Dynamic gyroscope sensors fusion and calibration using Kalman filter

2018 ◽  
Vol 7 (2.3) ◽  
pp. 18
Author(s):  
Mishell D. Lawas ◽  
Sherwin A. Guirnaldo
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicle ◽  
Systems Design ◽  
Mean Square ◽  
Fusion Technique ◽  
System Experiment ◽  
Aerial Vehicle ◽  
Sensors Fusion ◽  
The Stability ◽  
Actual Test

The stability of an Unmanned Aerial Vehicle (UAV) during actual flight conditions is one parameter that is very important in systems design in Avionics. In this research, two sensors, the autopilot microcontroller and the smartphone gyroscope sensing mechanism, are fused together and calibrated to monitor the flying behavior of the UAV prior to actual test flights. The two fused sensors and installed inside the UAV for relatively increased sensing accuracy and best flight monitoring capabilities. A Kalman filter is used as fusion technique and a Stewart Motion tracker is also used to test the ruggedness and accuracy of the fused sensor system. Experiment results show that fused system can give an overall mean square error or 1.9729.

Mathematical Model for Studying the Evolution of Multi-Role Unmanned Aerial Vehicle in Turbulent Atmosphere

2013 ◽  
Vol 325-326 ◽  
pp. 984-989
Author(s):  
Cristina Mihailescu ◽  
Ioan Farcasan
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Unmanned Aerial Vehicle ◽  
Degrees Of Freedom ◽  
Guidance System ◽  
Theoretical Development ◽  
Atmospheric Conditions ◽  
Aerial Vehicle ◽  
Six Dof ◽  
The Stability

The paper purpose is to present some aspects regarding the control system of unmanned aerial vehicle - UAV, used for local observations, surveillance and monitoring of interest area or as a training target for anti-aircraft systems. The calculus methodology allows a numerical simulation of UAV evolution in bad atmospheric conditions by using a nonlinear model, as well as a linear one for obtaining the guidance command. The UAV model which will be presented has six DOF (degrees of freedom), and an autonomous control system. This theoretical development allows us to build the stability matrix, command matrix and the control matrix and finally to analyze the stability of autonomous UAV flight. A robust guidance system, based on Kalman filter will be evaluated for different fly conditions and the results will be presented. The flight parameters and guidance will be analyzed. The paper is inspired by national project SAMO (Autonomous Aerial Monitoring System for Interest Areas of Great Endurance). Keywords: UAV, Simulation, Control, Guidance, Endurance, Surveillance, Monitoring, Kalman filter

scholarly journals An overview of various kinds of wind effects on unmanned aerial vehicle

2019 ◽  
Vol 52 (7-8) ◽  
pp. 731-739 ◽  
Author(s):  
Bo Hang Wang ◽  
Dao Bo Wang ◽  
Zain Anwar Ali ◽  
Bai Ting Ting ◽  
Hao Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Great Influence ◽  
Wind Disturbance ◽  
Wind Fields ◽  
Wind Model ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
The Stability ◽  
The Mathematical Model ◽  
Vehicle Movement

Attitude, speed, and position of unmanned aerial vehicles are susceptible to wind disturbance. The types, characteristics, and mathematical models of the wind, which have great influence on unmanned aerial vehicle in the low-altitude environment, are summarized, including the constant wind, turbulent flow, many kinds of wind shear, and the propeller vortex. Combined with the mathematical model of the unmanned aerial vehicle, the mechanism of unmanned aerial vehicle movement in the wind field is illustrated from three different kinds of viewpoints including velocity viewpoint, force viewpoint, and energy viewpoint. Some simulation tests have been implemented to show the effects of different kinds of wind on unmanned aerial vehicle’s path and flight states. Finally, some proposals are presented to tell reader in which condition, which wind model should be added to simulation, and how to enhance the stability of unmanned aerial vehicle for different kinds of wind fields.

