Quadcopter Unmanned Aerial Vehicle Research Articles

Forward flight tests of a quadcopter unmanned aerial vehicle with various spherical body diameters

This paper presents experimental results on the relation between forward airspeed, pitch angle, and power consumption of a quadcopter unmanned aerial vehicle. The quadcopter consists of an interchangeable spherical body, four cylindrical arms, and small propellers mounted at 1 m diagonal distance to minimize interference between body and propellers. This simple geometry facilitates results reproduction and comparison with simulation. Two different takeoff masses and four diameters of spherical bodies are tested for their steady-state speed and power for pitch angles up to [Formula: see text]. The steady-state horizontal flight is recorded with on-board sensors at the end of flying long straight lines at a constant pitch angle in wind-still conditions. The best effective lift-to-drag ratio increases for smaller bodies and occurs at higher speeds for increasing mass. Results show that the equivalent frontal surface stays constant for pitch angles further than [Formula: see text] up to the maximum recorded [Formula: see text] and increases linearly with the frontal surface of the body.

Dynamic Simulation of Tilted Rotor Quadcopter

Application of quadcopter unmanned aerial vehicle (UAV) has already become common today. As the name suggests, quadcopter has four rotors and these rotors can be classified into two types: fixed and tilted rotors. The maneuvering of quadcopter using the fixed rotor is achieved through the thrust differential. On contrary, a tilted rotor can be rotated or tilted and the resultant thrust component will move the quadcopter longitudinally. In this study, the dynamic behavior of HMF SL300 tiltrotor quadcopter is analyzed through simulation. The study is limited to longitudinal behavior of the HMF SL300 tiltrotor quadcopter in z- and x-axis. A Simulink model has been developed based on the longitudinal dynamic model of the quadcopter and acceleration, velocity and displacement of the quadcopter in z- and x-axis when the tilt angle is varied are presented. Based on the findings from the simulation, it can be concluded that the higher the tilt angle, the higher the rate of motion in x-axis but the lower the rate of motion in z-axis.

A Passive Fault-Tolerant Switched Control Approach for Linear Multivariable Systems: Application to a Quadcopter Unmanned Aerial Vehicle Model

Abstract This paper proposes synthesis algorithms for the design of passive state- and output-feedback fault-tolerant controllers. Sufficient conditions for the existence and the construction of such fault-tolerant controllers are given in terms of linear matrix inequalities (LMIs) which can be solved efficiently. The state-feedback fault-tolerant controller consists of a family of state-feedback gains switched appropriately according to a stabilizing switching signal so that the closed-loop system satisfies a performance requirement expressed in terms of system L2 norm. Similarly, the output feedback controller consists of a family of full-order linear, time-invariant controllers switched according to a stabilizing signal that depends only on the controller states. Both approaches are passive in the sense that they do not rely on the detection and/or the estimation of the faults. The proposed approaches are tested on a nonlinear model of a quadcopter. Simulation results show that satisfactory stability, tracking, and disturbance rejection are maintained despite of time-varying actuator faults.

Active Fault-Tolerant Control of a Quadcopter against Time-Varying Actuator Faults and Saturations Using Sliding Mode Backstepping Approach

Fault-tolerant control is becoming an interesting topic because of its reliability and safety. This paper reports an active fault-tolerant control method for a quadcopter unmanned aerial vehicle (UAV) to handle actuator faults, disturbances, and input constraints. A robust fault diagnosis based on the H ∞ scheme was designed to estimate the magnitude of a time-varying fault in the presence of disturbances with unknown upper bounds. Once the fault estimation was complete, a fault-tolerant control scheme was proposed for the attitude system, using adaptive sliding mode backstepping control to accommodate the actuator faults, despite actuator saturation limitation and disturbances. The Lyapunov theory was applied to prove the robustness and stability of the closed-loop system under faulty operation. Simulation results show the effectiveness of the fault diagnosis scheme and proposed controller for handling actuator faults.

