Drones, Volume 7, Issue 12 (December 2023) – 26 articles

With the continuous expansion of Unmanned Aerial Vehicle (UAV) applications, the threat of icing on UAV flights has garnered increased attention. Understanding the icing principles and developing anti-icing technologies for unmanned aircraft is a crucial step in mitigating the icing threat. However, existing research indicates that changes in Reynolds numbers have a significant impact on the physics of ice accretion. Icing studies on aircraft operating at high Reynolds numbers cannot be directly applied to unmanned aircraft, and mature anti-icing/deicing techniques for manned aircraft cannot be directly utilized for UAVs. This paper firstly provides a comprehensive overview of research on icing for fixed-wing UAVs, including various methods to study unmanned aircraft icing and the identified characteristics of icing on unmanned aircraft. Secondly, this paper focuses on discussing UAV anti-icing/deicing techniques, including those currently applied and under development, and examines the advantages and disadvantages of these techniques. Finally, the paper presents some recommendations regarding UAV icing research and the development of anti-icing/deicing techniques. Full article

In this paper, we address aerial tracking tasks by designing multi-phase aware networks to obtain rich long-range dependencies. For aerial tracking tasks, the existing methods are prone to tracking drift in scenarios with high demand for multi-layer long-range feature dependencies such as viewpoint change caused by the characteristics of the UAV shooting perspective, low resolution, etc. In contrast to the previous works that only used multi-scale feature fusion to obtain contextual information, we designed a new architecture to adapt the characteristics of different levels of features in challenging scenarios to adaptively integrate regional features and the corresponding global dependencies information. Specifically, for the proposed tracker (SiamMAN), we first propose a two-stage aware neck (TAN), where first a cascaded splitting encoder (CSE) is used to obtain the distributed long-range relevance among the sub-branches by the splitting of feature channels, and then a multi-level contextual decoder (MCD) is used to achieve further global dependency fusion. Finally, we design the response map context encoder (RCE) utilizing long-range contextual information in backpropagation to accomplish pixel-level updating for the deeper features and better balance the semantic and spatial information. Several experiments on well-known tracking benchmarks illustrate that the proposed method outperforms SOTA trackers, which results from the effective utilization of the proposed multi-phase aware network for different levels of features. Full article

This study focuses on addressing the tracking control problem for a coaxial unmanned aerial vehicle (UAV) without any prior knowledge of its dynamic model. To overcome the limitations of model-based control, a model-free approach based on terminal sliding mode control is proposed for achieving precise position and rotation tracking. The terminal sliding mode technique is utilized to approximate the unknown nonlinear model of the system, while the global stability with finite-time convergence of the overall system is guaranteed using the Lyapunov theory. Additionally, the selection of control parameters is addressed by incorporating the accelerated particle swarm optimization (APSO) algorithm. Finally, numerical simulation tests are provided to demonstrate the effectiveness and feasibility of the proposed design approach, which demonstrates the capability of the model-free control approach to achieve accurate tracking control even without prior knowledge of the system’s dynamic model. Full article

Uneven-aged mixed forests have been recognized as important contributors to biodiversity conservation, ecological stability, carbon sequestration, the provisioning of ecosystem services, and sustainable timber production. Recently, numerous studies have demonstrated the applicability of integrating remote sensing datasets with machine learning for forest management purposes, such as forest type classification and the identification of individual trees. However, studies focusing on the integration of unmanned aerial vehicle (UAV) datasets with machine learning for mapping of tree species groups in uneven-aged mixed forests remain limited. Thus, this study explored the feasibility of integrating UAV imagery with semantic segmentation-based machine learning classification algorithms to describe conifer and broadleaf species canopies in uneven-aged mixed forests. The study was conducted in two sub-compartments of the University of Tokyo Hokkaido Forest in northern Japan. We analyzed UAV images using the semantic-segmentation based U-Net and random forest (RF) classification models. The results indicate that the integration of UAV imagery with the U-Net model generated reliable conifer and broadleaf canopy cover classification maps in both sub-compartments, while the RF model often failed to distinguish conifer crowns. Moreover, our findings demonstrate the potential of this method to detect dominant tree species groups in uneven-aged mixed forests. Full article

