Drones, Volume 7, Issue 1 (January 2023) – 64 articles

Drones have been included in more and more activities in various domains, such as military, commercial and personal use. The existing legislative framework insufficiently addresses the responsibility and preventive measures angles in case of vulnerability exploitation and negligence in drone usage. Such aspects can be addressed by the industry in technological processes and standardization. These are especially important aspects given the high impact that misuse of drones can have on individuals, property and buildings within the flight zone when the drone is misused. The aim of this research paper is to investigate how these elements are viewed in existing legislation and by individuals, while taking into account the technical specifics and the stakeholder ecosystem of drone usage. In this respect, we use a complex questionnaire which was sent to a final number of 233 respondents pertaining to firms specialized in IT, legal and cybersecurity. The responses have been analyzed from a qualitative and quantitative perspective. Our results highlight the areas of improvement in the existing standardization and find the followings: (1) stakeholders across the drone ecosystem are viewed as having a shared liability in certain use cases, (2) preventive measure implementation should be dispersed across the stakeholders of drone usage and (3) automation of prevention measures is considered more useful in case of malfunctioning or misuse of drones rather than user manual intervention. In addition, we make proposals to accommodate new policy requirements for the above use cases. The results of this research paper assist policy makers in improving existing standardization framework and technological processes concerning drone usage, but also stakeholders of the drone ecosystem in generating increased trust of the drone users. Further, this research paper can also assist drone software and hardware producers in calibrating their products to ensure trust of the users. In addition, trust in the use of drones for commercial and personal purposes is increased through standardization and proper approaches for situations that may cause damages to drones and to third parties. Full article

Registration of unmanned aircraft is a common policy around the world and forms part of the International Civil Aviation Organisation’s model regulations for unmanned aircraft. This study conducts a review of the various registration policies that have been implemented amongst advanced economies to find commonalities and differences. New Zealand is then used as a case study. The country does not currently have registration of unmanned aircraft; however, their Ministry of Transport has put forward the idea of implementing a registration scheme. As part of this case study, the ownership characteristics of 919 New Zealand unmanned aircraft users were collected using an online survey. The results highlight that personally owned aircraft tend to only be used by their owner, with the number of users being lower than the number of aircraft. For organisationally owned aircraft, there are multiple users per aircraft; however, these users tend to only be employees of the organisation. These findings suggest that for New Zealand, the best way to implement a registration scheme would be to register users and organisations rather than individual aircraft. While specific to New Zealand, these findings also prompt the need for future research worldwide to see whether registration schemes reconcile with ownership data. Full article

With the continuous development of UAV technology and swarm intelligence technology, the UAV formation cooperative mission has attracted wide attention because of its remarkable function and flexibility to complete complex and changeable tasks, such as search and rescue, resource exploration, reconnaissance and surveillance. The collaborative trajectory planning of UAV formation is a key part of the task execution. This paper attempts to provide a comprehensive review of UAV formation trajectory planning algorithms. Firstly, from the perspective of global planning and local planning, a simple framework of the UAV formation trajectory planning algorithm is proposed, which is the basis of comprehensive classification of different types of algorithms. According to the proposed framework, a classification method of existing UAV formation trajectory planning algorithms is proposed, and then, different types of algorithms are described and analyzed statistically. Finally, the challenges and future research directions of the UAV formation trajectory planning algorithm are summarized and prospected according to the actual requirements. It provides reference information for researchers and workers engaged in the formation flight of UAVs. Full article

When employing remote sensing images, it is challenging to classify vegetation species and ground objects due to the abundance of wetland vegetation species and the high fragmentation of ground objects. Remote sensing images are classified primarily according to their spatial resolution, which significantly impacts the classification accuracy of vegetation species and ground objects. However, there are still some areas for improvement in the study of the effects of spatial resolution and resampling on the classification results. The study area in this paper was the core zone of the Huixian Karst National Wetland Park in Guilin, Guangxi, China. The aerial images (Am) with different spatial resolutions were obtained by utilizing the UAV platform, and resampled images (An) with different spatial resolutions were obtained by utilizing the pixel aggregation method. In order to evaluate the impact of spatial resolutions and resampling on the classification accuracy, the Am and the An were utilized for the classification of vegetation species and ground objects based on the geographic object-based image analysis (GEOBIA) method in addition to various machine learning classifiers. The results showed that: (1) In multi-scale images, both the optimal scale parameter (SP) and the processing time decreased as the spatial resolution diminished in the multi-resolution segmentation process. At the same spatial resolution, the SP of the An was greater than that of the Am. (2) In the case of the Am and the An, the appropriate feature variables were different, and the spectral and texture features in the An were more significant than those in the Am. (3) The classification results of various classifiers in the case of the Am and the An exhibited similar trends for spatial resolutions ranging from 1.2 to 5.9 cm, where the overall classification accuracy increased and then decreased in accordance with the decrease in spatial resolution. Moreover, the classification accuracy of the Am was higher than that of the An. (4) When vegetation species and ground objects were classified at different spatial scales, the classification accuracy differed between the Am and the An. Full article

