Types Of Unmanned Aerial Vehicles Research Articles

Penentuan Lintasan Pergerakan Quadcopter Berbasis GPS (Global Positioning System)

UNMANNED AERIAL VEHICLE (UAV) merupakan sebuah kendaraan udara tanpa awak yang dapat dikendalikan. Terdapat dua tipe UAV, yakni fixed wing dan rotary wing. Quadcopter menjadi salah satu tipe UAV rotary wing yang banyak digunakan dalam berbagai kebutuhan, seperti eksplorasi dan pengambilan citra. Pada penelitian ini Quadcopter berfungsi sebagai kendaraan yang harus bergerak mengikuti lintasan, dimana lintasan yang dikuti oleh Quadcopter berasal dari GPS yang dihasilkan oleh objek yang diikuti (Modul Utama). Tipe GPS yang terpasang pada Quadcopter (GPS1) maupun pada Modul Utama (GPS2) adalah GPS Ublox NEO. Prinsip kerja sistem adalah quadcopter mengikuti Koordinat-koordinat lintasan yang dihasilkan oleh GPS1, di mana data-data lintasan GPS1 dikirim ke Quadcopter menggunakan media Bluetooth. Dalam pergerakannya, Quadcopter akan terus-menerus membandingkan data-data koordinat yang dihasikan posisi Quadcopter dengan data-data koordinat lintasan yang sudah diterima. Pengujian pada Receiver GPS Modul Utama (GPS1) dan Receiver GPS Quadcoter (GPS2), kedua GPS mampu mendapatkan data GPS dari satelit. Kesalahan/perbedaan data dari GPS1 dan GPS2 pada pengujian pergerakkan Quadcopter untuk mengikuti Modul Utama sebagai titik tujuan sebesar 53% pada garis lintang dan 51% pada garis bujur.

Utilizing drone technology in the civil engineering

Abstract An unmanned aerial vehicle (UAVs), also known as drone technology, is used for different types of application in the civil engineering. Drones as a tools that increase communication between construction participants, improves site safety, uses topographic measurements of large areas, with using principles of aerial photogrammetry is possible to create buildings aerial surveying, bridges, roads, highways, saves project time and costs, etc. The use of UAVs in the civil engineering can brings many benefits; creating real-time aerial images from the building objects, overviews reveal assets and challenges, as well as the broad lay of the land, operators can share the imaging with personnel on site, in headquarters and with sub-contractors, planners can meet virtually to discuss project timing, equipment needs and challenges presented by the terrain. The aim of this contribution is to create a general overview of the use of UAVs in the civil engineering. The contribution also contains types of UAVs used for construction purposes, their advantages and also disadvantages.

Developing of the smart quadcopter with improved flight dynamics and stability

The cause of the rapid growth and enhancement in the dynamics of unmanned aerial vehicles (UAVs) is due to its vast utilization in every-day application. The major advantage of UAVs is no risk of human life with secure and suitable surveillance. The UAV facilitates in live video streaming and wide aerial coverage for monitoring. In this project, a type of UAV named quadcopter has been developed. The work mainly consists of design of the quadcopter frame, interfacing of the brushless DC motors and bluetooth module with the microcontroller, and adjustment of the roll, pitch, and yaw for keeping the smooth flight dynamics by flight controller board containing MEMS sensors, i.e., gyroscope, accelerometer, magnetometer, and pressure sensors. The repeated simulation and testing has been carried out in MATLAB for the mathematical modeling of the dynamics of the system, i.e., Euler method for solving differential equation for finding system states, computation of the rotation matrix R and functions to convert from an angular velocity vector w to the derivatives of roll, pitch, and yaw. The improvement in the parameters of the flight dynamics and removing the errors to gain stability in the frame are the main core issues which have been achieved by the several iterations. A closed-loop control system, i.e., PID controller, has been simulated and carefully designed for stabilizing the actual angle from the sensors and desired angle from the pilot. The reduced error rate of 0.05 degrees after every 10 s was achieved.

