Unmanned Aerial Vehicle (UAV) Data-Driven Modeling Software with Integrated 9-Axis IMU-GPS Sensor Fusion and Data Filtering Algorithm

Unmanned Aerial Vehicles (UAV) have emerged as versatile platforms, driving the demand for accurate modeling to support developmental testing. This paper proposes data-driven modeling software for UAV. Emphasizes the utilization of cost-effective sensors to obtain orientation and location data subse...

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Published in:International Conference on Information Technology and Electrical Engineering pp. 167 - 173
Main Authors: Arfakhsyad, Azfar Azdi, Rahman, Aufa Nasywa, Kinanti, Larasati, Awwalur Rizqi, Ahmad Ataka, Muhammad, Hannan Nur
Format: Conference Proceeding
Language:English
Published: IEEE 26.10.2023
Subjects:
Autonomous aerial vehicles
Data Filtering Algorithm
Data-driven modeling
Filtering algorithms
Quaternions
Real-time systems
Sensor fusion
Software
ISSN:2766-0419
Online Access:Get full text
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Abstract Unmanned Aerial Vehicles (UAV) have emerged as versatile platforms, driving the demand for accurate modeling to support developmental testing. This paper proposes data-driven modeling software for UAV. Emphasizes the utilization of cost-effective sensors to obtain orientation and location data subsequently processed through the application of data filtering algorithms and sensor fusion techniques to improve the data quality to make a precise model visualization on the software. UAV's orientation is obtained using processed Inertial Measurement Unit (IMU) data and represented using Quaternion Representation to avoid the gimbal lock problem. The UAV's location is determined by combining data from the Global Positioning System (GPS), which provides stable geographic coordinates but slower data update frequency, and the accelerometer, which has higher data update frequency but integrating it to get position data is unstable due to its accumulative error. By combining data from these two sensors, the software is able to calculate and continuously update the UAV's real-time position during its flight operations. The result shows that the software effectively renders UAV orientation and position with high degree of accuracy and fluidity.
AbstractList Unmanned Aerial Vehicles (UAV) have emerged as versatile platforms, driving the demand for accurate modeling to support developmental testing. This paper proposes data-driven modeling software for UAV. Emphasizes the utilization of cost-effective sensors to obtain orientation and location data subsequently processed through the application of data filtering algorithms and sensor fusion techniques to improve the data quality to make a precise model visualization on the software. UAV's orientation is obtained using processed Inertial Measurement Unit (IMU) data and represented using Quaternion Representation to avoid the gimbal lock problem. The UAV's location is determined by combining data from the Global Positioning System (GPS), which provides stable geographic coordinates but slower data update frequency, and the accelerometer, which has higher data update frequency but integrating it to get position data is unstable due to its accumulative error. By combining data from these two sensors, the software is able to calculate and continuously update the UAV's real-time position during its flight operations. The result shows that the software effectively renders UAV orientation and position with high degree of accuracy and fluidity.
Author Awwalur Rizqi, Ahmad Ataka
Arfakhsyad, Azfar Azdi
Muhammad, Hannan Nur
Rahman, Aufa Nasywa
Kinanti, Larasati
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  organization: Universitas Gadjah Mada,Department of Electrical Engineering and Information Technology,Yogyakarta,Indonesia
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Snippet Unmanned Aerial Vehicles (UAV) have emerged as versatile platforms, driving the demand for accurate modeling to support developmental testing. This paper...
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StartPage 167
SubjectTerms Autonomous aerial vehicles
Data Filtering Algorithm
Data-driven modeling
Filtering algorithms
Quaternions
Real-time systems
Sensor fusion
Software
Title Unmanned Aerial Vehicle (UAV) Data-Driven Modeling Software with Integrated 9-Axis IMU-GPS Sensor Fusion and Data Filtering Algorithm
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