Search Results (20)

The accurate estimation of flow angles is crucial for enhancing flight performance and aircraft safety. Flow angles of fixed-wing small unmanned aerial vehicles (SUAVs) are more vulnerable due to their low airspeed. Current flow angle measurement devices have not been widely implemented in SUAVs due to their substantial cost and size constraints. Moreover, there are no general estimation methods suitable for SUAVs based on their rudimentary sensor suite. This study presents a generalized optimization-assisted filter estimation (OAFE) method for estimating the relative velocity and flow angles of fixed-wing SUAVs based on a standard sensor suite. This OAFE method mainly consists of a cubature Kalman filter and an optimizer. The filter serves as the main loop with which to generate flow angles in real time by fusing the acceleration, angular rate, attitude, and airspeed. Without flow angle measurements, the optimizer generates approximate aerodynamic derivatives, which serve as pseudo-measurements with which to refine the performance of the filter. The results demonstrate that the estimated angle of attack and side slip angle displayed root mean square errors of around 0.11° and 0.24° in the simulation. The feasibility was also verified in field tests. The OAFE method does not require flow angle measurements, the prior acquisition of aerodynamic parameters, or model training, making it suitable for quick deployment on different SUAVs. Full article

This paper introduces a drone-based surrogate project aimed at serving as a preliminary educational platform for undergraduate students in the Electrical and Computer Engineering (ECE) fields. Utilizing small Unmanned Aerial Vehicles (sUAVs), this project serves as a surrogate for the inspection of wind turbines using scaled-down pedestal fans to replace actual turbines. This approach significantly reduces the costs, risks, and logistical complexities, enabling feasible and safe on-campus experiments. Through this project, students engage in hands-on applications of Python programming, computer vision, and machine learning algorithms to detect and classify simulated defects in pedestal fan blade (PFB) images. The primary educational objectives are to equip students with foundational skills in autonomous systems and data analysis, critical for their progression to larger scale projects involving professional drones and actual wind turbines in wind farm settings. This surrogate setup not only provides practical experience in a controlled learning environment, but also prepares students for real-world challenges in renewable energy technologies, emphasizing the transition from theoretical knowledge to practical skills. Full article

This paper presents experimental investigations on aerodynamic performance of a ducted coaxial-rotor system to evaluate its potential application as a small unmanned aerial vehicle (SUAV). Aimed at determining the influence of design parameters (rotor spacing, tip clearance and rotor position within the duct) on hover performance, a variety of systematic measurements for several correlative configurations (single/coaxial rotor with or without a duct) in terms of thrust and torque, as well as power, were conducted in an attempt to identify a better aerodynamic configuration. The experimental results for the coaxial-rotor system indicated that varying rotor spacing affected the thrust-sharing proportion between the two rotors, but this had no significant effect on the propulsive efficiency. The optimal H/R ratio was identified as being 0.40, due to a larger thrust and stronger stability in the case of identical rotation speeds. As for the ducted single-rotor configuration, the tip clearance played a dominant role in improving its thrust performance, especially for smaller gaps ($\delta \le 0.015\mathrm{R}$), while the rotor position made subordinate contributions. The maximum performance was obtained with the rotor located at the P₅ position (0.31C_d from the duct lip), which resulted in an enhancement of approximately 20% in power loading over the isolated single rotor. When the coaxial rotors were surrounded within the duct, the system thrust for a given power degraded with the increasing rotor spacing, which was mainly attributed to the upper rotor suffering from heavier leakage losses. And hence, the ducted coaxial-rotor system with S₁ spacing had the best propulsion efficiency and hover performance with a figure of merit of 0.61. Full article

Earthquakes–induced landslides generally provide abundant loose materials at hillslopes, possibly triggering morphological reshaping processes at river channels and riverbeds during the large flash flood hydrograph and bringing huge risk downstream. Therefore, in a Wenchuan earthquake-affected catchment, the collected hydro-meteorological data and high-precision small Unmanned Aerial Vehicle (sUAV) data were used to quantitatively analyze channel evolution by a large flash flood event on 25 and 26 June 2018. It was found that the stable riverbed structure formed by the armour layer appeared in the tenth year after the Wenchuan earthquake. In a confined channel, the layer can protect the channel and resist the drastic change after the flash flood event with only a small bed elevation from 0.2 m to 2 m. Without the protection of the armour, the change could reach 6 m in the unconfined channel. Meanwhile, more materials with a deposition volume of about 7450 m³ from tributaries were generally taken to the main channel, and more intense erosion with a volume of 10⁵ m³ mostly occurred downstream of tributaries. It was noted that, in the cross-section, the increased channel width could lead to a significant change with the large volume of 35 m³. Additionally, a conceptual diagram of the generalized channel response to large flash floods was provided during multi-stage periods after the Wenchuan earthquake. It determined the rebalance processes of channel evolution in the tenth year after the earthquake. This study will contribute to understanding the post-earthquake long-term channel evolutions and could provide decision-makers of assessing the mitigation strategies for higher-magnitude flood disasters triggered by channel change in earthquake-affected watersheds. Full article