Implementation and optimization of the cascade classifier algorithm for UAV detection and tracking

A number of vision-based algorithms designed to detect and track unmanned aerial vehicles (UAVs) from on board a second UAV have been researched, implemented, and experimentally validated over the last decade. However, the successful methods have tended to rely on characteristics such as color or shape, meaning they require the target UAV to have particular markings or geometries. This paper uses the Viola–Jones cascade classifier, a computer vision algorithm originally designed to detect human faces in video streams, and demonstrates its capability for detecting and tracking an arbitrary type of UAV with excellent performance in either indoor or outdoor environments and with a variety of backgrounds. The Viola–Jones algorithm is applied to two specific quadrotor UAV models, the Solo from 3D Robotics and the AR.Drone 2.0 from Parrot. Experimental testing demonstrates that the resulting system achieves very good detection and tracking performance in real time on each UAV type for both indoor and outdoor flight tests.

A Review on UAV-Based Applications for Precision Agriculture

Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

Beehive-Inspired Information Gathering with a Swarm of Autonomous Drones.

This paper presents a beehive-inspired multi-agent drone system for autonomous information collection to support the needs of first responders and emergency teams. The proposed system is designed to be simple, cost-efficient, yet robust and scalable at the same time. It includes several unmanned aerial vehicles (UAVs) that can be tasked with data collection, and a single control station that acts as a data accumulation and visualization unit. The system also provides a local communication access point for the UAVs to exchange information and coordinate the data collection routes. By avoiding peer-to-peer communication and using proactive collision avoidance and path-planning, the payload weight and per-drone costs can be significantly reduced; the whole concept can be implemented using inexpensive off-the-shelf components. Moreover, the proposed concept can be used with different sensors and types of UAVs. As such, it is suited for local-area operations, but also for large-scale information-gathering scenarios. The paper outlines the details of the system hardware and software design, and discusses experimental results for collecting image information with a set of 4 multirotor UAVs at a small experimental area. The obtained results validate the concept and demonstrate robustness and scalability of the system.

Interference modeling and analysis in 3-dimensional directional UAV networks based on stochastic geometry

Abstract In this paper, we model and characterize interference of directional unmanned aerial vehicle (UAV) networks based on stochastic geometry, where each UAV is equipped with a directional antenna and it communicates with another UAV that is located in the three dimensional (3D) space. In particular, the 3D location of UAVs is assumed to be uniformly distributed in a certain volume, which is modeled by Poisson point process. Given a beamwidth, we first design an ideal 3D directional antenna beam pattern with a constant gain of both main-lobe and side-lobe. To model the interference in the UAV network, we analyze the effect of elevation and azimuth between a typical UAV receiver and an interfering UAV transmitter with spherical coordinate system. Then, we investigate distribution of the aggregate interference at a typical UAV receiver from multiple UAVs in terms of side-lobe gain, beamwidth, height of UAV, and distance of a UAV transmitter–receiver pair.

Postural Stabilization of Quadrotor using Extended Kalman Filter and Integral Sliding Mode Control

In this paper, we consider the stabilization and the estimate of quadrotor’s posture. Nowadays, as the interest in unmanned aerial vehicle (UAV) has increased, various application fields using UAV have appeared. The quadrotor is the most common type of UAV and many approaches to control the system have been studied. In real control environment, the system of quadrotor is affected by disturbance and measurement noise. Minimizing the effects of disturbance or measurement noise is important part. We propose the extended kalman filter (EKF) to reduce effectively the measurement noise of the nonlinear drones system and the integral sliding mode control (ISMC), a robust controller for the model uncertainty and disturbance. We show the proposed control performance through some simulations.

UTSim: A framework and simulator for UAV air traffic integration, control, and communication

The interest in unmanned systems especially unmanned aerial vehicle is continuously increasing. Unmanned aerial vehicles started to become of great benefit in many different fields. It is anticipated that unmanned aerial vehicles will soon become a main component of the future urban air traffic. The integration of unmanned aerial vehicles within existing air traffic environments has started getting the attention of researchers. Integrating unmanned systems in the real-world urban air traffic requires the development of tools and simulators to enable researchers in their ongoing efforts. In this article, a simulator called UTSim is introduced. The proposed simulator is built using the Unity platform. UTSim is capable of simulating unmanned aerial vehicle physical specification, navigation, control, communication, sensing and avoidance in environments with static and moving objects. The simulator enables studying and exploring several unmanned aerial vehicle air traffic integration issues like sense and avoid, communication protocols, navigation algorithms, and much more. UTSim is designed and developed to be easily used. The user can specify the properties of the environment, the number and types of unmanned aerial vehicles in the environment, and specify the algorithm to be used for path planning and collision avoidance. The simulator outputs a log file with a lot of useful information such as the number of sent and received messages, the number of detected objects and collided unmanned aerial vehicles. Three scenarios have been implemented in this article to present the capabilities of UTSim and to illustrate how it can benefit researchers in the field of integrating unmanned aerial vehicles in urban air traffic.