Energy grids represent a fundamental infrastructure of any country. These structures consist of many kilometres of power lines that must be periodically inspected and maintained. Among the necessary operations are installing and removing bird diverters to reduce bird strikes on power lines. These devices are intended to improve birds’ detection of power lines and reduce the risk of collision. Often, the installation and removal of bird diverters from power lines is accomplished by humans operating from helicopters or directly on the power lines. Apart from the considerable cost of these operations, working in elevated environments creates human safety risks. To reduce these risks, this paper proposes a novel solution to automatize these tasks. The proposed solution is a prototype gripper that can be mounted on unmanned aerial vehicles (UAVs) and remotely operated to install or remove bird diverters. This work presents a mechatronic device and software architecture system that is experimentally evaluated in a laboratory mock-up, which consists of a manipulator equipped with the proposed tool for removing a bird diverter. Future work is needed to deploy the proposed tool on a UAV. Full article

In the traditional express delivery sector, trucks are the most available and efficient transportation mode in urban areas. However, due to the pressures of traffic congestion and air pollution problems, many cities have implemented strict measures to restrict trucks’ access to many zones during specified time periods, which has caused significant effects on the business of the industry. Due to their advantages, which include high speed, flexibility, and environmental friendliness, drones have great potential for being combined with trucks for efficient delivery in restricted traffic zones. In this paper, we propose a cooperative truck and drone delivery path optimization problem, in which a truck carrying cargo travels along the outer boundary of the restricted traffic zone to send and receive a drone, and the drone is responsible for delivering the cargo to customers. The objective of the problem is to minimize the completion time of all delivery tasks. To efficiently solve this problem, we propose a hybrid metaheuristic optimization algorithm to cooperatively optimize the outer path of the truck and the inner path of the drone. We conduct experiments on a set of test instances; the results demonstrate that the proposed algorithm exhibits a competitive performance compared to other selected popular optimization algorithms. Full article

This work demonstrates distributed motion planning for multi-rotor unmanned aerial vehicle in a windy outdoor environment. The motion planning is modeled as a receding horizon mixed integer nonlinear programming (RH-MINLP) problem. Each quadrotor solves an RH-MINLP to generate its time-optimal speed profile along a minimum snap spline path while satisfying constraints on kinematics, dynamics, communication connectivity, and collision avoidance. The presence of wind disturbances causes the motion planner to continuously regenerate new motion plans, thereby significantly increasing the computational time and possibly leading to safety violations. Control Barrier Functions (CBFs) are used for assist in collision avoidance in the face of wind disturbances while alleviating the need to recalculate the motion plans continually. The RH-MINLPs are solved using a novel combination of heuristic and optimal methods, namely Simulated Annealing and interior-point methods, respectively, to handle discrete variables and nonlinearities in real-time feasibly. The framework is validated in simulations featuring up to 50 quadrotors and Hardware-in-the-loop (HWIL) experiments, followed by outdoor field tests featuring up to 6 DJI M100 quadrotors. Results demonstrate (1) fast online motion planning for outdoor communication-centric multi-quadrotor operations and (2) the utility of CBFs in providing effective motion plans. Full article