Navigating multiple drones autonomously in complex and unpredictable environments, such as forests, poses a significant challenge typically addressed by wireless communication for coordination. However, this approach falls short in situations with limited central control or blocked communications. Addressing this gap, our paper explores the learning of complex behaviors by multiple drones with limited vision. Drones in a swarm rely on onboard sensors, primarily forward-facing stereo cameras, for environmental perception and neighbor detection. They learn complex maneuvers through the imitation of a privileged expert system, which involves finding the optimal set of neural network parameters to enable the most effective mapping from sensory perception to control commands. The training process adopts the Dagger algorithm, employing the framework of centralized training with decentralized execution. Using this technique, drones rapidly learn complex behaviors, such as avoiding obstacles, coordinating movements, and navigating to specified targets, all in the absence of wireless communication. This paper details the construction of a distributed multi-UAV cooperative motion model under limited vision, emphasizing the autonomy of each drone in achieving coordinated flight and obstacle avoidance. Our methodological approach and experimental results validate the effectiveness of the proposed vision-based end-to-end controller, paving the way for more sophisticated applications of multi-UAV systems in intricate, real-world scenarios. Full article

In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV’s environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the autonomous tracking and landing of UAVs. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAVs was established. Tracking performance was verified by tracking three trajectories of an unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance. Full article

Ducted UAVs have attracted much attention because the duct structure can reduce the propeller tip vortices and thus increase the effective lift area of the lower propeller. This paper investigates the effects of parameters on the aerodynamic characteristics of ducted UAVs, such as co-axial twin propeller configuration and duct structure. The aerodynamic characteristics of the UAV were analyzed using CFD methods, while the impact sensitivity analysis of the simulation data was sorted using the orthogonal test method. The results indicate that, while maintaining overall strength, increasing the propeller spacing by about 0.055 times the duct chord length can increase the lift of the upper propeller by approximately 1.3% faster. Reducing the distance between the propeller and the top surface of the duct by about 0.5 times the duct chord length can increase the lift of the lower propeller by approximately 7.7%. Increasing the chord length of the duct cross-section by about 35.3% can simultaneously make the structure of the duct and the total lift of the drone faster by approximately 150.6% and 15.7%, respectively. This research provides valuable guidance and reference for the subsequent overall design of ducted UAVs. Full article

Unmanned aerial vehicles (UAVs) have found expanding utilization in smart agriculture. Path loss (PL) is of significant importance in the link budget of UAV-aided air-to-ground (A2G) communications. This paper proposes a machine-learning-based PL model for A2G communication in agricultural scenarios. On this basis, a double-weight neurons-based artificial neural network (DWN-ANN) is proposed, which can strike a fine equilibrium between the amount of measurement data and the accuracy of predictions by using ray tracing (RT) simulation data for pre-training and measurement data for optimization training. Moreover, an RT pre-correction module is introduced into the DWN-ANN to optimize the impact of varying farmland materials on the accuracy of RT simulation, thereby improving the accuracy of RT simulation data. Finally, channel measurement campaigns are carried out over a farmland area at 3.6 GHz, and the measurement data are used for the training and validation of the proposed DWN-ANN. The prediction results of the proposed PL model demonstrate a fine concordance with the measurement data and are better than the traditional empirical models. Full article

This paper presents a fixed-time extended state observer-based adaptive sliding mode controller evaluated in a quadrotor unmanned aerial vehicle subject to severe turbulent wind while executing a desired trajectory. Since both the state and model of the system are assumed to be partially known, the observer, whose convergence is independent from the initial states of the system, estimates the full state, model uncertainties, and the effects of turbulent wind in fixed time. Such information is then compensated via feedback control conducted by a class of adaptive sliding mode controller, which is robust to perturbations and reduces the chattering effect by non-overestimating its adaptive gain. Furthermore, the stability of the closed-loop system is analyzed by means of the Lyapunov theory. Finally, simulation results validate the feasibility and advantages of the proposed strategy, where the observer enhances performance. For further demonstration, a comparison with an existent approach is provided. Full article

Recently, deep learning methods and multisensory fusion have been applied to address odometry challenges in unmanned ground vehicles (UGVs). In this paper, we propose an end-to-end visual-lidar-inertial odometry framework to enhance the accuracy of pose estimation. Grayscale images, 3D point clouds, and inertial data are used as inputs to overcome the limitations of a single sensor. Convolutional neural network (CNN) and recurrent neural network (RNN) are employed as encoders for different sensor modalities. In contrast to previous multisensory odometry methods, our framework introduces a novel attention-based fusion module that remaps feature vectors to adapt to various scenes. Evaluations on the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI) odometry benchmark demonstrate the effectiveness of our framework. Full article

