Small-scale Unmanned Aerial Vehicles Research Articles

Develop and Design Small Scale UAV

Nowadays, many people want to buy a small-scale unmanned aerial vehicle (UAV) either for recreational purposes, photography and video editing or for air surveillance. The main issue with conventional small-scale UAVs is that they require experience to operate them. Besides that, the control system also plays important role for its flight stability and endurance for small-scale UAV. The weight of the load and travelling speed also being issued for small-scale UAV. As a result, three objectives were formulated based on the problem statement which are to fabricate the appropriate size of a small-scale UAV in term of its mass and frame size; to design the control system and increase the stability of the small-scale UAV; and to test the flight endurance of the small-scale UAV. This research is conducted through the following methodology, which is designing the quadcopter body mainframe, constructing the circuit diagram, developing the RC transmitter and receiver for Arduino and testing the UAV functionality and flight test. Softwares that were involved in this project are Solidworks, Arduino compiler, Proteus, Processing and Matlab. The equipments that are used are Arduino UNO, MPU-6050 sensor, Li-Po 11.1V battery, Electronic Speed Controller (ESC), brushless DC motor, 2.4 GHZ RC transmitter and 6-channel receiver. The limitation also being found from those experiments. Finally, the results and discussion will show and explain about the early sketch for quadcopter body mainframe including dimension for each part and stress-strain diagram using Solidworks. It also includes the simulation and hardware connection for brushless DC motor and MPU-6050 sensor using Proteus, Arduino compiler, Processing and Matlab software.

Federated learning based on asynchronous and adjusted client training

Machine learning in small-scale unmanned aerial vehicle (UAV) deployment fails to accomplish complex missions of swarm intelligence. Federated learning (FL) tackles it by being able to handle large-scale learning, while keeping data private. The client data distribution involved in FL is often non-independent and identically distributed (Non-IID) and the amount of data is unbalanced. This may seriously affect the model’s convergence speed, and may even lead to nonconvergence. To tackle the above problems, we propose a new asynchronous FL scheme that combines adjusting client training. The scheme determines the weight of aggregation based on the staleness of client model parameters and the amount of data owned by the client, and dynamically adjusts the local training of each client using average training loss. Experiments on public datasets verify the effectiveness of our method, which can alleviate the impact of data Non-IID and data imbalance in asynchronous FL and accelerate the training of FL.

Monopole Antenna for Experimental Flight Applications

A monopole dual band antenna for experimental flight applications on a small scale Unmanned Aerial Vehicle available in the Aero modeling lab of Department of Aeronautical Engineering at Manav Rachna International Institute of Research and Studies is presented in this paper. Radiating patch of proposed antenna consists of concentric circle at resonance frequency of 1.35 GHz and 5.25 GHz. The minimum reflection constant at 1.5 GHz is -20 dB and 5.2 GHz is -36 dB. The bandwidth of 300 MHz and 830 MHz achieved during the design process is considered adequate for efficient and effective utilization of the designed antenna. Radiation pattern of proposed antenna show Omnidirectional pattern. Designed antenna provides solutions for L and C band aviation application for early reported geometry. Various parameters like return loss, gain, radiation pattern surface current and Voltage Standing Wave Ratio (VSWR) have been studied and analyzed using the application software CST Microwave Studio. The experimental antenna designed for experimental flight evaluation can also be used as Global System for Mobile Applications (GSM), Worldwide Interoperability for Microwave Access (WiMAX), Wireless Local Area Network (WLAN), Walkie-Talkie and Air Traffic Control (ATC) antenna. In the overall perspective, the proposed antenna can also be utilized for ground communication and other airport facilities in addition to the experimental role envisaged.