Fast Data Acquisition of Aerial Images Using Unmanned Aerial Vehicle System

2014 ◽  
Vol 3 (3) ◽  
pp. 162
Author(s):  
Norhadija Darwin ◽  
Anuar Ahmad
Keyword(s):  
Data Acquisition ◽  
Root Mean Square ◽  
Unmanned Aerial Vehicle ◽  
Large Scale ◽  
Digital Camera ◽  
Aerial Images ◽  
Mean Square ◽  
Fast Data Acquisition ◽  
Vehicle System ◽  
Aerial Vehicle

The present work discusses the technique and methodology of analysing the potential of fast data acquisition of aerial images using unmanned aerial vehicle system. This study utilizes UAV system for large scale mapping by using digital camera attached to the UAV. UAV is developed from the low-altitude photogrammetric mapping to perform the accuracy of the aerial photography and the resolution of the image. The Ground Control Points (GCPs) and Check Points (CPs) are established using Rapid Static techniques through GPS observation for registration purpose in photogrammetric process. The GCPs is used in the photogrammetric processes to produce photogrammetric output while the CP is employed for accuracy assessment. A Pentax Optio W90 consumer digital camera is also used in image acquisition of the aerial photograph. Besides, this study also involves image processing and map production using Erdas Imagine 8.6 software. The accuracy of the orthophoto is determined using the equation of Root Mean Square Error (RMSE). The final result from orthophoto is compared to the ground survey using total station to show the different accuracy of DEM and planimetric survey. It is discovered that root mean square errors obtained from UAV system are ± 0.510, ± 0.564 and ± 0.622 for coordinate x, y and z respectively. Hence, it can be concluded that the accuracy obtained from UAV system is achieved in sub meter. In a nutshell, UAV system has potential use for large scale mapping in field of surveying and other diversified environmental applications especially for small area which has limited time and less man power.

scholarly journals Aerial physical interaction via IDA-PBC

2019 ◽  
Vol 38 (4) ◽  
pp. 403-421 ◽  
Author(s):  
Burak Yüksel ◽  
Cristian Secchi ◽  
Heinrich H. Bülthoff ◽  
Antonio Franchi
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Control Method ◽  
Low Cost ◽  
Surface Inspection ◽  
Physical Interaction ◽  
External Disturbances ◽  
Aerial Vehicle ◽  
Passivity Based Control ◽  
The Stability ◽  
First Time

This paper proposes the use of a novel control method based on interconnection and damping assignment–passivity-based control (IDA-PBC) in order to address the aerial physical interaction (APhI) problem for a quadrotor unmanned aerial vehicle (UAV). The apparent physical properties of the quadrotor are reshaped in order to achieve better APhI performances, while ensuring the stability of the interaction through passivity preservation. The robustness of the IDA-PBC method with respect to sensor noise is also analyzed. The direct measurement of the external wrench, needed to implement the control method, is compared with the use of a nonlinear Lyapunov-based wrench observer and advantages/disadvantages of both methods are discussed. The validity and practicability of the proposed APhI method is evaluated through experiments, where for the first time in the literature, a lightweight all-in-one low-cost force/torque (F/T) sensor is used onboard of a quadrotor. Two main scenarios are shown: a quadrotor responding to external disturbances while hovering (physical human–quadrotor interaction), and the same quadrotor sliding with a rigid tool along an uneven ceiling surface (inspection/painting-like task).

scholarly journals Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments

2017 ◽  
Vol 9 (3) ◽  
pp. 169-186 ◽  
Author(s):  
Kexin Guo ◽  
Zhirong Qiu ◽  
Wei Meng ◽  
Lihua Xie ◽  
Rodney Teo
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Extended Kalman Filter ◽  
Ultra Wideband ◽  
Cooperative Localization ◽  
Relative Localization ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Displacement Measurements

This article puts forward an indirect cooperative relative localization method to estimate the position of unmanned aerial vehicles (UAVs) relative to their neighbors based solely on distance and self-displacement measurements in GPS denied environments. Our method consists of two stages. Initially, assuming no knowledge about its own and neighbors’ states and limited by the environment or task constraints, each unmanned aerial vehicle (UAV) solves an active 2D relative localization problem to obtain an estimate of its initial position relative to a static hovering quadcopter (a.k.a. beacon), which is subsequently refined by the extended Kalman filter to account for the noise in distance and displacement measurements. Starting with the refined initial relative localization guess, the second stage generalizes the extended Kalman filter strategy to the case where all unmanned aerial vehicles (UAV) move simultaneously. In this stage, each unmanned aerial vehicle (UAV) carries out cooperative localization through the inter-unmanned aerial vehicle distance given by ultra-wideband and exchanging the self-displacements of neighboring unmanned aerial vehicles (UAV). Extensive simulations and flight experiments are presented to corroborate the effectiveness of our proposed relative localization initialization strategy and algorithm.