Light small-sized, multi-rotor UAVs, with their notable advantages of portability, intelligence, and low cost, occupy a significant share in the civilian UAV market. To further reduce the full lifecycle cost of products, shorten development cycles, and increase market share, some manufacturers of these UAVs have adopted a series development strategy based on the concept of commonality in design. However, there is currently a lack of effective methods to quantify the commonality in UAV designs, which is key to guiding commonality design. In view of this, our study innovatively proposes a new UAV commonality evaluation model based on the basic composition of light small-sized multi-rotor UAVs and the theory of design structure matrices. Through cross-evaluations of four models, the model has been confirmed to comprehensively quantify the degree of commonality between models. To achieve commonality prediction in the early stages of multi-rotor UAV design, we constructed a commonality prediction dataset centered around the commonality evaluation model using data from typical light small-sized multi-rotor UAV models. After training this dataset with convolutional neural networks, we successfully developed an effective predictive model for the commonality of new light small-sized multi-rotor UAV models and verified the feasibility and effectiveness of this method through a case application in UAV design. The commonality evaluation and prediction models established in this study not only provide strong decision-making support for the series design and commonality design of UAV products but also offer new perspectives and tools for strategic development in this field. Full article

With the rapid advancement of unmanned aerial vehicles (UAVs) and the Internet of Things (IoTs), UAV-assisted IoTs has become integral in areas such as wildlife monitoring, disaster surveillance, and search and rescue operations. However, recent studies have shown that these systems are vulnerable to adversarial example attacks during data collection and transmission. These attacks subtly alter input data to trick UAV-based deep learning vision systems, significantly compromising the reliability and security of IoTs systems. Consequently, various methods have been developed to identify adversarial examples within model inputs, but they often lack accuracy against complex attacks like C&W and others. Drawing inspiration from model visualization technology, we observed that adversarial perturbations markedly alter the attribution maps of clean examples. This paper introduces a new, effective detection method for UAV vision systems that uses attribution maps created by model visualization techniques. The method differentiates between genuine and adversarial examples by extracting their unique attribution maps and then training a classifier on these maps. Validation experiments on the ImageNet dataset showed that our method achieves an average detection accuracy of 99.58%, surpassing the state-of-the-art methods. Full article

Recent studies have been focusing on unmanned aircraft systems (UASs) to inspect safety issues in the construction industry. A UAS can monitor a broad range in real time and identify unsafe situations and objects at the jobsite. The related studies mostly focus on technological development, and there are few studies investigating potential performance that can be obtained by implementing UASs in the construction domain. Hence, the main objective of this research is to evaluate the potential of UAS-based construction safety inspection. To achieve the goal, this study developed a system dynamic (SD) model, and scenario analysis was conducted. When compared to the existing methods, the use of a UAS resulted in improved safety inspection performance, reduced possibility of incidents, reduced worker fatigue, and reduced amount of delayed work. The results of this research verified that UAS-based safety inspections can be more effective than existing methods. The results of this study can contribute to the understanding of UAS-based construction safety inspection technologies and the potential of the technology. Full article

Understanding the atmospheric conditions in remote areas contributes to assessing local weather phenomena. Obtaining vertical profiles of the atmosphere in isolated locations can introduce significant challenges for the deployment and maintenance of equipment, as well as regulatory obstacles. Here, we assessed the potential of consumer drones equipped with lightweight atmospheric sensors to collect vertical meteorological profiles off One Tree Island (Great Barrier Reef), located approximately 85 km off the east coast of Australia. We used a DJI Matrice 300 drone with two InterMet Systems iMet-XQ2 UAV sensors, capturing data on atmospheric pressure, temperature, relative humidity, and wind up to an altitude of 1500 m. These flights were conducted three times per day (9 a.m., 12 noon, and 3 p.m.) and compared against ground-based weather sensors. Over the Austral summer/autumn, we completed 72 flights, obtaining 24 complete sets of daily measurements of atmospheric characteristics over the entire vertical profile. On average, the atmospheric temperature and dewpoint temperature were significantly influenced by the time of sampling, and also varied among days. The mean daily temperature and dewpoint temperature reached their peaks at 3 p.m., with the temperature gradually rising from its morning low. The mean dewpoint temperature obtained its lowest point around noon. We also observed wind speed variations, but changes in patterns throughout the day were much less consistent. The drone-mounted atmospheric sensors exhibited a consistent warm bias in temperature compared to the reference weather station. Relative humidity showed greater variability with no clear bias pattern, indicating potential limitations in the humidity sensor’s performance. Microscale temperature inversions were prevalent around 1000 m, peaking around noon and present in approximately 27% of the profiles. Overall, the drone-based vertical profiles helped characterise atmospheric dynamics around One Tree Island Reef and demonstrated the utility of consumer drones in providing cost-effective meteorological information in remote, environmentally sensitive areas. Full article