Attitude estimation for collision recovery of a quadcopter unmanned aerial vehicle

An extensive evaluation of attitude estimation algorithms in simulation and experiments is performed to determine their suitability for a collision recovery pipeline of a quadcopter unmanned aerial vehicle. A multiplicative extended Kalman filter (MEKF), unscented Kalman filter (UKF), complementary filter, filter, and novel adaptive varieties of the selected filters are compared. The experimental quadcopter uses a PixHawk flight controller, and the algorithms are implemented using data from only the PixHawk inertial measurement unit (IMU). Performance of the aforementioned filters is first evaluated in a simulation environment using modified sensor models to capture the effects of collision on inertial measurements. Simulation results help define the efficacy and use cases of the conventional and novel algorithms in a quadcopter collision scenario. An analogous evaluation is then conducted by post-processing logged sensor data from collision flight tests, to gain new insights into algorithms’ performance in the transition from simulated to real data. The post-processing evaluation compares each algorithm’s attitude estimate, including the stock attitude estimator of the PixHawk controller, to data collected by an offboard infrared motion capture system. Based on this evaluation, two promising algorithms, the MEKF and an adaptive filter, are selected for implementation on the physical quadcopter in the control loop of the collision recovery pipeline. Experimental results show an improvement in the metric used to evaluate experimental performance, the time taken to recover from the collision, when compared with the stock attitude estimator on the PixHawk (PX4) software.

Design, Implementation, and Evaluation of a Neural-Network-Based Quadcopter UAV System

In this paper, a quadcopter unmanned aerial vehicle (UAV) system based on neural-network enhanced dynamic inversion control is proposed for multiple real-world application scenarios. A sigma-pi neural network (SPNN) is used as the compensator to reduce the model error and improve the system performance in the presence of the uncertainties of UAV dynamics, payload, and environment. Besides, we present a technical framework for fast and robust implementation of multipurpose UAV systems and develop a testbed for the evaluation of UAV control system by using a high-precision optical motion capture system. Both simulation results and experiment results demonstrate that the SPNN can reduce the inversion errors related to UAV parameter uncertainties as well as tracking errors related to unknown disturbances and unmodeled dynamics. With the help of an online neural network (NN) learning mechanism, the entire system can achieve much higher accuracy in attitude and trajectory control than that achieved by conventional proportional-integral derivative based control systems under varying flight conditions.

Parametric Study of a Unmanned Aerial Vehicle Ingestion Into a Business Jet Size Fan Assembly Model

This work investigated the damage severity of an unmanned aerial vehicle (UAV) ingestion into fan models of a midsized business jet engine. The ingestion of the quadcopter UAV model into the fan was carried out in ls-dyna. The material models used for the quadcopter and the fan were previously validated, and the fan's durability was simulated through simulated bird ingestions. The results of this work show that UAVs will cause significantly more damage than birds due mostly to the hard components typically used in motors, batteries, and cameras. Particular parameters of the ingestion studied include the phase of flight of the plane, impact location and orientation, and fan blade thickness.

A view from above: Unmanned aerial vehicles (UAVs) provide a new tool for assessing liana infestation in tropical forest canopies

Abstract Tropical forests store and sequester large quantities of carbon, mitigating climate change. Lianas (woody vines) are important tropical forest components, most conspicuous in the canopy. Lianas reduce forest carbon uptake and their recent increase may, therefore, limit forest carbon storage with global consequences for climate change. Liana infestation of tree crowns is traditionally assessed from the ground, which is labour intensive and difficult, particularly for upper canopy layers. We used a lightweight unmanned aerial vehicle (UAV) to assess liana infestation of tree canopies from above. It was a commercially available quadcopter UAV with an integrated, standard three‐waveband camera to collect aerial image data for 150 ha of tropical forest canopy. By visually interpreting the images, we assessed the degree of liana infestation for 14.15 ha of forest for which ground‐based estimates were collected simultaneously. We compared the UAV liana infestation estimates with those from the ground to determine the validity, strengths, and weaknesses of using UAVs as a new method for assessing liana infestation of tree canopies. Estimates of liana infestation from the UAV correlated strongly with ground‐based surveys at individual tree and plot level, and across multiple forest types and spatial resolutions, improving liana infestation assessment for upper canopy layers. Importantly, UAV‐based surveys, including the image collection, processing, and visual interpretation, were considerably faster and more cost‐efficient than ground‐based surveys. Synthesis and applications. Unmanned aerial vehicle (UAV) image data of tree canopies can be easily captured and used to assess liana infestation at least as accurately as traditional ground data. This novel method promotes reproducibility of results and quality control, and enables additional variables to be derived from the image data. It is more cost‐effective, time‐efficient and covers larger geographical extents than traditional ground surveys, enabling more comprehensive monitoring of changes in liana infestation over space and time. This is important for assessing liana impacts on the global carbon balance, and particularly useful for forest management where knowledge of the location and change in liana infestation can be used for tailored, targeted, and effective management of tropical forests for enhanced carbon sequestration (e.g., REDD+ projects), timber concessions, and forest restoration.

