern United States. We compared the performance of UAV-live feed surveys and UAV-created map surveys with conventional on-the-ground surveys across major BMP categories on 30 study sites using BMP guidelines and implementation survey questions from Alabama, Georgia, and Florida for the sites specific to that state (10 sites in each state). The UAV-live feed surveys involved real-time assessments conducted using the live feed function, enabling on-the-spot evaluations. Whereas the UAV-created map surveys entailed autonomous UAV flight to collect imagery specifically for generating maps and facilitating subsequent assessments.We found that using a UAV for monitoring BMPs efficiently provided an overview of a timber harvest area from above. UAV-live feed surveys were as effective as conventional on-the-ground surveys across all major BMP categories. Specifically, the Cramer's V correlation between the responses from UAV-live feed and conventional on-the-ground surveys for all BMP categories, was 0.95 (p < 0.0001) for detecting implemented BMPs and 0.86 (p < 0.0001) for detecting when BMPs were not implemented but needed to be. UAV-created map survey results vs. conventional on-the-ground surveys were not as effective (V of 0.79 for detecting implemented BMPs and 0.46 for detecting BMPs not implemented but needed). Waste and stream crossing BMP implementation were the most difficult to verify using the map surveys, which was not an issue with the live feed surveys due to the capability to evaluate those BMPs at different camera angles. While UAV-created map surveys may be less effective, they provide an observation record that includes a map of the site. This project also developed a standardized framework for using UAVs to monitor forestry BMPs, including instructions and procedures for conducting UAV-live feed and map surveys. In conclusion, a UAV using the live feed function in the field is a feasible option to monitor forestry BMPs.

Towards Enabling Haptic Communications over 6G: Issues and Challenges

This research paper provides a comprehensive overview of the challenges and potential solutions related to enabling haptic communication over the Tactile Internet in the context of 6G networks. The increasing demand for multimedia services and device proliferation has resulted in limited radio resources, posing challenges in their efficient allocation for Device-to-Device (D2D)-assisted haptic communications. Achieving ultra-low latency, security, and energy efficiency are crucial requirements for enabling haptic communication over TI. The paper explores various methodologies, technologies, and frameworks that can facilitate haptic communication, including backscatter communications (BsC), non-orthogonal multiple access (NOMA), and software-defined networks. Additionally, it discusses the potential of unmanned aerial vehicles (UAVs), network slicing, and wireless communication beyond 100 GHz and THz levels in improving haptic communication performance. The research emphasizes the importance of addressing security risks, optimizing resource allocation, and minimizing network congestion to unlock the potential of future networks and services. Aerial, ground, and underwater communication technologies are highlighted as key components of 6G networks, each with their advantages and challenges. The need for specialized equipment in remote areas to meet the bandwidth and latency requirements of haptic communication is underscored. The findings of this research contribute to a deeper understanding of haptic communication in the context of 6G networks and provide insights into potential solutions for overcoming the associated challenges.

ASSESSING THE POSSIBILITY OF IMPROVING RESCUE OPERATIONS WITH THE USE OF UAVS. CASE STUDIES FROM WIELKOPOLSKIE PROVINCE

The growing intensity of fire hazards is increasing the need for incorporating aerial vehicles including unmanned aerial vehicles, which are now becoming a valuable tool in the hands ofvarious state bodies and institutions, including the State Fire Service (SFS) in Poland, in supportingactivities aimed at saving human life, health or property. The article presents the characteristicsand purpose of the SFS and the range of tasks performed by it, as well as key problems in thisarea. Examples of the use of unmanned aerial vehicles (UAVs) in firefighting activities werealso presented. It was pointed out that improving the ability of SFS to carry out tasks should bea priority, especially in an era of cyclical threats arising from human activity or environmentalforces, and the continued growth of technology means that unmanned aerial vehicles can provideincreasing support. The purpose of the article was to present the scale and level of a fire threat andto indicate the potential of UAVs in combating it. The research was conducted on the exampleof a selected region. The article uses the case study method: 3 examples are presented to supportrescue operations using unmanned aerial vehicles. Descriptions of cases are supplemented byoperational conclusions and recommendations. It was assumed (research hypothesis) that thelarge-scale use of unmanned systems for search and rescue operations in the SFS would not onlyaffect the speed and effectiveness of the response, but also enhance the level of safety of the officersinvolved. The authors also emphasise the need of creating a new unit in the structures of theState Fire Service, which would deal exclusively with the implementation of tasks related to theprocessing, management, analysis and placement on the map of the necessary data on the hazards.This would make preventive measures more effective.e

УПРАВЛЕНЧЕСКИЕ РЕШЕНИЯ ДЛЯ РАЗВИТИЯ БЕСПИЛОТНОЙ АВИАЦИИ РОССИИ

This scientific article examines the use of unmanned aerial vehicles in urban life and education. In recent years, the development of unmanned aerial vehicles has achieved significant success, which has led to a growing interest in their use in various fields. In this study, we focus on the potential of unmanned aerial vehicles in urban environments and educational institutions. The main benefits are analyzed, such as time savings, reduced costs, and improved security.

