Design and Implementation of a Low-Cost Aircraft Orientation Angle Measurement System using MPU6050 Sensor for Avionics Education

Ramadani Jamil, Freddy Kurniawan, Mardiana Irawaty, Lasmadi Lasmadi, Rindu Alriavindra Funny

Submitted : 2025-11-05, Published : 2025-01-14.

Abstract

This study presents the design and implementation of a low-cost aircraft orientation angle measurement system using the MPU6050 sensor and Arduino Uno microcontroller for educational applications in avionics. The system aims to simulate aircraft attitude angles—yaw, pitch, and roll—by integrating an inertial sensor module (MPU6050) with open-source hardware and software. The Arduino IDE was employed to acquire and process sensor data, while the Processing 3 environment was used to visualize the attitude motion in real time. The prototype was assembled and tested to evaluate its performance in detecting orientation changes across three axes. Experimental results demonstrate that the MPU6050-based system can accurately capture and display attitude variations without communication delay or signal error, providing a clear visualization of aircraft motion. The system’s simplicity, affordability, and open-source framework make it suitable for classroom demonstrations and laboratory exercises in avionics and control-system courses. Future improvements include the integration of Euler-angle computation and wireless communication to enhance mobility and data acquisition.

Full Text:

Artikel PDF

References

[1] M. Razavi, R. Lundberg, and H. Forsman, “Comparison of Six Sensor Fusion Algorithms with Electrogoniometer Estimation of Wrist Angle in Simulated Work Tasks,” Sensors, vol. 24, no. 13, 4173, 2024. doi: 10.3390/s24134173

[2] X. Zhou, C. Gao, J. Wei, and L. Sun, “Highly Accurate Attitude Estimation of Unmanned Aerial Vehicle Payloads Using Low-Cost MEMS,” Micromachines, vol. 16, no. 6, 632, 2025. doi: 10.3390/mi16060632

[3] S. P. H. Driessen, P. J. Scherpen, and C. C. de Visser, “Experimentally Validated Extended Kalman Filter for UAV State Estimation Using Low-Cost Sensors,” IFAC-PapersOnLine, vol. 51, no. 15, pp. 108–115, 2018. doi: 10.1016/j.ifacol.2018.09.017

[4] P. Narkhede, N. Rathod, and V. Chaudhari, “Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation,” Sensors, vol. 21, no. 6, 2021. doi: 10.3390/s21062007

[5] M. Caruso, A. Russo, G. Calabrò, and A. Di Salvo, “Analysis of the Accuracy of Ten Algorithms for Orientation,” Sensors, vol. 21, no. 7, 2021. doi: 10.3390/s21072258

[6] R. Li, X. Zhang, Y. Liu, and F. Wang, “Calib-Net: Calibrating the Low-Cost IMU via Deep Convolutional Neural Network,” Frontiers in Robotics and AI, vol. 9, 2022. doi: 10.3389/frobt.2022.836443

[7] Y. Fan, T. Li, and Y. Wang, “Influence of Sampling Rate on Wearable IMU Orientation,” Sensors, vol. 25, no. 7, 2025. doi: 10.3390/s25071694

[8] T. Franco, M. Ortiz, and L. Alvarez, “Motion Sensors for Knee Angle Recognition Using IMU,” Sensors, vol. 22, no. 19, 2022. doi: 10.3390/s22197493

[9] F. Aligia, M. R. Domínguez, and C. Soria, “An Orientation Estimation Strategy for Low-Cost IMU,” Measurement, vol. 173, 108873, 2021. doi: 10.1016/j.measurement.2020.108873

[10] M. A. R. Wicaksono, F. Kurniawan, and L. Lasmadi, “Kalman Filter untuk Mengurangi Derau Sensor Accelerometer pada IMU Guna Estimasi Jarak,” AVITEC, vol. 2, no. 2, pp. 67–74, 2020. doi: 10.28989/avitec.v2i2.752

[11] L. Lasmadi, F. Kurniawan, and M. I. Pamungkas, “Estimasi Sudut Rotasi Benda Kaku Berbasis IMU Menggunakan Kalman Filter,” AVITEC, vol. 3, no. 1, pp. 11–18, 2021. doi: 10.28989/avitec.v3i1.909

[12] F. Kurniawan, M. Nafiq, O. Dinaryanto, and L. Lasmadi, “Penentuan Koreksi Sudut Attitude pada Quadrotor Menggunakan Algoritma Zero Acceleration Compensation,” AVITEC, vol. 4, no. 1, 2022. doi: 10.28989/avitec.v4i1.1109

[13] L. Lasmadi, O. Dinaryanto, and F. Kurniawan, “Data Fusion for Displacement Estimation and Tracking of UAV Quadrotor in Dynamic Motion,” AVITEC, vol. 5, no. 2, 2023. doi: 10.28989/avitec.v5i2.1758

[14] F. Kurniawan, M. R. E. Nasution, and L. Lasmadi, “Penentuan Orientasi dan Translasi Gerakan UAV menggunakan Data Fusion berbasis Kalman Filter,” AVITEC, vol. 3, no. 2, pp. 89–94, 2021. doi: 10.28989/avitec.v3i2.890

[15] W. Widada, “Metode Adaptif Frekuensi-Cutoff untuk Complementary Filter pada Accelerometer dan Gyroscope untuk Sudut Pitch dan Roll Wahana Terbang,” Indonesian Journal of Aerospace, vol. 13, no. 1, 2025. doi: 10.30536/j.indojaer.2025.v13.i1.123

[16] F. Fahriannur and C. N. Karimah, “Sistem 3D Monitoring Lintasan Roket Menggunakan Sensor IMU dan Kalman Filter,” JTEIN: Jurnal Teknik Elektro Indonesia, vol. 4, no. 1, 2023. doi: 10.24036/jtein.v4i1.406

[17] S. Herfiah and F. N. Alpudli, “Implementation of IMU Sensor in VSAT Antenna Direction Monitoring System Using LoRa Module,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 13, no. 3S1, 2023. doi: 10.23960/jitet.v13i3S1.7831

[18] M. N. Kusuma, “Sensor Fusion Implementation for Attitude Estimation in Low-Cost UAVs,” Jurnal Teknologi Dirgantara, vol. 22, no. 2, 2024. doi: 10.30536/jt.2024.v22.i2.781

Article Metrics

Abstract view: 5 times
Download     : 2   times

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Refbacks

  • There are currently no refbacks.