The utilization of small unmanned aerial vehicles (SUAVs), commonly known as drones, has increased drastically in various industries in the past decade. Commercial drones face challenges in terms of safety, durability, flight performance, and environmental effects such as the risk of collision and damage. Biomimetics, which is inspired by the sophisticated flying mechanisms in aerial animals, characterized by robustness and intelligence in aerodynamic performance, flight stability, and low environmental impact, may provide feasible solutions and innovativeness to drone design. In this paper, we review the recent advances in biomimetic approaches for drone development. The studies were extracted from several databases and we categorized the challenges by their purposes—namely, flight stability, flight efficiency, collision avoidance, damage mitigation, and grasping during flight. Furthermore, for each category, we summarized the achievements of current biomimetic systems and then identified their limitations. We also discuss future tasks on the research and development associated with biomimetic drones in terms of innovative design, flight control technologies, and biodiversity conservation. This paper can be used to explore new possibilities for developing biomimetic drones in industry and as a reference for necessary policy making. Full article

Profiling wind information when using a small unmanned aerial vehicle (sUAV) is vital for atmospheric profiling and monitoring attitude during flight. Wind speed on an sUAV can be measured directly using ultrasonic anemometers or by calculating its attitude control information. The former method requires a relatively large payload for an onboard ultrasonic anemometer, while the latter requires real-time flight log data access, which depends on the UAV manufacturers. This study proposes the feasibility of a small thermal anemometer to measure wind speeds inexpensively using a small commercial quadcopter (DJI Mavic2: M2). A laboratory experiment demonstrated that the horizontal wind speed bias increased linearly with ascending sUAV speed. A smoke experiment during hovering revealed the downward wind bias (1.2 m s${}^{-1}$) at a 12-cm height above the M2 body. Field experiments in the ice-covered ocean demonstrated that the corrected wind speed agreed closely with the shipboard wind data observed by a calibrated ultrasonic anemometer. A dual-mount system comprising thermal anemometers was proposed to measure wind speed and direction. Full article

The purpose of this study is to fill the gap that exists when applying the airfoil selection methodology according to the textbooks that appear in the above featured application section, in the low Reynolds number segment, by providing useful data. Data acquisition software was XFLR5. The major result is the construction of a prototype maximum lift coefficient versus ideal lift coefficient diagram, or $\left(C_{l_{max}}-C_{l_i}\right)$ diagram, composed exclusively of low Reynolds number airfoils. In addition, the necessary supplementary airfoil characteristics’ tables are provided, for conducting fast airfoil selection for Small Unmanned Aerial Vehicles (SUAVs). As a conclusion by implementing the proposed methodology, the SUAV designer is disengaged from the time-consuming process of the construction of similar $C_{l_{max}}-C_{l_i}$ diagrams and supplementary characteristic tables and the airfoil selection-processing time can be greatly shortened, because the main work of the process is reflected by the current findings. To express the time gain in a percentage manner, authors estimate that 85% of engineering time will be economized in the overall airfoil selection procedure if the current findings are used, due to the fact that no new airfoil simulations are required. Finally, candidate SUAV designers are encouraged to expand the airfoil database, according to the proposed methodology. Full article

The energy efficiency and flight endurance of small unmanned aerial vehicles (SUAVs) can be improved through the implementation of autonomous soaring strategies. Biologically inspired flight techniques such as dynamic and thermal soaring offer significant energy savings through the exploitation of naturally occurring wind phenomena for thrustless flight. Recent interest in the application of artificial intelligence algorithms for autonomous soaring has been motivated by the pursuit of instilling generalized behavior in control systems, centered around the use of neural networks. However, the topology of such networks is usually predetermined, restricting the search space of potential solutions, while often resulting in complex neural networks that can pose implementation challenges for the limited hardware onboard small-scale autonomous vehicles. In exploring a novel method of generating neurocontrollers, this paper presents a neural network-based soaring strategy to extend flight times and advance the potential operational capability of SUAVs. In this study, the Neuroevolution of Augmenting Topologies (NEAT) algorithm is used to train efficient and effective neurocontrollers that can control a simulated aircraft along sustained dynamic and thermal soaring trajectories. The proposed approach evolves interpretable neural networks in a way that preserves simplicity while maximizing performance without requiring extensive training datasets. As a result, the combined trajectory planning and aircraft control strategy is suitable for real-time implementation on SUAV platforms. Full article