Real-time operating system for autonomous drone control

Drones have become a transformative force across industries in the quickly changing world of autonomous technologies. Their applications are expanding, from precision agriculture to emergency response, thanks to developments in real-time operating systems. A key development is a "Real-time operating system for autonomous drone control" that reshapes the potential of unmanned aerial vehicles through the convergence of high-speed data processing, low-latency responsiveness, and complex control algorithms.

Caching and 3D Deployment Strategy for Scalable Videos in Cache-Enabled Multi-UAV Networks

Unleashing the potential of unmanned aerial vehicles (UAVs) to play the role of caching devices for furnishing layered video services to ground users (GUs) holds tremendous promise. However, it is crucial to strike a balance between the quality and the diversity of video content when deciding the placement strategies of video caching. In this study, we examine the feasibility of implementing a layered caching scheme utilizing scalable video coding (SVC) in three-dimensional (3D) multi-UAV networks with cache capabilities. The objective is to investigate the minimization of the cumulative video access delay experienced by all GUs through the joint design of layered scalable video caching, user-UAV association, and 3D UAV deployment. This approach enables the network to offer video services across a range of quality levels. We have formulated an intricate mixed-integer non-convex optimization problem which poses a significant challenge to solve directly. Then we decompose the formulated problem into three interrelated sub-problems and put forth specific algorithms designed to address each component, i.e., layered caching strategy and UAV's two-dimensional (2D) deployment optimization with penalty based difference-of-convex (penalty-DC) framework, user-UAV association optimization with bipartite graph based linear programming (LP) relaxation, as well as UAV altitude optimization with successive convex approximation (SCA) method. Then we introduce a highly efficient iterative algorithm that employs an alternate optimization approach for three sub-problems until convergence. Our simulation results demonstrate that our proposed algorithm surpasses other benchmark schemes significantly in terms of mitigating video access delay.

The assessment and management of plant invasions with Unmanned Aerial Vehicles. The Yucca gloriosa case, Italy

Invasive Alien Plants (IAPs) represent a severe threat to biodiversity and the functioning of crucial ecosystems such as coastal dunes. In this work, the Yucca gloriosa invasion along Italy’s Mediterranean coastal dunes was used as a case study to explore the potential of Unmanned Aerial Vehicles (UAVs). High-resolution images were collected during different seasons (spring: pre-flowering and fall: flowering stage) for detecting, mapping, and managing this IAP. Due to its peculiar foliar characteristics, Y. gloriosa is particularly suitable for assessing this UAV technique. We suggest springtime for data collection because light conditions are ideal and shading effects are minimal. Moreover, long-term dynamics of the Y. gloriosa invasion after plant removal over the last 10–15 years were investigated. The information presented here is the first step in the future development of an early detection program to manage with this IAP problem in the study area.

Estimating Crop Seed Composition Using Machine Learning from Multisensory UAV Data

The pre-harvest estimation of seed composition from standing crops is imperative for field management practices and plant phenotyping. This paper presents for the first time the potential of Unmanned Aerial Vehicles (UAV)-based high-resolution hyperspectral and LiDAR data acquired from in-season stand crops for estimating seed protein and oil compositions of soybean and corn using multisensory data fusion and automated machine learning. UAV-based hyperspectral and LiDAR data was collected during the growing season (reproductive stage five (R5)) of 2020 over a soybean test site near Columbia, Missouri and a cornfield at Urbana, Illinois, USA. Canopy spectral and texture features were extracted from hyperspectral imagery, and canopy structure features were derived from LiDAR point clouds. The extracted features were then used as input variables for automated machine-learning methods available with the H2O Automated Machine-Learning framework (H2O-AutoML). The results presented that: (1) UAV hyperspectral imagery can successfully predict both the protein and oil of soybean and corn with moderate accuracies; (2) canopy structure features derived from LiDAR point clouds yielded slightly poorer estimates of crop-seed composition compared to the hyperspectral data; (3) regardless of machine-learning methods, the combination of hyperspectral and LiDAR data outperformed the predictions using a single sensor alone, with an R2 of 0.79 and 0.67 for corn protein and oil and R2 of 0.64 and 0.56 for soybean protein and oil; and (4) the H2O-AutoML framework was found to be an efficient strategy for machine-learning-based data-driven model building. Among the specific regression methods evaluated in this study, the Gradient Boosting Machine (GBM) and Deep Neural Network (NN) exhibited superior performance to other methods. This study reveals opportunities and limitations for multisensory UAV data fusion and automated machine learning in estimating crop-seed composition.