Design, manufacturing and flight-testing of a fixed-wing, small-scale UAV for the transportation of blood bags to remote locations

The current work presents the design, manufacturing, and flight-testing of a fixed-wing, small-scale Unmanned Aerial Vehicle (UAV). The research activities presented in this paper have all been conducted by the Aristotle Space & Aeronautics Team (ASAT), a Greek aerospace student team, for their participation in the international student competition Air Cargo Challenge 2022. Its mission is to carry bags of blood to a remote location. The design requirements refer to a short take-off distance of 60m of rough terrain, climb time of 60s and a cruise/loiter time of 120s. Size restrictions also apply, as the aircraft must fit inside a 1.5m x 1.5m x 0.5m volume, in its deployed form. A step-by-step presentation of the design procedure is conducted, emphasizing on the critical design decisions, such as the airfoil, cargo bay location and wing planform design. The corresponding trade studies are shown, along with the main advantages of the resulting configuration. The in-house manufacturing process is consequently described, focusing on the molds tooling, as well as on the carbon fiber and fiberglass manufacturing techniques. Finally, the results of the flight testing are shown, including the initial challenges that lead to the fine-tuning of the UAV. The test results are validated against the design data and the overall performance of the aerial vehicle is discussed.

Electromagnetic interference modeling and elimination for a solar/hydrogen hybrid powered small-scale UAV

The trajectory related and Direct Current (DC) Electromagnetic Interference (EMI) of lithium battery, fuel cell and photovoltaic modules has a great influence on the small-scale Unmanned Aerial Vehicle (UAV) airborne magnetometer and is hard to be shielded, calibrated or filtered. Besides, the mechanisms underlying the DC EMI have been rarely investigated yet. To cope with this problem, this paper systematically studies the EMI models, and proposes an online 3-layer EMI reduction scheme. First, EMI coupled with UAV motion model and hybrid power system is established. Second, the mechanism EMI models of hybrid power system are established and verified based on the proposed concept “equivalent current”. Third, an online 3-layer EMI reduction scheme is proposed, including battery layer, trajectory planning layer and energy management layer. In the first main layer, EMI self-cancellation is realized by rotating battery inclinations and symmetrical circuits. In response to errors, the trajectory planning layer reduces the EMI intensity by optimizing an optimal trajectory, while the energy management layer prioritizes power allocation to power sources that can produce small and stable EMI. Simulation results of climb, level flight and descent illustrate the efficaciousness and applicability of the proposed online 3-layer EMI reduction scheme.

LightMAN: A Lightweight Microchained Fabric for Assurance- and Resilience-Oriented Urban Air Mobility Networks

Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm have led to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely deployed Internet of drones (IoD) in smart cities has also brought up new concerns regarding performance, security, and privacy. The centralized framework adopted by conventional UAM networks is not adequate to handle high mobility and dynamicity. Moreover, it is necessary to ensure device authentication, data integrity, and privacy preservation in UAM networks. Thanks to its characteristics of decentralization, traceability, and unalterability, blockchain is recognized as a promising technology to enhance security and privacy for UAM networks. In this paper, we introduce LightMAN, a lightweight microchained fabric for data assurance and resilience-oriented UAM networks. LightMAN is tailored for small-scale permissioned UAV networks, in which a microchain acts as a lightweight distributed ledger for security guarantees. Thus, participants are enabled to authenticate drones and verify the genuineness of data that are sent to/from drones without relying on a third-party agency. In addition, a hybrid on-chain and off-chain storage strategy is adopted that not only improves performance (e.g., latency and throughput) but also ensures privacy preservation for sensitive information in UAM networks. A proof-of-concept prototype is implemented and tested on a micro-air–vehicle link (MAVLink) simulator. The experimental evaluation validates the feasibility and effectiveness of the proposed LightMAN solution.

Unmanned Aerial Vehicle Swarm-Enabled Edge Computing: Potentials, Promising Technologies, and Challenges

Unmanned aerial vehicle (UAV) swarm enabled edge computing is envisioned to be promising in the sixth generation wireless communication networks due to their wide application sensories and flexible deployment. However, most of the existing works focus on edge computing enabled by a single or a small scale UAVs, which are very different from UAV swarm-enabled edge computing. In order to facilitate the practical applications of UAV swarm-enabled edge computing, the state of the art research is presented in this article. The potential applications, architectures and implementation considerations are illustrated. Moreover, the promising enabling technologies for UAV swarm-enabled edge computing are discussed. Furthermore, we outline challenges and open issues in order to shed light on the future research directions.