Background: Unmanned Aerial Vehicles (UAVs) are increasingly being used commercially for crop protection in East Asia as a new type of equipment for pesticide applications, which is receiving more and more attention worldwide. A new model of pear cultivation called the ‘Double Primary Branches Along the Row Flat Type’ standard trellised pear orchards (FT orchard) is widely used in China, Japan, Korea, and other Asian countries because it saves manpower and is suitable for mechanization compared to traditional spindle and open-center cultivation. The disease and pest efficacy of the flat-type trellised canopy structure of this cultivation is a great challenge. Therefore, a UAV spraying trial was conducted in an FT orchard, and a four-factor (SV: Spray application volume rate, FS: Flight speed, FH: Flight height, FD: Flight direction) and 3-level orthogonal test were designed. Results: These data were used to analyze the effect, including spray coverage, deposit density, coefficient of variation, and penetration coefficient on the canopy, to determine the optimal operating parameters of the UAV for pest efficacy in FT orchards. The analysis of extremes of variance showed that factor FD had a significant effect on both spray coverage and deposition density. Followed by factor FS, which had a greater effect on spray coverage (p < 0.05), and factor SV, FH, which had a greater effect on deposition density (p < 0.05). The effects of different factors on spray coverage and deposit density were FD > FS > FH > SV, FD > FH > SV > FS, in that order. The SV3-FS1-FH1-FD3, which flight along the row with an application rate of 90 L/ha, a flight speed of 1.5 m/s, and a flight height of 4.5 m, was the optimal combination, which produced the highest deposit density and spray coverage. It was determined through univariate analysis of all experimental groups, using droplet density of 25/cm² and spray coverage of 1%, and uniformity of 40% as the measurement index, that T4 and T8 performed the best and could meet the control requirements in different horizontal and vertical directions of the pear canopy. The parameters were as follows: flight along the tree rows, application rate not less than 75 L/ha, flight speed no more than 2 m/s, and flight height not higher than 5 m. Conclusion: This article provides ample data to promote innovation in the use of UAVs for crop protection programs in pergola/vertical trellis system orchards such as FT orchards. At the same time, this project provided a comprehensive analysis of canopy deposition methods and associated recommendations for UAV development and applications. Full article

With the strengths of quickness, low cost, and adaptability, unmanned aerial vehicle (UAV) communication is widely utilized in the next-generation wireless network. However, some risks and hidden dangers such as UAV “black flight” disturbances, attacks, and spying incidents lead to the necessity of the real-time supervision of UAVs. A compressed sensing-based genetic Markov localization method is proposed in this paper for two-dimensional trajectory tracking of the mobile transmitter in a finite domain, which consists of three modules: the multi-station sampling module, the reconstruction module, and the localization module. In the multi-station sampling module, multiple stations are deployed to receive the signal transmitted by the UAV using compressed sensing, and the motion model of the mobile transmitter is the constant turn rate and acceleration (CTRA) model. In the reconstruction module, we propose a direct reconstruction method to extract the joint cross-spatial spectrum. In the genetic Markov localization module, we propose a two-step localization method to genetically correct the inaccurate points in the preliminary results and generate the tracking result. Extensive simulations are conducted to verify the effectiveness of the proposed method. The results show that the proposed method is superior to the particle filter method and the Markov Monte Carlo method at all sampling moments. Specifically, when SNR = 15dB, the root-mean-square error (RMSE) of the proposed method is $39\%$ and $60\%$ lower than that of the other two methods, respectively. Moreover, under the premise that the RMSE of the localization result is less than 30 m, the reconstruction module can reduce the running time of the proposed method by $33.3\%$. Full article

Multi-UAV cooperative trajectory planning (MUCTP) refers to the planning of multiple flyable trajectories based on the location of each UAV and mission point in a complex environment. In the planning process, the complex 3D space structure increases the difficulty of solving the trajectory points, and the mutual constraints of the UAV cooperative constraints can degrade the performance of the planning system. Therefore, to improve the efficiency of MUCTP, this study proposes MUCTP based on feasible domain space and adaptive differential evolution algorithm (FDS-ADEA). The method first constructs a three-dimensional feasible domain space to reduce the complexity of the search space structure; then, the constraints of heterogeneous UAVs are linearly weighted and transformed into a new objective function, and the information of the fitness value is shared in accordance with the adaptive method and the code correction method to improve the search efficiency of the algorithm; finally, the trajectories are smoothed to ensure the flyability of the UAVs performing the mission by combining the cubic B-spline curves. Experiments 1, 2, 3, and 4 validate the proposed algorithm. Simulation results verify that FDS-ADEA has fast convergence, high cooperative capability, and more reasonable planned trajectory sets when processing MUCTP. Full article

Driven by recent technological breakthroughs, the electric vertical take-off and landing (eVTOL) aircraft has gained considerable attention. The widespread demand for eVTOL aircraft can be attributed to their potential use in the commercialisation of urban air mobility (UAM) in low-altitude urban airspaces. However, the urban low-altitude airspace environment is complex. UAM has a high traffic density and the eVTOL aircraft specifications are not uniform. Particularly in commercial scenarios, controlling eVTOL aircraft and ensuring safety in UAMs are the two major problems that should be addressed in future studies. The design of reasonable traffic rules is a potential solution; hence, we organised a UAM traffic rule system and proposed several alternative UAM traffic rules from three perspectives: a single eVTOL aircraft, a certain route, and key control areas. In addition, we validated these traffic rules using multi-rotor and fixed-wing eVTOL aircraft. The results show that designing reasonable traffic rules can facilitate attaining the primary objectives of commercialisation of UAM. Full article