Research on developing a smart security system is based on Artificial Intelligence with an unmanned aerial vehicle (UAV) to detect and monitor alert situations, such as fire accidents and theft/intruders in the building or factory, which is based on the Internet of Things (IoT) network. The system includes a Passive Pyroelectric Infrared Detector for human detection and an analog flame sensor to sense the appearance of the concerned objects and then transmit the signal to the workstation via Wi-Fi based on the microcontroller Espressif32 (Esp32). The computer vision models YOLOv8 (You Only Look Once version 8) and Cascade Classifier are trained and implemented into the workstation, which is able to identify people, some potentially dangerous objects, and fire. The drone is also controlled by three algorithms—distance maintenance, automatic yaw rotation, and potentially dangerous object avoidance—with the support of a proportional–integral–derivative (PID) controller. The Smart Drone Surveillance System has good commands for automatic tracking and streaming of the video of these specific circumstances and then transferring the data to the involved parties such as security or staff. Full article

We propose a novel 6D pose estimation approach tailored for auto-landing fixed-wing unmanned aerial vehicles (UAVs). This method facilitates the simultaneous tracking of both position and attitude using a ground-based vision system, regardless of the number of cameras (N-cameras), even in Global Navigation Satellite System-denied environments. Our approach proposes a pipeline consisting of a Convolutional Neural Network (CNN)-based detection of UAV anchors which, in turn, drives the estimation of UAV pose. In order to ensure robust and precise anchor detection, we designed a Block-CNN architecture to mitigate the influence of outliers. Leveraging the information from these anchors, we established an Extended Kalman Filter to continuously update the UAV’s position and attitude. To support our research, we set up both monocular and stereo outdoor ground view systems for data collection and experimentation. Additionally, to expand our training dataset without requiring extra outdoor experiments, we created a parallel system that combines outdoor and simulated setups with identical configurations. We conducted a series of simulated and outdoor experiments. The results show that, compared with the baselines, our method achieves 3.0% anchor detection precision improvement and 19.5% and 12.7% accuracy improvement of position and attitude estimation. Furthermore, these experiments affirm the practicality of our proposed architecture and algorithm, meeting the stringent requirements for accuracy and real-time capability in the context of auto-landing fixed-wing UAVs. Full article

This study focuses on the path-planning problem of rescue UAVs with regional detection priority. Initially, we propose a mixed-integer programming model that integrates coverage path planning (CPP) and the hierarchical traveling salesman problem (HTSP) to address multi-regional path planning under priority constraints. For intra-regional path planning, we present an enhanced method for acquiring reciprocating flight paths to ensure complete coverage of convex polygonal regions with shorter flight paths when a UAV is equipped with sensors featuring circular sampling ranges. An additional comparison was made for spiral flight paths, and second-order Bezier curves were employed to optimize both sets of paths. This optimization not only reduced the path length but also enhanced the ability to counteract inherent drone jitter. Additionally, we propose a variable neighborhood descent algorithm based on K-nearest neighbors to solve the inter-regional access order path-planning problem with priority. We establish parameters for measuring distance and evaluating the priority order of UAV flight paths. Simulation and experiment results demonstrate that the proposed algorithm can effectively assist UAVs in performing path-planning tasks with priority constraints, enabling faster information collection in important areas and facilitating quick exploration of three-dimensional characteristics in unknown disaster areas by rescue workers. This algorithm significantly enhances the safety of rescue workers and optimizes crucial rescue times in key areas. Full article