UAVSwarm Dataset: An Unmanned Aerial Vehicle Swarm Dataset for Multiple Object Tracking

In recent years, with the rapid development of unmanned aerial vehicles (UAV) technology and swarm intelligence technology, hundreds of small-scale and low-cost UAV constitute swarms carry out complex combat tasks in the form of ad hoc networks, which brings great threats and challenges to low-altitude airspace defense. Security requirements for low-altitude airspace defense, using visual detection technology to detect and track incoming UAV swarms, is the premise of anti-UAV strategy. Therefore, this study first collected many UAV swarm videos and manually annotated a dataset named UAVSwarm dataset for UAV swarm detection and tracking; thirteen different scenes and more than nineteen types of UAV were recorded, including 12,598 annotated images—the number of UAV in each sequence is 3 to 23. Then, two advanced depth detection models are used as strong benchmarks, namely Faster R-CNN and YOLOX. Finally, two state-of-the-art multi-object tracking (MOT) models, GNMOT and ByteTrack, are used to conduct comprehensive tests and performance verification on the dataset and evaluation metrics. The experimental results show that the dataset has good availability, consistency, and universality. The UAVSwarm dataset can be widely used in training and testing of various UAV detection tasks and UAV swarm MOT tasks.

Problems of Extended State Observer and Proposal of Compensation Function Observer for Unknown Model and Application in UAV

The extended state observer (ESO) used to estimate the unknown model for nonlinear systems has been widely applied in model-free control. In the ESO, three factors: 1) nonderivative structure; 2) insufficient usage of innovation; and 3) no compensation of the unknown character of a nonlinear function, causing low type and low accuracy of observation are identified. The convergence problem of the ESO is also raised. A simple linear compensation function observer (CFO) is proposed. The CFO adopts the derivative form overcoming the structure problem of the ESO, fully uses the available innovation, and then introduces a compensator to replace the unknown function. The filtering compensator counteracts the effect of the time-varying nonlinear unknown model to make the error equation behave with desired poles. The convergence of the CFO is theoretically proved. The CFO is the Type-III system, whereas the ESO is a Type-I system; therefore, the CFO has a qualitative leap over the ESO in accuracy improvement of observation. The CFO can observe nonlinear functions, including lumped disturbances, uncertainties, and unmodeled parts for nonlinear systems. The CFO decouples the multi-input and multioutput (MIMO) systems. Five unknown typical nonlinear functions are tested and demonstrates that the CFO qualitatively prevails over the ESO. The MIMO chaotic attitude system of a small-scale unmanned aerial vehicle (UAV) helicopter is conducted with results that CFO performance of function observation is highly improved over that of ESO about 29 times.

Learning Spatio-Temporal Attention Based Siamese Network for Tracking UAVs in the Wild

The popularity of unmanned aerial vehicles (UAVs) has made anti-UAV technology increasingly urgent. Object tracking, especially in thermal infrared videos, offers a promising solution to counter UAV intrusion. However, troublesome issues such as fast motion and tiny size make tracking infrared drone targets difficult and challenging. This work proposes a simple and effective spatio-temporal attention based Siamese method called SiamSTA, which performs reliable local searching and wide-range re-detection alternatively for robustly tracking drones in the wild. Concretely, SiamSTA builds a two-stage re-detection network to predict the target state using the template of first frame and the prediction results of previous frames. To tackle the challenge of small-scale UAV targets for long-range acquisition, SiamSTA imposes spatial and temporal constraints on generating candidate proposals within local neighborhoods to eliminate interference from background distractors. Complementarily, in case of target lost from local regions due to fast movement, a third stage re-detection module is introduced, which exploits valuable motion cues through a correlation filter based on change detection to re-capture targets from a global view. Finally, a state-aware switching mechanism is adopted to adaptively integrate local searching and global re-detection and take their complementary strengths for robust tracking. Extensive experiments on three anti-UAV datasets nicely demonstrate SiamSTA’s advantage over other competitors. Notably, SiamSTA is the foundation of the 1st-place winning entry in the 2nd Anti-UAV Challenge.