Privacy preservation of image data has been a top priority for many applications. The rapid growth of technology has increased the possibility of creating fake images using social media as a platform. However, many people, including researchers, rely on image data for various purposes. In rural areas, lane images have a high level of importance, as this data can be used for analyzing various lane conditions. However, this data is also being forged. To overcome this and to improve the privacy of lane image data, a real-time solution is proposed in this work. The proposed methodology assumes lane images as input, which are further classified as fake or bona fide images with the help of Error Level Analysis (ELA) and artificial neural network (ANN) algorithms. The U-Net model ensures lane detection for bona fide lane images, which helps in the easy identification of lanes in rural areas. The final images obtained are secured by using the proxy re-encryption technique which uses RSA and ECC algorithms. This helps in ensuring the privacy of lane images. The cipher images are maintained using fog computing and processed with integrity. The proposed methodology is necessary for protecting genuine satellite lane images in rural areas, which are further used by forecasters, and researchers for making interpretations and predictions on data. Full article

Strapdown celestial imaging sensors provide a compact, lightweight alternative to their gimbaled counterparts. Strapdown imaging systems typically require a wider field of view, and consequently longer exposure intervals, leading to significant motion blur. The motion blur for a constellation of stars results in a constellation of trails on the image plane. We present a method that extracts the path of these star trails, and uses a linearized weighted least squares approach to correct noisy inertial attitude measurements. We demonstrate the validity of this method through its application to synthetically generated images, and subsequently observe its relative performance by using real images. The findings of this study indicate that the motion blur present in strapdown celestial imagery yields an a posteriori mean absolute attitude error of less than 0.13 degrees in the yaw axis, and 0.06 degrees in the pitch and roll axes (3 $\sigma$) for a calibrated wide-angle camera lens. These findings demonstrate the viability of low-cost, wide-angle, strapdown celestial attitude sensors on lightweight UAV hardware. Full article

One of the main objectives of today’s archaeological sites and museums is the development of research, understood as the interpretation and contextualisation of tangible and intangible cultural heritage to broaden the knowledge and accessibility of archaeological parks often unknown to visitors and the public on a large scale. In this perspective, the Appia Antica Archaeological Park aims to support research in digitising infrastructures and archaeological contexts of high historical and cultural value to plan short- and medium-term preservation and maintenance projects. In this context, unmanned aerial vehicles (UAVs) are tools with enormous potential in survey, inspection and digitisation, providing the basis for the subsequent phases of data interpretation, representation and material analysis. Thanks to the photorealistic reconstruction of dense structure from motion (DSfM) in the application of structural inspections, today it is possible to intercept the geometry and material conditions of small, medium and large structures, reducing the costs of inspections, limiting the interruption of the public and providing professionals and visitors with a better volumetric understanding of the system. However, inserting information that gradually accumulates throughout the process requires advanced 3D digital representation techniques, such as HBIM (historic building information modelling), scan-to-BIM approach and interactive forms, such as virtual and augmented reality (VR-AR). For these reasons, this study summarises the experience and lessons learned from the UAV inspection of three research case studies at archaeological, architectural, and infrastructure scales to increase awareness of the Roman-built heritage. Full article

Wide-speed-range vehicles are characterized by high flight altitude and high speed, with significant changes in the flight environment. Due to the strong uncertainty of its aerodynamic characteristics, higher requirements are imposed on attitude control. In this paper, an adaptive prescribed performance control method based on online aerodynamic identification is proposed, which consists of two parts: an online aerodynamic parameter identification method and an adaptive attitude control method based on the pre-defined parameters of the control system. The aerodynamic parameter identification is divided into offline design and online design. In the offline design, neural networks are used to fit nonlinear aerodynamic characteristics. In the online design, a nonlinear recursive identification method is used to correct the errors of the offline fitted model. The adaptive attitude control is based on the conventional control method and updates the control gain in real time according to the desired system parameters to enhance the robustness of the controller. Finally, the effectiveness of the offline neural network and online discrimination correction is verified by mathematical simulations, and the effectiveness and robustness of the adaptive control proposed in this paper are verified by comparative simulation. Full article