Achieving neighbor discovery for a directional flying ad hoc network (FANET) with multiple channels poses challenges for media access control (MAC) protocol design, as it requires simultaneous main lobe and channel rendezvous while dealing with the high UAV mobility. In order to achieve fast neighbor discovery for initial access without coordination or prior information, we first establish the theoretical supremum for the directional main lobe. Then, to achieve the supremum, we introduce the BR-DA and BR-DA-FANET algorithms to respectively establish the supremum on rendezvous between a pair of UAVs’ main lobes and rendezvous of main lobes for all UAVs in the FANET. To further accelerate the neighbor discovery process, we propose the neighbor discovery with location prediction protocol (ND-LP) and the avoiding communication interruption with location prediction (ACI-LP) protocol. ND-LP enables quick main lobe rendezvous and channel rendezvous, while ACI-LP enables beam tracking and channel rendezvous together with the avoidance of communication interruptions. The simulation results demonstrate that the proposed protocols outperform the state-of-the-art works in terms of neighbor discovery delay. Full article

End-to-end deep neural network (DNN)-based motion planners have shown great potential in high-speed autonomous UAV flight. Yet, most existing methods only employ a single high-capacity DNN, which typically lacks generalization ability and suffers from high sample complexity. We propose a novel event-triggered hierarchical planner (ETHP), which exploits the bi-level optimization nature of the navigation task to achieve both efficient training and improved optimality. Specifically, we learn a depth-image-based end-to-end motion planner in a hierarchical reinforcement learning framework, where the high-level DNN is a reactive collision avoidance rerouter triggered by the clearance distance, and the low-level DNN is a goal-chaser that generates the heading and velocity references in real time. Our training considers the field-of-view constraint and explores the bi-level structural flexibility to promote the spatio–temporal optimality of planning. Moreover, we design simple yet effective rules to collect hindsight experience replay buffers, yielding more high-quality samples and faster convergence. The experiments show that, compared with a single-DNN baseline planner, ETHP significantly improves the success rate and generalizes better to the unseen environment. Full article

The Charleston Estuarine System Stock (CESS) of Tamanend’s bottlenose dolphins (Tursiops erebennus) exhibit long-term site fidelity to the Charleston Harbor, and the Ashley, Cooper, and Wando Rivers in Charleston, South Carolina, USA. In the Cooper River, dolphins have been irregularly sighted in upper regions where salinity levels are below what is considered preferred dolphin habitat. We conducted unoccupied aerial system (UAS) surveys in high-salinity (>15 parts per thousand) and low-salinity (<15 parts per thousand) regions (n = 8 sites) of the Cooper River and surrounding waters to assess dolphin distribution in terms presence/absence, detection rate, abundance, and density. We also assessed the influence of ecological factors (salinity, water temperature, season, and prey availability) on dolphin distribution. Dolphins were detected at five sites, with higher salinity and water temperature being significant predictors of presence and abundance. Dolphins were detected year-round across high-salinity sites, and were infrequently detected in low-salinity sites during months with warmer water temperatures. The results from this study contribute to the overall understanding of dolphin distribution across various habitats within the Charleston Estuary System and the potential drivers for their movement into low-salinity waters. Full article

In this study, a reconfigurable intelligent surface (RIS)-assisted wireless-powered mobile edge computing (WP-MEC) system is proposed, where a single-antenna unmanned aerial vehicle (UAV)-mounted cloudlet provides offloading opportunities to K user equipments (UEs) with a single antenna, and the K UEs can harvest the energy from the broadcast radio-frequency signals of the UAV. In addition, rate-splitting multiple access is used to provide offloading opportunities to multiple UEs for effective power control and high spectral efficiency. The aim of this paper is to minimize the total energy consumption by jointly optimizing the resource allocation in terms of time, power, computing frequency, and task load, along with the UAV trajectory and RIS phase-shift matrix. Since coupling issues between optimization variable designs are caused, however, an alternating optimization-based algorithm is developed. The performance of the proposed algorithm is verified via simulations and compared with the benchmark schemes of partial optimizations of resource allocation, path planning, and RIS phase design. The proposed algorithm exhibits high performance in WP-MEC systems with insufficient resources, e.g., achieving up to 40% energy reduction for a UAV with eight elements of RIS. Full article