Design and Implementation of Sensor Platform for UAV-Based Target Tracking and Obstacle Avoidance

Small-scale unmanned aerial vehicles are being deployed in urban areas for missions such as ground target tracking, crime scene monitoring, and traffic management. Aerial vehicles deployed in such cluttered environments are required to have robust autonomous navigation with both target tracking and obstacle avoidance capabilities. To this end, this work presents a simple-to-design but effective steerable sensor platform and its implementation techniques for both obstacle avoidance and target tracking. The proposed platform is a 2-axis gimbal system capable of roll and pitch/yaw. The mathematical model that governs the dynamics of this platform is developed. The performance of the platform is validated through a software-in-the-loop simulation. The simulation results show that the platform can be effectively steered to all regions of interest except backward. With its design layout and mount location, the platform can engage sensors for obstacle avoidance and target tracking as per requirements. Moreover, steering the platform in any direction does not induce aerodynamic instability on the unmanned aerial vehicle in mission.

Computational Fluid Dynamics Study of Wing in Air Flow and Air–Solid Flow Using Three Different Meshing Techniques and Comparison with Experimental Results in Wind Tunnel

The main purpose of this work is to simulate the flow of air and solid particles over a wildfire and to investigate the single and multiphase flow over the surface of a custom-designed wing with an Eppler-420 airfoil including an appendant custom-designed blended winglet. The wing is the result of a conceptual and preliminary design of a small-scale unmanned aerial vehicle (UAV) designed to assist in firefighting. The fire embers will be simulated in the Ansys Fluent commercial code as solid particles injected in the continuous phase, in an Euler–Lagrange approach. Primarily studied were the response of the model in air and air–solid flows, as well as the impact on aerodynamic efficiency due to the existence of the second phase. Moreover, the effects of unstructured, structured and mosaic poly-hexcore meshes are investigated and compared. The computational fluid dynamics (CFD) simulations, were implemented using a pressure-based solver, spatial discretization was conducted with a second-order upwind scheme, and the k-omega SST (k-ω SST) turbulence model was applied. Meanwhile, the two-phase flow was simulated using the Discrete Phase Model with reflect boundary condition on the surface of the wing and two-way coupling between continuous and discrete phase. To validate the results, experiments were conducted in a subsonic wind tunnel using a 3D printed model of the wing. The results show good agreement between simulations and experiments, with the structured mesh coming closer to reality, followed by the mosaic and unstructured meshes, respectively. Finally, a reduction in the aerodynamic efficiency of the wing section is observed, due to the presence of solid particles.

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

In recent years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video surveillance applications, such as vehicle tracking, border control, dangerous object detection, and many others. Anomaly detection can represent a prerequisite of many of these applications thanks to its ability to identify areas and/or objects of interest without knowing them a priori. In this paper, a One-Class Support Vector Machine (OC-SVM) anomaly detector based on customized Haralick textural features for aerial video surveillance at low-altitude is presented. The use of a One-Class SVM, which is notoriously a lightweight and fast classifier, enables the implementation of real-time systems even when these are embedded in low-computational small-scale UAVs. At the same time, the use of textural features allows a vision-based system to detect micro and macro structures of an analyzed surface, thus allowing the identification of small and large anomalies, respectively. The latter aspect plays a key role in aerial video surveillance at low-altitude, i.e., 6 to 15 m, where the detection of common items, e.g., cars, is as important as the detection of little and undefined objects, e.g., Improvised Explosive Devices (IEDs). Experiments obtained on the UAV Mosaicking and Change Detection (UMCD) dataset show the effectiveness of the proposed system in terms of accuracy, precision, recall, and F1-score, where the model achieves a 100% precision, i.e., never misses an anomaly, but at the expense of a reasonable trade-off in its recall, which still manages to reach up to a 71.23% score. Moreover, when compared to classical Haralick textural features, the model obtains significantly higher performances, i.e., ≈20% on all metrics, further demonstrating the approach effectiveness.