Evaluation of Radar Cross Section in Aerial Operations by using POFACETS for a standard Unmanned Aerial Vehicle (UAV)

In military and common applications, radar assumes an exceptionally noteworthy job in exploring the vehicle, recognizing for flying machines, ships and so on. Radar cross section is a measure of a target's ability to reflect radar signals in the direction of the receiver. It is obvious that RCS depends on the shape of the reflecting objects. In this paper, a brief overview of various radars used in surveillance applications and radar cross section (RCS) measurement techniques are presented. The objective of this paper is to illustrate methods for obtaining the Radar Cross Section (RCS) of a typical Unmanned Aerial Vehicle (UAV) for various frequencies in radar bands. UAV technologies have advanced tremendously and are being developed successfully. These features lead UAV to patrol even in civil airspace for civilian applications. These objects operate with small radar cross-section and/or on low altitude which imposes security threat. Keeping this view the RCS of a UAV which are flying in civilian airspace has to be determined drastically for security purposes. Several shapes are considered for the estimation of RCS. The results on the variations of RCS for the above objects as a function of frequency, aspect angle are presented. The POFACETS plays a crucial role and provides the user an easy–to–use GUI that allows the input of all necessary parameters, while preventing erroneous data input and other user errors. In this paper the RCS of sphere, ellipsoid and UAV are determined.

Adaptive Filtration of Parameters of the UAV Movement Based on the TDOA-Measurement Sensor Networks

In modern conditions unmanned aerial vehicles (UAVs) generate new classes of threats, including their use for terrorist purposes. A feature of modern UAVs is the ability to perform sudden maneuvers and to keep the same position in the point in space. For the description of the UAV movement with various types of maneuver it is used a rectangular coordinate system. We use the model in the form of stochastic dynamic system with random structure in the discrete time in which the change type UAV movement occurs at random times. When a UAV emits a sign, its location can be determined by wireless sensor networks (WSN) using the TDOA method. On the basis of a mathematical apparatus of the mixed Markov processes for in discrete time optimal and quasi-optimal adaptive algorithms for filtration of UAV movement parameters based on the TDOA-measurement, sensor networks are synthesized. Devices that realize these algorithms are multichannel and belong to the class of devices with feedback between channels. At the same time, in a quasi-optimal algorithm, a sequential procedure of the arriving measurements from sensors of a sensor network is realized, which allows to avoid the inversion of large-dimensional matrices. An analysis of the quasi-optimal adaptive algorithm is performed using statistical modeling. On the intervals of hovering and of the UAV movements without maneuver, the developed algorithm allows to increase significantly the accuracy of the estimation of the UAV coordinates, and also to recognize various types of its movement with high probability level.

Testing Procedure of Unmanned Aerial Vehicles (UAVs) Trajectory in Automatic Missions

This paper describes an experimental test campaign while using an Unmanned Aerial Vehicle (UAV) and measuring the obtained UAV positions during different flight tasks and in different operative conditions. A new test procedure has been presented and tested for different devices in various weather conditions. This paper describes and analyses the measurements of the flight trajectory of the UAV that was performed with the use of a robotic total station (RTS), as compared to the design data and the data recorded in the internal memory of the UAV. Five different test tasks have been conducted. The obtained results have allowed for the assessment of the correctness of task performance as compared to the design and to determine the flying accuracy of the entire UAV set. The proposed set of tasks can be successfully utilised to control the correctness of operation of various types of UAVs and it may be implemented as a universal test to verify the algorithms optimising take-offs and landings, test flights of the objects, as well as flight planning in various terrain and weather conditions, which will increase the safety of the flights while using UAVs.

Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

Personal assistant robots provide novel technological solutions in order to monitor people’s activities, helping them in their daily lives. In this sense, unmanned aerial vehicles (UAVs) can also bring forward a present and future model of assistant robots. To develop aerial assistants, it is necessary to address the issue of autonomous navigation based on visual cues. Indeed, navigating autonomously is still a challenge in which computer vision technologies tend to play an outstanding role. Thus, the design of vision systems and algorithms for autonomous UAV navigation and flight control has become a prominent research field in the last few years. In this paper, a systematic mapping study is carried out in order to obtain a general view of this subject. The study provides an extensive analysis of papers that address computer vision as regards the following autonomous UAV vision-based tasks: (1) navigation, (2) control, (3) tracking or guidance, and (4) sense-and-avoid. The works considered in the mapping study—a total of 144 papers from an initial set of 2081—have been classified under the four categories above. Moreover, type of UAV, features of the vision systems employed and validation procedures are also analyzed. The results obtained make it possible to draw conclusions about the research focuses, which UAV platforms are mostly used in each category, which vision systems are most frequently employed, and which types of tests are usually performed to validate the proposed solutions. The results of this systematic mapping study demonstrate the scientific community’s growing interest in the development of vision-based solutions for autonomous UAVs. Moreover, they will make it possible to study the feasibility and characteristics of future UAVs taking the role of personal assistants.

H∞ Control-Based Robust CAS Design for QTW-UAV via the Multiple-Model Approach with Particle Swarm Optimization

Quad-Tilt-Wing (QTW) Unmanned Aerial Vehicle (UAV) is one of the promising types of UAVs because of its high-speed cruise performance similar to fixed-wing aircraft and VTOL (Vertical TakeOff and Landing) ability like helicopters. The control performance of our previously designed Control Augmentation System (CAS) for the aircraft was not satisfactory due to the oscillatory motions in flight tests. This paper thus presents an H∞ control-based robust CAS design for QTW-UAV via multiple-model approach with Particle Swarm Optimization (PSO) to suppress the oscillatory motions. Although the adoption of the multiple-model approach to obtain robust CAS gains is the same as in our previous design, our new method has two unique features in contrast to the previously used method, that is, the design requirement for CAS gains is given in the frequency domain to shape the frequency responses from attitude command to attitude error and PSO is used to reduce the numerical complexity coming from a brute-force method, i.e., the gridding method. The overall control performance of the designed CAS gains is examined by human-in-the-loop nonlinear flight simulations. As an extension of the proposed method, we consider the situation in which uncertainty models with different probabilistic densities should be incorporated into the nominal model and show that the nominal performance can be improved at the expense of slight performance degradation for the models with small probabilistic density.

Efficient Energy Supply Using Mobile Charger for Solar-Powered Wireless Sensor Networks.

An energy-harvesting wireless sensor network mitigates the energy shortage problems of existing battery-based wireless sensors; however, its hotspot area sensor nodes still experience 3 blackouts, thereby reducing network connectivity. Techniques that transfer energy directly to sensor nodes using wireless power transfer (WPT) have been studied in recent years to address this issue. In this paper, we propose a technique that uses a drone (quadcopter), which is a type of unmanned aerial vehicle (UAV), as a mobile sink. The drone selects and manages anchor nodes that aggregate data temporarily, collects data by visiting the anchor nodes to mitigate the hotspot issue, and then prevents blackouts by supplying energy to low-energy nodes, thereby improving network connectivity. The anchor nodes are carefully selected after considering the energy capacity of the drone, the size of the network, the amount of collected data, and the energy consumed by the nodes to increase the network’s energy efficiency. Furthermore, energy is transferred from the drone to the anchor nodes to support their energy consumption. In our study, this method reduced the blackouts of sensor nodes, including anchor nodes, in hotspot regions, and increased network connectivity, thereby improving the amount of data gathered by the mobile sink.

Investigation on Effects of HPM Pulse on UAV's Datalink

Unmanned aerial vehicle (UAV) is likely to be interfered by high-power microwave (HPM) pulse, especially its datalink. To investigate the effects of HPM pulse on the datalink of a UAV in a dynamic flight scenario, a tracking interference model for an HPM source toward a UAV is proposed, as well a dynamic flight model is also built to determine the HPM reception of the UAV's datalink. In order to predict the possible effects of HPM on the datalink, the tests on a certain type of UAV's datalink are carried out in the presence of HPM with its carrier frequency in L -, S - and C -band, respectively. The results show that the electromagnetic interference (EMI) can easily enter the receiver via front-door when the carrier frequency of the HPM falls into the operating frequency band of the datalink, which will lead to a permanent physical damage. However, when the carrier frequency deviates from the operating frequency band of the datalink, the EMI couples into the incomplete shielded power line via back-door and generates a high-voltage signal at the power port, resulting in a transient lost-link effect or even a functional failure of the datalink due to its abnormal electric power supply. Finally, some suggestions are put forward to improve the UAV's adaptability in the HPM environment.