Sliding Mode Thau Observer for Actuator Fault Diagnosis of Quadcopter UAVs

Fault diagnosis (FD) is one of the main roles of fault-tolerant control (FTC) systems. An FD should not only identify the presence of a fault, but also quantify its magnitude and location. In this work, we present a robust fault diagnosis method for quadcopter unmanned aerial vehicle (UAV) actuator faults. The state equation of the quadcopter UAV is examined as a nonlinear system. An adaptive sliding mode Thau observer (ASMTO) method is proposed to estimate the fault magnitude through an adaptive algorithm. We then obtain the design matrices and parameters using the linear matrix inequalities (LMI) technique. Finally, experimental results are presented to show the advantages of the proposed algorithm. Unlike previous research on quadcopter UAV FD systems, our study is based on ASMTO and can, therefore, determine the time variability of a fault in the presence of external disturbances.

Robust Hybrid Nonlinear Control Systems for the Dynamics of a Quadcopter Drone

Robustness in the face of uncertainties is an important aspect in designing high performance control systems. This paper addresses the problem of accurate trajectory tracking of a small quadcopter unmanned aerial vehicle in the face of uncertainties. Accommodating the worst -case scenario, we propose a hybrid feedback and feedforward autopilot that has the capability to eliminate the cross-coupling disturbance between the lateral and the longitudinal loops with respect to the vertical loop as well as external disturbances (e.g., wind gusts). The proposed control system leverages on the technical benefits of both the nonlinear model predictive control and the fuzzy feedforward compensator. We highlight the efficacy of our hybrid autopilot system with respect to the performance of the conventional PD control systems through rigorous comparative studies. We also present stability analysis of our hybrid control system.

Quaternion Modeling and Observer-based Torque Compensation of an Aerial Manipulator

This paper proposes a simple solution regarding the stabilization of a quadcopter unmanned aerial vehicle endowed with a manipulator arm. The manipulator robot is attached below the rotors plane and this one induces torques producing stability issues. The present study deals with the stabilization of the full system (quadcopter and arm) by means of a set of nonlinear control techniques. First, a mathematical model is proposed for the system. Then, an attitude control, consisting on a bounded quaternion-based feedback allows the quadcopter attitude stabilization while compensating adverse torques from manipulator’s motion. A simple-to-implement strategy is proposed to estimate the actual torque for compensation purposes. Then, the formulation of a nonlinear control, which drives the aerial vehicle to a desired position is presented. Both controls consist on saturation functions. Simulation results validate the proposed control strategy and compare the results with different manipulator torque estimations.

Structure from Motion (SfM) Photogrammetry with Drone Data: A Low Cost Method for Monitoring Greenhouse Gas Emissions from Forests in Developing Countries