Unmanned Aerial Vehicles (UAVs), a subset of aerial robots, play crucial roles in various domains, such as disaster management, agriculture, and healthcare. Their application proves invaluable in situations where human intervention poses risks or involves high costs. However, traditional approaches to UAV path planning struggle in efficiently navigating complex and dynamic environments, often resulting in suboptimal routes and extended mission durations. This study seeks to investigate and improve the utilization of meta-heuristic algorithms for optimizing UAV path planning. Toward this aim, we carried out a systematic review of five major databases focusing on the period from 2018 to 2022. Following a rigorous two-stage screening process and a thorough quality appraisal, we selected 68 papers out of the initial 1500 to answer our research questions. Our findings reveal that hybrid algorithms are the dominant choice, surpassing evolutionary, physics-based, and swarm-based algorithms, indicating their superior performance and adaptability. Notably, time optimization takes precedence in mathematical models, reflecting the emphasis on CPU time efficiency. The prevalence of dynamic environmental types underscores the importance of real-time considerations in UAV path planning, with three-dimensional (3D) models receiving the most attention for accuracy in complex trajectories. Additionally, we highlight the trends and focuses of the UAV path planning optimization research community and several challenges in using meta-heuristic algorithms for the optimization of UAV path planning. Finally, our analysis further highlights a dual focus in UAV research, with a significant interest in optimizing single-UAV operations and a growing recognition of the challenges and potential synergies in multi-UAV systems, alongside a prevalent emphasis on single-target mission scenarios, but with a notable subset exploring the complexities of multi-target missions. Full article

The implementation of distributed propulsion and boundary layer ingestion for unmanned aerial vehicles represents various challenges for the design of embedded ducts in blended wing body configurations. This work explores the conceptual design and evaluation of DP configurations with BLI. The aerodynamic integration of each configuration is evaluated following a proposed framework, including simulation analysis. Power saving coefficient and propulsive efficiency were compared against a baseline podded case. The results show the optimal propulsion configuration for the BWB UAV obtaining 3.95% of power benefit and propulsive efficiency $({\eta }_p>80\%)$. Indeed, the aerodynamic integration effects for the proposed design maintain the BWB’s aerodynamic efficiency, which will contribute to longer endurance and better performance. Full article

Uncrewed aerial vehicles (UAVs), commonly known as drones, have emerged as transformative tools in the healthcare sector, offering the potential to revolutionize medical logistics, emergency response, and patient care. This scoping review provides a comprehensive exploration of the diverse applications of drones in healthcare, addressing critical gaps in existing literature. While previous reviews have primarily focused on specific facets of drone technology within the medical field, this study offers a holistic perspective, encompassing a wide range of potential healthcare applications. The review categorizes and analyzes the literature according to key domains, including the transport of biomedical goods, automated external defibrillator (AED) delivery, healthcare logistics, air ambulance services, and various other medical applications. It also examines public acceptance and the regulatory framework surrounding medical drone services. Despite advancements, critical knowledge gaps persist, particularly in understanding the intricate interplay between technological challenges, the existing regulatory framework, and societal acceptance. This review highlights the need for the extensive validation of cost-effective business cases, the development of control techniques that can address time and resource savings within the constraints of real-life scenarios, the design of crash-protected containers, and the establishment of corresponding tests and standards to demonstrate their conformity. Full article

The operation of unmanned aircraft systems in shared airspace can serve as an accelerator for the global economy and a sensitive addition to the existing mix of transportation modes. For these reasons, concepts of Unmanned Traffic Management have been recently published, defining advanced rules for all potential participants in the operation of unmanned systems. Airspace primarily dedicated to automated unmanned system operations, referred to as U-space in Europe, needs to be designated with consideration for the surrounding airspace. This is especially important in cases where the airspace is controlled, and when declaring U-space airspace, it is necessary to pay particular attention to the density of surrounding air traffic. The goal of this article is to assess the suitability of establishing U-space airspace for Urban Air Mobility in terms of traffic density in a controlled area above the selected metropolis, which is Prague, Czech Republic. To achieve this goal, data on air traffic in the given area were analyzed to obtain precise information about the traffic distribution. Areas in which the establishment of U-space airspace is possible both without implementing dynamic reconfiguration and with the application of the dynamic reconfiguration concept were also selected. The result is the determination of whether it is possible to establish U-space in airspace, as in the analyzed case of the Ruzyně CTR, U-space can be introduced in 83 % of the territory. Full article