Improvement of unmanned aerial vehicles detection method by spectral analysis of acoustic signals

The creation of algorithms for the detection of unmanned aerial vehicles (UAVs) based on the analysis of acoustic signals is an actual problem of the present. To solve such problem, it is necessary to correctly take into account the conditions for the propagation of acoustic signals in the atmosphere, as well as the characteristics of UAV flight, the height of which can vary up to several kilometers, and the speed can reach 200 km/h.The purpose of this study is to improve the UAV detection method by the results of spectral analysis of their acoustic signal. The investigations of the spectrum of acoustic signals from various types of UAV showed the presence of several characteristic harmonics in it. The criterion for a reliable detection of UAV is finding at least three characteristic harmonics in a received acoustic signal. The input values for this algorithm are samples of the input acoustic signal passing through the bandpass filter. To represent the signal in the frequency domain, the method of spectral estimation based on the periodogram without a window function has been selected.In this work the improved method and the complex layout for detection of unmanned aerial vehicles (UAV) based on the results of spectral analysis of acoustic signals are proposed. To implement the proposed algorithm, a specialized software and mathematical support for detecting an acoustic signal from the UAV were created. They allow with high reliability to detect an acoustic signal from the UAV at distances of about 1 kilometer. The efficiency of the proposed algorithm is verified using the layout of the acoustic detection complex in real conditions. Unmanned aerial systems "Fury" and "Leleka 100" were used in the experiment.Thus, it is shown that the acoustic signal created by UAV can be represented as the sum of harmonic components. The criterion for a reliable detection of UAV is finding at least three characteristic harmonics in a received acoustic signal.

INVESTIGATION OF DETECTION POSSIBILITY OF UAVS USING LOW COST MARINE RADAR

The technologies of Unmanned Aerial Vehicles (UAVs) are fast emerging, but as any other technology, development of UAVs provides not only benefits but also the threats. UAV technologies are developing much faster than means of their control and detection. RADAR technology is one of the means of UAV’s detection. Usually, radars are expensive, and usage of high-power radiation is problematic in many cases. Today’s market provides low cost marine radar working on various principles of operation. Such radar are not optimal, but could be used for UAV detection. Detection possibility of UAVs by FMCW marine radar was investigated by using two types of small UAVs as targets.

A UAV classification system based on FMCW radar micro-Doppler signature analysis

Due to its small size, slow flying speed, and low flying altitude, classification of mini-sized unmanned aerial vehicles (UAVs) using a frequency-modulated continuous wave (FMCW) surveillance radar is a challenging task. This is because the FMCW radar echo signals are acquired at a short dwell time and thus contain limited information about targets. In this paper, we first analyze FMCW radar returns from various types of UAVs and non-UAV objects in terms of the micro-Doppler signature (m-DS) pattern. Based on the analysis results, we propose an effective and efficient UAV classification system using FMCW radar echo signals. The proposed system consists of five main parts namely, (i) burst selection, (ii) rule-based scan pruning, (iii) the empirical mode decomposition based m-DS analysis and features extraction, (iv) error counting minimization based class label estimation, and (v) scan-to-scan filtering. Our experimental results on physically measured FMCW radar echo signals from several types of UAVs and non-UAV objects show that the proposed system consistently outperforms a commercial-off-the-shelf UAV classification system in terms of the classification accuracy.

Penalaan Mandiri Full State Feedback dengan LQR dan JST Pada Kendali Quadrotor

Quadrotor is one type of unmanned aerial vehicle that has the ability to vertical takeoff and landing. In this research, a system designed to stabilize quadrotor during flight condition by maintaining at angle of roll, pitch, yaw, and x, y, and z axis position using LQR full state feedback with artificial neural network (ANN).The LQR full state feedback method uses 12 states with each K constant being tuned with ANN. This research implements ANN method to change feedback constant at angle of roll, pitch, and yaw and x, y, and z axis. The artificial neural network method uses 12 input layers, 12 hidden layers, and 1 output layer.Testing with ANN improved the rise time to ± 2.18 seconds at the roll angle, ± 1.23 seconds at the pitch angle, and ± 0.31 seconds at the yaw angle. Improved settling time value up to ± 2.41 seconds at roll angle, ± 1.23 seconds at pitch angle, and ± 1.07 seconds at yaw angle. Improved steady state eror value of ± 0.61% at roll angle, ± 4.88% at pitch angle, and ± 0.82% at the yaw angle.