Presently, a significant part of the world population does not have Internet access. The fifth-generation cellular network technology evolution (5G) is focused on reducing latency, increasing the available bandwidth, and enhancing network performance. However, researchers and companies have not invested enough effort into the deployment of the Internet in remote/rural/undeveloped areas for different techno-economic reasons. This article presents the result of a collaboration between Brazil and the European Union, introducing the steps designed to create a fully operational experimentation scenario with the main purpose of integrating the different achievements of the H2020 5G-RANGE project so that they can be trialed together into a 5G networking use case. The scenario encompasses (i) a novel radio access network that targets a bandwidth of 100 Mb/s in a cell radius of 50 km, and (ii) a network of Small Unmanned Aerial Vehicles (SUAV). This set of SUAVs is NFV-enabled, on top of which Virtual Network Functions (VNF) can be automatically deployed to support occasional network communications beyond the boundaries of the 5G-RANGE radio cells. The whole deployment implies the use of a virtual private overlay network enabling the preliminary validation of the scenario components from their respective remote locations, and simplifying their subsequent integration into a single local demonstrator, the configuration of the required GRE/IPSec tunnels, the integration of the new 5G-RANGE physical, MAC and network layer components and the overall validation with voice and data services. Full article

This paper investigates optimal power management of a fuel cell hybrid small unmanned aerial vehicle (sUAV) from the perspective of endurance (time of flight) maximization in a stochastic environment. Stochastic drift counteraction optimal control is exploited to obtain an optimal policy for power management that coordinates the operation of the fuel cell and battery to maximize the expected flight time while accounting for the limits on the rate of change of fuel cell power output and the orientation dependence of fuel cell efficiency. The proposed power management strategy accounts for known statistics in transitions of propeller power and climb angle during the mission, but does not require the exact preview of their time histories. The optimal control policy is generated offline using value iterations implemented in Cython, demonstrating an order of magnitude speedup as compared to MATLAB. It is also shown that the value iterations can be further sped up using a discount factor, but at the cost of decreased performance. Simulation results for a $1.5$ kg sUAV are reported that illustrate the optimal coordination between the fuel cell and the battery during aircraft maneuvers, including a turnpike in the battery state of charge ($SOC$) trajectory. As the fuel cell is not able to support fast changes in power output, the optimal policy is shown to charge the battery to the turnpike value if starting from a low initial $SOC$ value. If starting from a high $SOC$ value, the battery energy is used till a turnpike value of the $SOC$ is reached with further discharge delayed to later in the flight. For the specific scenarios and simulated sUAV parameters considered, the results indicate the capability of up to $2.7$ h of flight time. Full article

5G communications have become an enabler for the creation of new and more complex networking scenarios, bringing together different vertical ecosystems. Such behavior has been fostered by the network function virtualization (NFV) concept, where the orchestration and virtualization capabilities allow the possibility of dynamically supplying network resources according to its needs. Nevertheless, the integration and performance of heterogeneous network environments, each one supported by a different provider, and with specific characteristics and requirements, in a single NFV framework is not straightforward. In this work we propose an NFV-based framework capable of supporting the flexible, cost-effective deployment of vertical services, through the integration of two distinguished mobile environments and their networks: small sized unmanned aerial vehicles (SUAVs), supporting a flying ad hoc network (FANET) and vehicles, promoting a vehicular ad hoc network (VANET). In this context, a use case involving the public safety vertical will be used as an illustrative example to showcase the potential of this framework. This work also includes the technical implementation details of the framework proposed, allowing to analyse and discuss the delays on the network services deployment process. The results show that the deployment times can be significantly reduced through a distributed VNF configuration function based on the publish–subscribe model. Full article