The purpose of this paper is the evaluation of the aero-propulsive effects on a UAV wing model with distributed propulsion. An array of three propellers is placed ahead of the leading edge of a rectangular wing with flap. The investigation was performed with high-fidelity numerical analyses to provide insights into the phenomenology and to screen the interesting positions to be validated in the wind tunnel. The propellers’ array is moved into twelve different positions, allowing longitudinal and vertical translations. The wing has an untwisted and constant section profile, with a single slot trailing-edge flap that is deflected into three positions. The flap span is entirely covered by the propellers’ blowing. Results show an increment of lift, drag, and pitching moment coefficients with distributed propellers enabled. For a given thrust level, the magnitude of such increments depends on the propellers’ positions, the flap configuration, and the angle of attack. The lift enhancement sought in distributed propulsion applications comes at the expense of a significant increase in drag and pitching moment magnitude. In some combinations, the wing’s contribution to the aircraft longitudinal stability is severely affected. Conversely, the propellers’ inflow is altered such that thrust is increased in all the investigated configurations, with a small reduction of propulsive efficiency. Full article

A biplane quadrotor (hybrid vehicle) benefits from rotary-wing and fixed-wing structures. We design a dual observer-based autonomous trajectory tracking controller for the biplane quadrotor. Extended state observer (ESO) is designed for the state estimation, and based on this estimation, a Backstepping controller (BSC), Integral Terminal Sliding Mode Controller (ITSMC), and Hybrid Controller (HC) that is a combination of ITSMC + BSC are designed for the trajectory tracking. Further, a Nonlinear disturbance observer (DO) is designed and combined with ESO based controller to estimate external disturbances. In this simulation study, These ESO-based controllers with and without DO are applied for trajectory tracking, and results are evaluated. An ESO-based Adaptive Backstepping Controller (ABSC) and Adaptive Hybrid controller (AHC) with DO are designed, and performance is evaluated to handle the mass change during the flight despite wind gusts. Simulation results reveal the effectiveness of ESO-based HC with DO compared to ESO-based BSC and ITSMC with DO. Furthermore, an ESO-based AHC with DO is more efficient than an ESO-based ABSC with DO. Full article

The intensity and frequency of bushfires have increased significantly, destroying property and living species in recent years. Presently, unmanned aerial vehicle (UAV) technology advancements are becoming increasingly popular in bushfire management systems because of their fundamental characteristics, such as manoeuvrability, autonomy, ease of deployment, and low cost. UAVs with remote-sensing capabilities are used with artificial intelligence, machine learning, and deep-learning algorithms to detect fire regions, make predictions, make decisions, and optimize fire-monitoring tasks. Moreover, UAVs equipped with various advanced sensors, including LIDAR, visual, infrared (IR), and monocular cameras, have been used to monitor bushfires due to their potential to provide new approaches and research opportunities. This review focuses on the use of UAVs in bushfire management for fire detection, fire prediction, autonomous navigation, obstacle avoidance, and search and rescue to improve the accuracy of fire prediction and minimize their impacts on people and nature. The objective of this paper is to provide valuable information on various UAV-based bushfire management systems and machine-learning approaches to predict and effectively respond to bushfires in inaccessible areas using intelligent autonomous UAVs. This paper aims to assemble information about the use of UAVs in bushfire management and to examine the benefits and limitations of existing techniques of UAVs related to bushfire handling. However, we conclude that, despite the potential benefits of UAVs for bushfire management, there are shortcomings in accuracy, and solutions need to be optimized for effective bushfire management. Full article

Drone deployment in Qatar has been lagging behind that in other countries due to a wide range of reported challenges. This study developed a framework to address these operational gaps and serve as a roadmap for different stakeholders to enable drone applications for successful, safe, accountable and sustainable development. Moreover, the framework could help overcome key challenges and lay the groundwork for addressing other challenges facing UAV deployment in Qatar, thereby enabling Qatar to join the global efforts in this technological evolution. The framework was based on an analysis of the available data from previous guidelines for UAV operation and the identification of the challenges facing drone deployment in Qatar. The proposed framework was evaluated through interviews with key stakeholders in the Qatari drone steering committee. The outcomes from this evaluation supported the implementation of the framework with minor amendments and are ready to be put into practice by policymakers. In addition, it could be helpful for Gulf Cooperation Council (GCC) countries and other countries in the region to consider this framework in their efforts to facilitate drone deployment. Full article

