scholarly journals Reconstruction of aerodynamic angles from flight data for "Slybird" Unmanned Aerial Vehicle (UAV)

2016 ◽  
Author(s):  
Shikha Jain ◽  
C Kamali
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Flight Data ◽  
Aerodynamic Angles ◽  
Aerial Vehicle

scholarly journals Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Matthew B. Rhudy ◽  
Yu Gu ◽  
Haiyang Chao ◽  
Jason N. Gross
Keyword(s):  
Optical Flow ◽  
Unmanned Aerial Vehicle ◽  
Inertial Sensors ◽  
Wide Field ◽  
Flight Data ◽  
Information Filter ◽  
Full State ◽  
Flow Information ◽  
Aerial Vehicle ◽  
Image Distance

This paper offers a set of novel navigation techniques that rely on the use of inertial sensors and wide-field optical flow information. The aircraft ground velocity and attitude states are estimated with an Unscented Information Filter (UIF) and are evaluated with respect to two sets of experimental flight data collected from an Unmanned Aerial Vehicle (UAV). Two different formulations are proposed, a full state formulation including velocity and attitude and a simplified formulation which assumes that the lateral and vertical velocity of the aircraft are negligible. An additional state is also considered within each formulation to recover the image distance which can be measured using a laser rangefinder. The results demonstrate that the full state formulation is able to estimate the aircraft ground velocity to within 1.3 m/s of a GPS receiver solution used as reference “truth” and regulate attitude angles within 1.4 degrees standard deviation of error for both sets of flight data.

scholarly journals The IMPLEMENTATION OF AUTONOMOUS WAYPOINT IN RECONNAISSANCE PLANE (UNMANNED AERIAL VEHICLE) UAV GALAK-24 USE WITH MISSION PLANNER

2021 ◽  
Vol 2 (Oktober) ◽  
pp. 47-55
Author(s):  
Luthfan Herlambang ◽  
Eko Kuncoro ◽  
Muhamat Maariful Huda
Keyword(s):  
Remote Control ◽  
Unmanned Aerial Vehicle ◽  
Flight Data ◽  
Air Vehicle ◽  
Aerial Vehicle ◽  
The Military

Abstract: UAV or unmanned aerial vehicle is an air vehicle or what we often call an airplane that is controlled without a crew but controlled by a pilot remotely using a remote control. This study uses quantitative experiment methods because in this study it must be carried out when the UAV is flying using the autonomous waypoint method. Running the UAV with the autonomous waypoint method, we can use the Mission Planner software. First, we have to install the application on the mission planner and install pixhawk on the UAV which will act as the UAV brain that will receive and execute flight commands that will be sent by the mission planner later. The mission planner can also directly display flight data such as UAV altitude, UAV speed, UAV location, then the mission planner can also store flight data run by the UAV. The Autonomous waypoint method has been widely used in the military field, such as to carry out attacks on the enemy, reconnaissance, and patrol in an area quickly, and can also reduce casualties during combat operations.

scholarly journals APPROXIMATE ASSESSMENT OF THE OPERATIONAL PERFORMANCES OF AN UNMANNED AERIAL VEHICHLE ACCORDING TO ITS FLIGHT DATA

Aviation ◽  
2016 ◽  
Vol 19 (4) ◽  
pp. 187-193 ◽  
Author(s):  
Valeriy Silkov ◽  
Mykola Delas
Keyword(s):  
Fuel Consumption ◽  
Unmanned Aerial Vehicle ◽  
Comparative Assessment ◽  
Flight Duration ◽  
Flight Data ◽  
Flight Range ◽  
Different Types ◽  
Aerial Vehicle ◽  
Drag Ratio ◽  
Lift To Drag Ratio

The article is dedicated to the substantiation of the complex parameter that characterizes the technical level of an unmanned aerial vehicle (UAV). This parameter includes the maximum lift-to-drag ratio, propeller efficiency, specific fuel consumption, and other components, on which the main flight characteristics, such as flight range and flight duration, depend. To make a comparative assessment of UAVs of different types, a special scale is developed.

Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

2018 ◽  
Vol 122 (1252) ◽  
pp. 889-912
Author(s):  
Subrahmanyam Saderla ◽  
Dhayalan Rajaram ◽  
A. K. Ghosh
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Unmanned Aerial Vehicle ◽  
Rectangular Cross Section ◽  
A Priori ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Estimated Parameters ◽  
Aerial Vehicle ◽  
Cramer Rao Bound

ABSTRACTThe current research paper describes the lateral-directional parameter estimation from flight data of a miniature Unmanned Aerial Vehicle (UAV) using Maximum Likelihood (ML), and Neural-Gauss-Newton (NGN) methods. An unmanned configuration with a cropped delta planform and thin rectangular cross-section has been designed, fabricated and instrumented. Exhaustive full-scale wind-tunnel tests were performed on the UAV to extract the form of aerodynamic model that has to be postulated a priori for parameter estimation. Rigorous flight tests have been performed to acquire the flight data for several prescribed manoeuvres. Four sets of compatible flight data have been used to carry out parameter estimation using classical ML and neural-network-based NGN methods. It is observed that the estimated parameters are consistent and the lower values of the Cramer-Rao bound for the corresponding estimates have shown significant confidence in the obtained parameters. Furthermore, to validate the aerodynamic model used and to enhance the confidence in the estimated parameters, a proof of match exercise has been carried out.

A dynamic model parameter identification method for quadrotors using flight data

2018 ◽  
Vol 233 (6) ◽  
pp. 1990-2002 ◽  
Author(s):  
Pin Lyu ◽  
Sheng Bao ◽  
Jizhou Lai ◽  
Shichao Liu ◽  
Zang Chen
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Unmanned Aerial Vehicle ◽  
Model Parameters ◽  
Rolling Moment ◽  
Flight Data ◽  
Model Parameter ◽  
Identification Method ◽  
Aerial Vehicle ◽  
Model Parameter Identification

The dynamic model parameter identification is important for unmanned aerial vehicle modeling and control. The unmanned aerial vehicle model parameters are usually identified through wind tunnel experiments, which are complex. In this paper, a model parameter identification method is proposed using the flight data for quadrotors. The parameters of the thrust, drag force, torque, rolling moment and pitching moment are estimated through Kalman filter. Global positioning system and inertial sensors are used as measurements. The observabilities of the model parameters and their degrees of observability are analyzed. Flight experiments are carried out to verify the proposed method. It is shown that the model parameters estimated by the proposed method have good accuracies, demonstrating the validity of the proposed method.

Longitudinal and lateral aerodynamic characterisation of reflex wing Unmanned Aerial Vehicle from flight tests using Maximum Likelihood, Least Square and Neural Gauss Newton methods

2019 ◽  
Vol 123 (1269) ◽  
pp. 1807-1839
Author(s):  
S. Saderla ◽  
R. Dhayalan ◽  
K. Singh ◽  
N. Kumar ◽  
A. K. Ghosh
Keyword(s):  
Maximum Likelihood ◽  
Unmanned Aerial Vehicle ◽  
Full Scale ◽  
Least Square ◽  
Newton Methods ◽  
Consistency Check ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Estimated Parameters ◽  
Aerial Vehicle

ABSTRACTIn this paper, longitudinal and lateral-directional aerodynamic characterisation of the Cropped Delta Reflex Wing (CDRW) configuration–based unmanned aerial vehicle is carried out by means of full-scale static wind-tunnel tests followed by full-scale flight testing. A predecided set of longitudinal and lateral/directional manoeuvres is performed to acquire the respective flight data, using a dedicated onboard flight data acquisition system. The compatibility of the acquired dynamics is quantified, in terms of scale factors and biases of the measured variables, using Kinematic consistency check. Maximum likelihood (ML), least squares and newly emerging neural Gauss–Newton (NGN) methods were implemented for a wing-alone delta configuration, mainly to capture the dynamic derivatives for both longitudinal and lateral directional cases. Estimated damping and weak dynamic derivatives, which are in general challenging to capture for a wing alone configuration, are consistent using ML and NGN methods. Validation of the estimated parameters with aerodynamic model is performed by proof-of-match exercise and are presented therein.

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