Development of an Efficiency Platform Based on MQTT for UAV Controlling and DoS Attack Detection.

Several market sectors are attracted by the potential of unmanned aerial vehicles (UAVs), such as delivery, agriculture, and cinema, among others. UAVs are becoming part of Internet of Things (IoT) networks in the development of autonomous and scalable solutions. However, these vehicles are gradually becoming attractive targets for cyberattacks. This study proposes the development of an efficient platform based on the Message Queuing Telemetry Transport (MQTT) protocol for UAV control and Denial-of-Service (DoS) detection embedded in the UAV system. For the efficiency test, latency, network and memory consumption on the platform were measured, in addition to the correlation between payload and delay time. The results of efficiency tests were collected for the three levels of quality of service (QoS). A strong correlation greater than 90% was found between delay and data size for all QoS levels, showing almost a linear proportion. In DoS detection, the best results were a true positive rate (TPR) of 0.97 with 16 features from the AWID2 dataset using LightGBM with Bayesian optimization and data balancing. Unlike other studies, the built platform shows efficiency for UAV control and guarantees security in the communication with the broker and in the Wi-Fi UAV network.

Challenges and threats of unmanned aerial vehicles for aviation transport safety

The research problem of the article is formulated as follows: How do unmanned aerial vehicles (UAV) influence on aviation safety? New trends in the use of unmanned aerial vehicle technology have been correlated. The attention was paid to the most modern solutions of the battlefield. In each case of the implementation of drones in selected areas, an analysis of the risk for aviation was made. Bearing in mind the priority of using drones in a responsible and legal manner, taking into account the absolute safety of airspace zones, the hypothetical risk of the development of a given UAV branch should be assessed each time. In the paper the theoretical methods were used: analogy, system analysis, statistical, analytical and comparative method. Moreover, diagnostic survey carried out by aviation institutions was used. Analysis of the potential of unmanned aerial vehicles with an indication of development directions that are safe for aviation, bringing clear benefits from implementation in relation to standard, commonly used solutions. The results of the analysis performed, combined with the synthesis of selected facts and data from statistics, are to prove the necessity to apply remedial measures in the process of dynamic development of UAVs. The multidirectional opportunities created by the dynamics of UAV development is undeniably a challenge for companies producing specific unmanned utility devices. Risk assessment at every stage of the development of a new technology - in this case unmanned aerial vehicles is a key issue of implementation success.

Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization

The challenge of dynamic traffic demand in mobile networks is tackled by moving cells based on unmanned aerial vehicles. Considering the tremendous potential of unmanned aerial vehicles in the future, we propose a new heuristic algorithm for coverage optimization. The proposed algorithm is implemented based on a conditional generative adversarial neural network, with a unique multilayer sum-pooling loss function. To assess the performance of the proposed approach, we compare it with the optimal core-set algorithm and quasi-optimal spiral algorithm. Simulation results show that the proposed approach converges to the quasi-optimal solution with a negligible difference from the global optimum while maintaining a quadratic complexity regardless of the number of users.

Guest Editorial Special Issue on UAV Communications in 5G and Beyond Networks—Part II

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

Guest Editorial Special Issue on UAV Communications in 5G and Beyond Networks—Part I

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

Mathematical Modeling, Analysis and Evaluation of the Complexity of Flight Paths of Groups of Unmanned Aerial Vehicles in Aviation and Transport Systems

Recently, we have seen the rapidly growing popularity of unmanned aerial vehicles. This is due to some advantages, namely portability, the ability to fly over hard-to-reach areas without human intervention. They are also widely used for commercial purposes, agriculture, delivery, automation in warehouses. The potential of unmanned aerial vehicles is vast and demonstrates promising opportunities. However, when using these devices, the issue of safety is acute. This article presents a developed software application that is used to improve the efficiency of flight research of groups of unmanned aerial vehicles, based on a new method for assessing flight safety by comparing the complexity of specified air routes. A practical approach to modeling and evaluating the search for a safe way is proposed. A suitable method of research is computer and simulation modeling. It is suggested to use the spectrum of dynamic characteristics of the sequence as a formal attribute for analyzing routes. The method is illustrated by an example of comparing air trajectories according to the flight safety criterion. The software application is intended for use in the educational process when training specialists in transport security, robotics, and system analysis.