Utilising emerging innovative technologies and systems to improve construction processes in an effort towards digitalisation has been earmarked as critical to delivering resilience and responsive infrastructure. However, successful implementation is hindered by several challenges. Hence, this study evaluates the challenges facing the adoption of unmanned aerial vehicles towards the digitalisation of the built environment. The study adopted a quantitative survey of built environment stakeholders in developed and developing economies. A total of 161 completely filled forms were received after the survey, and the data were analysed using descriptive analysis and inferential statistics. The study’s findings show that there are different barriers experienced between developed and developing countries in the adoption of drones towards digitalising construction processes in the built environment. Moreover, economic/cost-related factors were identified as the most critical barriers to the adoption of drones, followed by technical/regulatory factors and education/organisation-related factors. The findings can assist the built environment in reducing the impact of these barriers and could serve as a policy instrument and helpful guidelines for governmental organisations, stakeholders, and others. Full article

The mining industry has recently shown increased interest in drones for routine activities in underground and surface mines. Designing a drone for coal mines is extremely complicated since the Mine Safety and Health Administration (MSHA) has tight guidelines for any equipment that can be used in underground coal mines. Due to these criteria, designing a drone for underground coal mining is exceedingly difficult. This paper explores the challenges of creating an intrinsically safe drone propulsion system. To address the challenges of designing an intrinsically safe drone’s propulsion system for an underground coal mine, this work aims to investigate the potential approaches to enhance efficiency and mitigate the heat. The study begins with the drone’s sizing approach before moving on to the experimental setup that is utilized to test the drone’s propulsion system. Finally, answers to numerous issues arising during the inquiry are offered, and these solutions are empirically explored. Full article

FVC (fractional vegetation cover) is highly correlated with wheat plant density in the reviving period, which is an important indicator for conducting variable-rate nitrogenous topdressing. In this study, with the objective of improving inversion accuracy of wheat plant density, an innovative approach of retrieval of FVC values from remote sensing images of a UAV (unmanned aerial vehicle) was proposed based on the mixed pixel decomposition method. Firstly, remote sensing images of an experimental wheat field were acquired by using a DJI Mini UAV and endmembers in the image were identified. Subsequently, a linear unmixing model was used to subdivide mixed pixels into components of vegetation and soil, and an abundance map of vegetation was acquired. Based on the abundance map of vegetation, FVC was calculated. Consequently, a linear regression model between the ground truth data of wheat plant density and FVC was established. The coefficient of determination (R²), RMSE (root mean square error), and RRMSE (Relative-RMSE) of the inversion model were calculated as 0.97, 1.86 plants/m², and 0.677%, which indicates strong correlation between the FVC of mixed pixel decomposition method and wheat plant density. Therefore, we can conclude that the mixed pixel decomposition model of the remote sensing image of a UAV significantly improved the inversion accuracy of wheat plant density from FVC values, which provides method support and basic data for variable-rate nitrogenous fertilization in the wheat reviving period in the manner of precision agriculture. Full article

In this paper, the consensus control of unmanned surface vehicles (USVs) is investigated by employing a distributed model predictive control approach. A hierarchical control structure is considered during the controller design, where the upper layer determines the reference signals of USV velocities while the lower layer optimizes the control inputs of each USV. The main feature of this work is that a post-verification procedure is proposed to address the failure states caused by local errors or cyberattacks. Each USV compares the actual state and the predicted one obtained at the previous moment. This allows the estimation of local perturbations. In addition, the failure state of the USV can also be determined if a preset condition is satisfied, thus forcing a change in the communication topology and avoiding further impact. Simulations show that the proposed method is effective in USV formation control. Compared with the method without post-verification, the proposed approach is more robust when failure states occur. Full article

Ultra-reliable and low-latency communications (uRLLC) has received great attention in the study of wireless communication for it can provide high network performance in terms of reliability and latency. However, the reliability requirements of uRLLC require further investigation due to the inherent openness of the wireless channel. Different from the previous reliable contributions that focused on the retransmission mechanism, in this paper, we consider scenarios with the interference of multiple UAVs. We establish an analytical framework of the packet error rate (PER) for an air-to-ground (A2G) channel. In this framework, the cellular users are allocated to different UAVs according to their minimum path loss with the aim of minimizing the PER. Furthermore, a wireless link scheduling algorithm is proposed to enhance the reliability between the UAV and cellular user. Simulated results show that, under the same power and channel block length level, our proposed non-orthogonal multiple access (NOMA) scheduling scheme has the best performance. Full article