A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images.

Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.

Comparison of UAV and fixed-wing aerial application for alfalfa insect pest control: evaluating efficacy, residues, and spray quality.

Integrating unmanned aerial vehicles (UAV) as a new method of pesticide application into existing commercial crop protection systems requires extensive research and comparison to conventional, proven application technology. Pest control expressed as efficacy against target pests, and spray quality expressed as coverage and chemical residue are three key criteria. We investigated and compared these quantitative parameters between a multi-rotor UAV and conventional piloted airplanes in two commercial alfalfa production systems. Effective and equivalent control of leaf-feeding insect pests was achieved by both methods of aerial application when delivering chlorantraniliprole at the same labeled use rate in different spray volumes (46.8 and 93.5L ha-1 ) on commercially grown alfalfa in California. Residue levels and spray coverage were also comparable and consistent between the UAV and airplane applications across three sampling techniques, specifically residue levels on alfalfa, insecticide recovery from filter paper, and spray coverage on water sensitive cards. Differences in droplet size and deposit characteristics were more variable for the UAV than airplanes based on analysis of deposition images. The results of this study provide confidence supporting the use of small-scale multi-rotor UAVs for pesticide application on agricultural crops. According to the parameters tested, UAV application quality and crop protection performance were comparable to that of the conventional fixed wing airplane application. However, the droplet spectrum and the short-term fate of droplets from unmanned aerial spray system require further optimization for effective and efficient crop protection with minimal risk to the environment.

Covert-inspired flaps for lift enhancement and stall mitigation

Even though unmanned aerial vehicles (UAVs) are taking on more expansive roles in military and commercial applications, their adaptability and agility are still inferior to that of their biological counterparts like birds, especially at low and moderate Reynolds numbers. A system of aeroelastic devices used by birds, known as the covert feathers, has been considered as a natural flow-control device for mitigating flow separation, enhancing lift, and delaying stall. This study presents the effects of a covert-inspired flap on two airfoils with different stall characteristics at Reynolds numbers in the order of 105, where small scale UAVs operate. Detailed experiments and simulations are used to investigate how the covert-inspired flap affects lift and drag on an airfoil that exhibits sharp or sudden stall (i.e. the NACA 2414 airfoil) and one that exhibits soft or gradual stall (i.e. an E387(A) airfoil). The effects of the flap chord-wise locations and deflection angles on lift and drag is investigated, through wind tunnel experiments, for two types of flaps namely, a freely-moving flap and a static flap. Results show that the static covert-inspired flap can delay stall by up to 5° and improve post-stall lift by up to 23%. However, the post-stall lift improvement characteristics and sensitivities are highly affected by the airfoil choice. For the soft stall airfoil (i.e. E387(A)), the stall onset delay is insensitive to changing the flap deflection angle, and the flap becomes ineffective when the flap location is changed. In contrast, for the sharp stall airfoil (i.e. NACA 2414), the post-stall lift improvements can be tuned using the flap deflection angle, and the flap remains effective over a wide range of chord-wise locations. Numerical studies reveal that the lift improvements are attributed to a step in the pressure distribution over the airfoil, which allows for lower pressures on the suction side upstream of the flap. The distinctions between the flap-induced lift enhancements on the soft and sharp stall airfoils suggest that the flap can be used as a tunable flow control device for the sharp stall airfoil, while for the soft stall airfoil, it can solely be used as a stall mitigation device that is either on or off.