The Methods of Information Acquisition and Information Fusion of the Photoelectric Sensor-Based Quadrotor Unmanned Aerial Vehicle

2020 ◽  
Vol 15 (1) ◽  
pp. 82-91
Author(s):  
Fen Hang ◽  
Xiangyang Hao
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicle ◽  
Kalman Filtering ◽  
Information Fusion ◽  
Information Acquisition ◽  
Reference System ◽  
Fusion Technology ◽  
Photoelectric Sensor ◽  
Aerial Vehicle ◽  
Sensor Information

When quadrotor unmanned aerial vehicle (UAV) is performing various tasks, even a small angular error will affect the evaluation of the entire motion trajectory. The multiple photoelectric sensor information fusion technology and the ARM microprocessor platform are used to form an attitude reference system for UAV. First, the hardware design of the small quadrotor UAV attitude reference system based on an ARM is introduced. The design framework and information acquisition module are expounded. In terms of the software of the system, the photoelectric sensor is used to receive different kinds of information, and the dynamic loading component is adopted as the solution to the interface diversification problems. Based on the attitude reference system, the collected information needs to be fused. The Kalman filtering is taken as the research object. Combined with the multiple photoelectric sensor information fusion technology, the Kalman filtering method is improved in the data preprocessing, and the low-pass filtering is added. Therefore, the abnormal data is filtered, and the estimated values are converged in a short time. Then, the data fusion is performed by the joint Kalman filter, least-squares fusion, and extended Kalman filter, respectively. During the experimental process, the system is proved to have good robustness, that is, in the case of individual sensor failure, the attitude acquisition section still obtains accurate attitude information of the UAV. The attitude reference system of UAV is realized. With the help of multi-sensor/information fusion technology, the attitude of the UAV is better handled, and its flight stability is improved.

scholarly journals Planar Smooth Path Guidance Law for a Small Unmanned Aerial Vehicle with Parameter Tuned by Fuzzy Logic

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yang Chen ◽  
Jianhong Liang ◽  
Chaolei Wang ◽  
Yicheng Zhang ◽  
Tianmiao Wang ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Unmanned Aerial Vehicle ◽  
Oscillation Amplitude ◽  
Mathematical Expression ◽  
Path Following ◽  
Simulation Experiments ◽  
Guidance Law ◽  
Smooth Path ◽  
Aerial Vehicle ◽  
The Stability

A guidance law has been designed to guide the small unmanned aerial vehicle towards the predefined horizontal smooth path. The guidance law only needs the mathematical expression for the predefined path, the positions, and the velocities of the vehicle in the horizontal inertial frame. The stability of the guidance law has been demonstrated by the Lyapunov stability arguments. In order to improve the path following performance, one of the parameters of the guidance law is tuned by using the fuzzy logic which will still keep its stability. The simulation experiments in the Matlab/Simulink environment to realize the square-, circular-, and the athletics track-style paths following are given to verify the effectiveness of the proposed method. The simulation results show that the path following performance will be improved with smaller overshoot and oscillation amplitude and shorter arrival time with the parameter tuned.

scholarly journals Aspects Regarding Fly Control of Quadcopter

2016 ◽  
Vol 3 (1-2.) ◽  
Author(s):  
Endrowednes Kuantama ◽  
Ioan Tarca ◽  
Radu Tarca ◽  
Dan Craciun
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Attitude Determination ◽  
Comparative Data ◽  
Reference Line ◽  
Mathematical Formula ◽  
Control Factors ◽  
Aerial Vehicle ◽  
Orientation Sensor ◽  
The Stability ◽  
Fly Control

Quadcopter is one of Unmanned Aerial Vehicle (UAV) which has two pairs of identical fixed pitched rotor propellers. It can fly autonomously based on pre-programmed flight or manually controlled by a remote, and every movement achieved by varying the speed of each rotor independently. The orientation of quadcopter axes relative to a reference line and its direction of motion are known as attitude. Fly control factors are affected by attitude determination which can be calculated from 3 possible angles using combined measurement. Gyroscope and accelerometer are primary sensors to control quadcopter attitude, but magnetometer sensor and GPS also used to enhance the stability during flight. This paper will focus on details of function and mathematical formula of every factor regarding fly control and comparative data of 2 types of orientation sensor used in this system.

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