To reduce costs, an unmanned swarm usually consists of nodes with high-accuracy navigation sensors (HAN) and nodes with low-accuracy navigation sensors (LAN). Transmitting and fusing the navigation information obtained by HANs enables LANs to improve their positioning accuracy, which in general is called cooperative navigation (CN). In this method, the accuracy of relative observation between platforms in the swarm have dramatic effects on the positioning results. In the popular research, constructing constraints in three-dimensional (3D) frame could only optimize the position and velocity of LANs but neglected the attitude estimation so LANs cannot maintain a high attitude accuracy when utilizing navigation information obtained by sensors installed during maneuvers over long periods. Considering the performance of the inertial measurement unit (IMU) and other common sensors, this paper advances a new method to estimate the attitude of LANs in a swarm. Because the small unmanned nodes are strictly limited by relevant practical engineering problems such as size, weight and power, the method proposed could compensate for the attitude error caused by strapdown gyroscopic drift, which only use visual vectors built by the targets detected by cameras with the function of range finding. In our method, the coordinates of targets are mainly given by the You Only Look Once (YOLO) algorithm, then the visual vectors are built by connecting the targets in the covisibility graph of the nodes in the swarm. The attitude transformation matrices between each camera frame are calculated using the multivector attitude determination algorithm. Finally, we design an information filter (IF) to determine the attitude of LANs based on the observation of HANs. Considering the problem of positioning reference, the field test was conducted in the open air and we chose to use two-wheeled robots and one UAV to carry out the experiment. The results show that the relative attitude error between nodes is less than 4 degrees using the visual vector. After filtering, the attitude divergence of LANs’ installed low precision IMU can be effectively constrained, and the high-precision attitude estimation in an unmanned CN swarm can be realized. Full article

Various types of small drones constitute a modern threat for infrastructure and hardware, as well as for humans; thus, special-purpose radar has been developed in the last years in order to identify such drones. When studying the radar signatures, we observed that the majority of the scientific studies refer to multirotor aerial vehicles; there is a significant gap regarding small, fixed-wing Unmanned Aerial Vehicles (UAVs). Driven by the security principle, we conducted a series of Radar Cross Section (RCS) simulations on the Euclid fixed-wing UAV, which has a wingspan of 2 m and is being developed by our University. The purpose of this study is to partially fill the gap that exists regarding the RCS signatures and identification distances of fixed-wing UAVs of the same wingspan as the Euclid. The software used for the simulations was POFACETS (v.4.1). Two different scenarios were carried out. In scenario A, the RCS of the Euclid fixed-wing UAV, with a 2 m wingspan, was analytically studied. Robin radar systems’ Elvira Anti Drone System is the simulated radar, operating at 8.7 to 9.65 GHz; θ angle is set at 85° for this scenario. Scenario B studies the Euclid RCS within the broader 3 to 16 Ghz spectrum at the same θ = 85° angle. The results indicated that the Euclid UAV presents a mean RCS value $\left(\overline{\sigma }\right)$ of −17.62 dBsm for scenario A, and a mean RCS value $\left(\overline{\sigma }\right)$ of −22.77 dBsm for scenario B. These values are much smaller than the values of a typical commercial quadcopter, such as DJI Inspire 1, which presents −9.75 dBsm and −13.92 dBsm for the same exact scenarios, respectively. As calculated in the study, the Euclid UAV can penetrate up to a distance of 1784 m close to the Elvira Anti Drone System, while the DJI Inspire 1 will be detected at 2768 m. This finding is of great importance, as the obviously larger fixed-wing Euclid UAV will be detected about one kilometer closer to the anti-drone system. Full article

Drone-based surveillance has become widespread due to its flexibility and ability to access hazardous areas, particularly in industrial complexes. As digital camera capabilities improve, more visual information can be stored in high-resolution images, resulting in larger image sizes. Therefore, algorithms for encrypting digital images sent from drones must be both secure and highly efficient. This paper presents a novel algorithm based on DNA computing and a finite state machine (FSM). DNA and FSM are combined to design a key schedule with high flexibility and statistical randomness. The image encryption algorithm is designed to achieve both confusion and diffusion properties simultaneously. The DNA bases themselves provide diffusion, while the random integers extracted from the DNA bases contribute to confusion. The proposed algorithm underwent a thorough set of statistical analyses to demonstrate its security. Experimental findings show that the proposed algorithm can resist many well-known attacks and encrypt large-sized images at a higher throughput compared to other algorithms. High experimental results for the proposed algorithm include correlation coefficients of 0.0001 and Shannon entropy of 7.999. Overall, the proposed image encryption algorithm meets the requirements for use in drone-based surveillance applications. Full article