Chaos control of small-scale UAV helicopter based on high order differential feedback controller

ABSTRACT The small-scale unmanned aerial vehicle (UAV) helicopter has a complex structure, strong nonlinearity, coupling and open-loop instability, making it shake violently and even produce chaotic oscillation under wind disturbances and improper configuration. The chaotic angular velocity dynamic model of the UAV helicopter is analysed via two cases. The sliding mode controller (SMC) relying on the linearised model, the PID controller, and the high order differential feedback controller (HODFC) not relying on the model are designed. The closed-loop system stability under both SMC and HODFC are analysed. The HODFC with a control filtering can actively reject the disturbance and uncertainty. The control performance of HODFC is superior to SMC and PID controllers. The SMC and HODFC can stabilise the chaotic oscillations of UAV helicopter’s angular velocity, whereas the PID controller fails, and the HODFC control effect is superior to the SMC for disturbance rejection, uncertainty and linearisation bias in steady-state errors.

Automatic estimation of optimal UAV flight parameters for real-time wide areas monitoring

In the last few years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video-based monitoring applications, such as search and rescue (SAR) operations, border control, precision agriculture, and many others. Usually, during these missions, a human operator manually selects UAV flight parameters according to the specific monitoring application to be performed. Anyway, regardless a particular mission, some main tasks can be considered common preprocessing steps that require to be accomplished. These tasks include the mosaicking of areas of interest, the detection of changes over time on these areas, finally, the classification of what is present on the ground. The success of these tasks strictly depends on flight and video sensor parameters. In this paper, for the first time in the literature, a method to automatically estimate the optimal parameters, in particular altitude and frame rate, to accomplish the three main tasks reported above is presented and tested. The parameters are estimated according to several factors, including size of the target to be analysed, cruise speed of UAVs, and main internal parameters of the video sensor, i.e., focal length, field of view, and size of the pixel. The full effectiveness of the proposed method, on the three case studies (i.e., main tasks), was proven both by synthetic videos generated with the Aerial Informatics and Robotics Simulation (AirSim) and by real video sequences reported in the UAV Mosaicking and Change Detection (UMCD) and NPU Drone-Map datasets.

Design, Aerodynamic Calculation and Manufacturing using CFRP and Additive Manufacturing of a small Tilt-Rotor UAV for Railway Inspection

The present paper aims to develop a small scale Unmanned Aerial Vehicle for railway inspection. The development process and structure manufacturing aimed at a low complexity technological process both to meet market requirements and to be economically efficient. To define the architectural structure and its components in the design process, a NACA 0024 aerodynamic profile was selected out of two proposed airfoils. The novelty of this concept is the double benefit of the wing, which serves as a cargo bay while uses a low-cost manufacturing technology, incorporating the 3D printing process. Further, a simplified preliminary aerodynamic calculation was performed, the model was manufactured using composite materials reinforced with carbon fibre while other key components have been obtained using the 3D printing technology. In the end, the manufacturing process was a success and the unmanned aerial vehicle was designed and manufactured specifically that its flight envelope meets the requirements of the mission.

A UAV Video Dataset for Mosaicking and Change Detection From Low-Altitude Flights

In recent years, the technology of small-scale unmanned aerial vehicles (UAVs) has steadily improved in terms of flight time, automatic control, and image acquisition. This has lead to the development of several applications for low-altitude tasks, such as vehicle tracking, person identification, and object recognition. These applications often require to stitch together several video frames to get a comprehensive view of large areas (mosaicking), or to detect differences between images or mosaics acquired at different times (change detection). However, the datasets used to test mosaicking and change detection algorithms are typically acquired at high-altitudes, thus ignoring the specific challenges of low-altitude scenarios. The purpose of this paper is to fill this gap by providing the UAV mosaicking and change detection dataset. It consists of 50 challenging aerial video sequences acquired at low-altitude in different environments with and without the presence of vehicles, persons, and objects, plus metadata and telemetry. In addition, this paper provides some performance metrics to evaluate both the quality of the obtained mosaics and the correctness of the detected changes. Finally, the results achieved by two baseline algorithms, one for mosaicking and one for detection, are presented. The aim is to provide a shared performance reference that can be used for comparison with future algorithms that will be tested on the dataset.