Structure from Motion (SfM) photogrammetry applied to photographs captured from Unmanned Aerial Vehicle (UAV) platforms is increasingly being utilised for a wide range of applications including structural characterisation of forests. The aim of this study was to undertake a first evaluation of whether SfM from UAVs has potential as a low cost method for forest monitoring within developing countries in the context of Reducing Emissions from Deforestation and forest Degradation (REDD+). The project evaluated SfM horizontal and vertical accuracy for measuring the height of individual trees. Aerial image data were collected for two test sites; Meshaw (Devon, UK) and Dryden (Scotland, UK) using a Quest QPOD fixed wing UAV and DJI Phantom 2 quadcopter UAV, respectively. Comparisons were made between SfM and airborne LiDAR point clouds and surface models at the Meshaw site, while at Dryden, SfM tree heights were compared to ground measured tree heights. Results obtained showed a strong correlation between SfM and LiDAR digital surface models (R2 = 0.89) and canopy height models (R2 = 0.75). However, at Dryden, a poor correlation was observed between SfM tree heights and ground measured heights (R2 = 0.19). The poor results at Dryden were explained by the fact that the forest plot had a closed canopy structure such that SfM failed to generate enough below-canopy ground points. Finally, an evaluation of UAV surveying methods was also undertaken to determine their usefulness and cost-effectiveness for plot-level forest monitoring. The study concluded that although SfM from UAVs performs poorly in closed canopies, it can still provide a low cost solution in those developing countries where forests have sparse canopy cover (<50%) with individual tree crowns and ground surfaces well-captured by SfM photogrammetry. Since more than half of the forest covered areas of the world have canopy cover <50%, we can conclude that SfM has enormous potential for forest mapping in developing countries.

Rahbin: A quadcopter unmanned aerial vehicle based on a systematic image processing approach toward an automated asphalt pavement inspection

Automatic inspection of pavement cracking is a critical issue in pavement management systems. In this study, a quadcopter-based digital imaging system is introduced for collecting pavement surface data over a distressed area for visual conditions interpretation. An aerial evaluation was carried out using a quadcopter unmanned aerial vehicle (QUAV), which is a device equipped with a set of automatic systems. Since QUAV flies autonomously and has high maneuverability, it is potentially useful in a variety of conditions particularly the positions dangerous for surveillance and reconnaissance. The main purpose of this work is to design a multi-stage system for QUAV image analysis consisting of image processing, threshold selection, and classification stages. The images are transformed into a new domain; then, an adaptive thresholding is applied to build the pattern of transformed cracks; and finally, the polar support vector machine (the PSVM) is applied for interpretation of crack distress. The PSVM is an automation procedure based on the support vector machine (SVM) classifier defined in the polar coordinate frame. A Mixture of Wavelet modulus and three-dimensional polar Radon transform (3DPRT) are used for feature generation. We show that the PSVM method can be successfully applied to classify the crack and is capable of providing new features about cracking distress, threshold selection and classification. In order to show the applicability and efficiency of the proposed system and method, a test was conducted applying a variety of pavement distresses. The experimental results demonstrate that the applied system provides reliable output. In addition, the comparison of the derived information with the on-site manual quantifications revealed the potentiality of the QUAV and multi-stage system for future practice.

State Space System Modeling of a Quad Copter UAV

In this paper, a linear mathematical model for a quad copter unmanned aerial vehicle (UAV) is derived. The three degrees of freedom (3DOF) and six degrees of freedom (6DOF) quad copter state-space models are developed starting from basic Newtonian equations. These state space models are very important to control the quad copter system which is inherently dynamically unstable.

Design and Development of Optimal Control System for Quad Copter UAV

Background/Objectives: Design and development of an optimal control system for a quadcopter unmanned aerial vehicle (UAV). Methods/Statistical Analysis: The 6DOF quad copter state-space models was used for Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) simulations in MATLAB/Simulink. The simulations produced satisfactory results, which have been presented. Findings: A comparison between Low Pass Filter (LPF) and Kalman filter is also shown which shows that LQR is useless in presence of noise hence LQG was employed in such a situation. Application/Improvements: The optimal control system for quadcopter was successfully developed, which can be practically implemented on an actual quadcopter for stable unmanned flight of the aerial vehicle.