Bathymetric mapping is an important tool for reservoir management, typically completed before reservoir construction. Historically, bathymetric maps were produced by interpolating between points measured at a relatively large spacing throughout a reservoir, typically on the order of a few, up to 10, meters or more depending on the size of the reservoir. These measurements were made using traditional survey methods before the reservoir was filled, or using sonar surveys after filling. Post-construction issues such as sedimentation and erosion can change a reservoir, but generating updated bathymetric maps is difficult as the areas of interest are typically in the sediment deltas and other difficult-to-access areas that are often above water or exposed for part of the year. We present a method to create complete reservoir bathymetric maps, including areas above the water line, using small unmanned aerial vehicle (sUAV) photogrammetry combined with multi-beam sonar data—both established methods for producing topographic models. This is a unique problem because the shoreline topographic models generated by the photogrammetry are long and thin, not an optimal geometry for model creation, and most images contain water, which causes issues with image-matching algorithms. This paper presents methods to create accurate above-water shoreline models using images from sUAVs, processed using a commercial software package and a method to accurately knit sonar and Structure from Motion (SfM) data sets by matching slopes. The models generated by both approaches are point clouds, which consist of points representing the ground surface in three-dimensional space. Generating models from sUAV-captured images requires ground control points (GCPs), i.e., points with a known location, to anchor model creation. For this study, we explored issues with ground control spacing, masking water regions (or omitting water regions) in the images, using no GCPs, and incorrectly tagging a GCP. To quantify the effect these issues had on model accuracy, we computed the difference between generated clouds and a reference point cloud to determine the point cloud error. We found that the time required to place GCPs was significantly more than the time required to capture images, so optimizing GCP density is important. To generate long, thin shoreline models, we found that GCPs with a ~1.5-km (~1-mile) spacing along a shoreline are sufficient to generate useful data. This spacing resulted in an average error of 5.5 cm compared to a reference cloud that was generated using ~0.5-km (~1/4-mile) GCP spacing. We found that we needed to mask water and areas related to distant regions and sky in images used for model creation. This is because water, objects in the far oblique distance, and sky confuse the algorithms that match points among images. If we did not mask the images, the resulting models had errors of more than 20 m. Our sonar point clouds, while self-consistent, were not accurately georeferenced, which is typical for most reservoir surveys. We demonstrate a method using cross-sections of the transition between the above-water clouds and sonar clouds to geo-locate the sonar data and accurately knit the two data sets. Shore line topography models (long and thin) and integration of sonar and drone data is a niche area that leverages current advances in data collection and processing. Our work will help researchers and practitioners use these advances to generate accurate post-construction reservoir bathometry maps to assist with reservoir management. Full article

To study the aerodynamic performance of hovering octorotor small unmanned aerial vehicles (SUAV) with different rotor spacing, the computational fluid dynamics (CFD) method is applied to analyze the flow field of an octorotor SUAV in detail. In addition, an experimental platform is built to measure the thrust and power of the rotors with rotor spacing ratios L/D of 1.0, 1.2, 1.4, 1.6, and 1.8, sequentially. According to the theory of momentum, rotor aerodynamic performance is obtained with qualitative analysis. Further analysis with numerical simulation is presented with the flow field of the octorotor SUAV, the vorticity distribution, velocity distribution, pressure distribution, and streamline. The results show that the aerodynamic performance varies with the rotor spacing. Specifically, the aerodynamic performance is poor at L/D = 1.0, which is accompanied with strong interaction of wake and tip vortexes and interaction with each other. However, the aerodynamic efficiency is much improved with a larger rotor spacing, especially achieving the highest at L/D = 1.8, which is considered to be the best rotor spacing ratio for this kind of octorotor SUAV. Full article

This study presents the first accuracy assessment of a low cost small unmanned aerial vehicle (sUAV) in reconstructing three dimensional (3D) models of traffic accidents at extreme operating environments. To date, previous studies have focused on the feasibility of adopting sUAVs in traffic accidents photogrammetry applications as well as the accuracy at normal operating conditions. In this study, 3D models of simulated accident scenes were reconstructed using a low-cost sUAV and cloud-based photogrammetry platform. Several experiments were carried out to evaluate the measurements accuracy at different flight altitudes during high temperature, low light, scattered rain and dusty high wind environments. Quantitative analyses are presented to highlight the precision range of the reconstructed traffic accident 3D model. Reported results range from highly accurate to fairly accurate represented by the root mean squared error (RMSE) range between 0.97 and 4.66 and a mean percentage absolute error (MAPE) between 1.03% and 20.2% at normal and extreme operating conditions, respectively. The findings offer an insight into the robustness and generalizability of UAV-based photogrammetry method for traffic accidents at extreme environments. Full article

In this paper, we identify the main challenges and problems related with the management and orchestration of Virtualized Network Functions (VNFs) over aerial networks built with Small Unmanned Aerial Vehicles (SUAVs). Our analysis starts from a reference scenario, where several SUAVs are deployed over a delimited geographic area, and provide a mobile cloud environment that supports the deployment of functions and services using Network Functions Virtualization (NFV) technologies. After analyzing the main challenges to NFV orchestration in this reference scenario from a theoretical perspective, we undertake the study of one specific but relevant aspect following a practical perspective, i.e., the limitations of existing transport-layer solutions to support the dissemination of NFV management and orchestration information in the considered scenario. While in traditional cloud computing environments this traffic is delivered using TCP, our simulation results suggest that using this protocol over an aerial network of SUAVs presents certain limitations. Finally, based on the lessons learned from our practical analysis, the paper outlines different alternatives that could be followed to address these challenges. Full article