The deployment of urban air mobility in built-out metropolitan regions is constrained by infrastructure opportunities, land use, and airspace zoning designations. Meanwhile, the availability and spatial distribution of infrastructure opportunities influence the travel demand that can be potentially captured by UAM services. The purpose of this study is to provide an initial assessment of the infrastructure opportunities of UAM in southern California with different mixes of spatial constraints, such as noise levels, school buffer zones, and airspace zones. The corresponding travel demand that can be potentially captured under each scenario is estimated with a home–workplace trip table. The results of the analyses indicate that supply-side infrastructure opportunities, such as heliports and elevated parking structures, are widely available to accommodate the regional deployment of UAM services. However, current spatial constraints can significantly limit the scope of vertiport location choices. Furthermore, the low-income population, blue-collar workers, and young people live farther away from supply-side opportunities than the general population. Moreover, this study proposes a network of UAM based on the top home-based and workplace-based stations for long-distance trips. Full article

Positioning of unoccupied aerial systems (UAS, drones) is predominantly based on Global Navigation Satellite Systems (GNSS). Due to potential signal disruptions, redundant positioning systems are needed for reliable operation. The objective of this study was to implement and assess a redundant positioning system for high flying altitude drone operation based on visual-inertial odometry (VIO). A new sensor suite with stereo cameras and an inertial measurement unit (IMU) was developed, and a state-of-the-art VIO algorithm, VINS-Fusion, was used for localisation. Empirical testing of the system was carried out at flying altitudes of 40–100 m, which cover the common flight altitude range of outdoor drone operations. The performance of various implementations was studied, including stereo-visual-odometry (stereo-VO), monocular-visual-inertial-odometry (mono-VIO) and stereo-visual-inertial-odometry (stereo-VIO). The stereo-VIO provided the best results; the flight altitude of 40–60 m was the most optimal for the stereo baseline of 30 cm. The best positioning accuracy was $2.186$ m for a 800 m-long trajectory. The performance of the stereo-VO degraded with the increasing flight altitude due to the degrading base-to-height ratio. The mono-VIO provided acceptable results, although it did not reach the performance level of the stereo-VIO. This work presented new hardware and research results on localisation algorithms for high flying altitude drones that are of great importance since the use of autonomous drones and beyond visual line-of-sight flying are increasing and will require redundant positioning solutions that compensate for potential disruptions in GNSS positioning. The data collected in this study are published for analysis and further studies. Full article

It is necessary to develop a vehicle digital twin (DT) for urban air mobility (UAM) that uses an accurate, physics-based emulator to model the statics and dynamics of a vehicle. This is because the use of digital twins in the operation and control of UAM vehicles is essential for the UAM operational digital twin infrastructure (UAM-ODT). There are several issues that need to be addressed in this process: (i) the lack of digital twin engines for the digitalization (twinization) of the dynamics and control of UAM vehicles at the core of UAM-ODT systems; (ii) the lack of back-end system engineering in the development of UAM vehicle DTs; and (iii) the lack of fault-tolerant mechanisms for the DT cloud back-end system to run uninterrupted operations 24/7. On the other hand, software aging and rejuvenation are becoming increasingly important in a variety of computing scenarios as the demand for reliable and available services increases. With the increasing use of containerized systems, there is also a need for an orchestrator to support easy management and reduce operational costs. In this paper, an operational digital twin (ODT) of a typical urban air mobility (UAM) infrastructure is developed on a private cloud system based on Kubernetes using a proposed cloud-in-the-loop simulation approach. To ensure the ODT can provide uninterrupted operational control and services in UAM around the clock, we propose a methodology for investigating software aging in Kubernetes-based containerized clouds. We evaluate the behavior of Kubernetes software using the Nginx and K3S tools while they manage pods in an accelerated lifetime experiment. We continuously execute operations for creating and terminating pods, allowing us to observe the utilization of computing resources (e.g., CPU, memory, and I/O), the performance of the Nginx and K3S environments, and the response time of an application hosted in those environments. In some conditions and for specific metrics, such as virtual memory usage, we observed the effects of software aging, including a memory leak that is not fully cleared when the cluster is stopped. These issues could lead to system performance degradation and eventually compromise the reliability and availability of the system when it crashes due to memory space exhaustion or full utilization of swap space on the hard disk. This study helps with the deployment and maintenance of virtualized environments from the standpoint of system dependability in digital twin computing infrastructures where a large number of services are running under strict continuity requirements. Full article