Thermal infrared imaging of geothermal environments and by an unmanned aerial vehicle (UAV): A case study of the Wairakei – Tauhara geothermal field, Taupo, New Zealand

Recent advances in unmanned aerial vehicles (UAVs) for civilian use make it possible to regularly monitor geothermal environments at spatial and temporal scales that would be difficult to achieve using conventional methods. Previous aerial monitoring of geothermal environments has been expensive and time consuming. This paper demonstrates the use of a small (<2 kg), cost effective quadcopter UAV to safely and accurately map physical and biological characteristics of these unique habitats. Thermal infrared imaging and photogrammetry are used to capture detailed information of geothermal surface features and surrounding vegetation within the Wairakei – Tauhara geothermal field near Taupo, New Zealand. The study highlights advanced techniques in sampling, processing and analysing UAV images and identifies some research challenges and limitations in the use of UAV platforms and sensors. The application of UAVs to describe and monitor geothermal features and other environments is a rapidly developing field in science and natural resource management. This project demonstrates the utility of UAV applications in geothermal science and the potential for their use in many other areas of research.

Altitude Controller Design for Quadcopter UAV

This paper discusses on attitude control of a quadcopter unmanned aerial vehicle (UAV) in real time application. Newton-Euler equation is used to derive the model of system and the model characteristic is analyzed. The paper describes the controller design method for the hovering control of UAV automatic vertical take-off system. In order to take-off the quadcopter and stable the altitude, PID controller has been designed. The scope of study is to develop an altitude controller of the vertical take-off as realistic as possible. The quadcopter flight system has nonlinear characteristics. A simulation is conducted to test and analyze the control performance of the quadcopter model. The simulation was conducted by using Mat-lab Simulink. On the other hand, for the real time application, the PCI-1711 data acquisition card is used as an interface for controller design which routes from Simulink to hardware. This study showed the controller designs are implemented and tuned to the real system using Real Time Windows Target approach by Mat-Lab Simulink.

State Space System Modeling of a Quad Copter UAV

Background/Objectives: To derive a linear mathematical modeling of a quad-copter Unmanned Aerial Vehicle (UAV). Methods/Statistical Analysis: The quad-copter physical system with different inputs and outputs has been represented in a state-space form. representation is a mathematical model of a physical system as a set of input, output and state variables related by first-order differential equations. State space is the which has state variables as its axes. Vectors are used within state to show the state of the system. Findings: The six Degrees Of Freedom (6DOF) quad copter state-space model is developed starting from basic Newtonian equations, which is very important to control the quad copter system. The system of quadcopter aerial vehicle is inherently dynamically unstable, which means that this study has found out the state-space modeling. Therefore, this study has enabled the successful modelling of the quadcopter system, which directly contributes to the stable flight of the unmanned aerial vehicle. Application/Improvements: Unmanned aerial reconnaissance, autonomous aerial flight.

Multi-rotor unmanned aerial vehicles (UVAs) and high-resolution compact digital cameras: a promising new method for monitoring changes to surface karst resources

In the course of doctoral research, the authors required a quick and accurate means of documenting the real-time state of surface karst features at a variety of scales in remote and challenging field conditions. The main difficulty was finding an aerial platform that was 1) consistently effective; 2) versatile; and 3) relatively inexpensive. High resolution vertical images obtained during recreational use of a small multi-rotor unmanned aerial vehicle (UAV) seemed to have the potential to answer this need. Using five case studies, the authors examine the potential of these images for mapping, documenting, and monitoring changes to surface karst resources following forestry-related activities in the coastal temperate rainforest of British Columbia (B.C.). Possible applications, strengths and limitations of this technology are discussed. The authors conclude that mini quadcopter UAVs equipped with high-resolution compact digital cameras are a promising and cost-effective new tool for karst scientists and karst managers.

Attitude Estimation in SO(3): A Comparative UAV Case Study

This paper introduces a novel algorithm to obtain attitude estimations from low cost inertial measurement units including 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. This nonlinear attitude estimator is derived from Lyapunov's theory and formulated in the special orthogonal group SO(3). The impact of the gyroscope bias is also assessed and an online estimator provided. The performance of the proposed estimator is validated and compared to current commonly used methods, namely the classical extended Kalman filter and two other nonlinear estimators in SO(3). Realistic simulations consider a quadcopter unmanned aerial vehicle subject to wind disturbances and whose sensors parameters have been identified from flight tests data.