The Use of Kalman Filter in Control The PanTilt Two-Axis Robot With Wearable System

Year 2022, Volume: 10 Issue: 2, 209 - 213, 30.04.2022

Temel Sönmezocak

https://doi.org/10.17694/bajece.1079636

Abstract

Today, the use of MEMS-based control-based unmanned aerial vehicles is becoming widespread. It is important that the systems used for the control of unmanned aerial vehicles are used sensitively. In this study, a wearable MEMS gyroscope-based headband is designed for the remote control of unmanned aerial vehicles. This system provides vibration-free control of the rotation angles of the motors in the direction of the camera connected to the 2-axis robotic pan-tilt system based on human-machine interaction. In addition, the signals produced by MEMS, the vibrations caused by electrical noise in the motors due to human interaction and environmental factors, are effectively eliminated with the Kalman filter. In this way, the images transmitted to the pilot become smoother. Therefore, it is cost-effective as it eliminates the need for additional hardware filtering structures.

Keywords

Accelerometer, Kalman filter, micro-electromechanical, servo control, signal processing, wearable systems.

References

• [1] A. Harindranath, M. Arora. “MEMS IMU sensor orientation algorithms-comparison in a simulation environment”, Int. Conf. Netw. Embedded Wireless Syst. IEEE Access, 2018.
• [2] P. Schopp, H. Graf, W. Burgard, Y. Manoli. “Self-calibration of accelerometer arrays”, IEEE Trans. Instrum. Meas., vol. 65, 2016.
• [3] S.O. Shin, D. Kim, Y.H. Seo. “Controlling mobile robot using IMU and EMG sensor-based gesture recognition”, 9th Int. Conf. Broadband Wireless Comput. Commun. & Appl., IEEE Access, China, 2014, pp.554-557.
• [4] G. Qinglei, L. Huawei, M. Shifu, H. Jian. “Design of a plane inclinometer based on MEMS accelerometer”, Int. Conf. Inf. Acqui., IEEE Access, 2007, pp.320-323.
• [5] G. Li., Y. He, Y. Wei, S. Zhu, Y. Cao. “The MEMS gyro stabilized platform design based on Kalman filter”, International Conference on Optoelectronics and Microelectronics (ICOM). IEEE Access, Harbin, 2013.
• [6] A. Nawrocka, M. Nawrocki, A. Kot. “The use of Kalman filter in control the balancing robot”, 21st Inter. Carpathian Control Conf. (ICCC). IEEE Access, Slovakia, 2020.
• [7] M.L. Hoang, A. P,etrosanto. “A new technique on vibration optimization of industrial inclinometer for MEMS accelerometer without sensor fusion”, IEEE Access. vol. 9, 2021.
• [8] S. Evren, F. Yavuz, M. Unel. “High precision stabilization of Pan-Tilt systems using reliable angular acceleration feedback from a master-slave Kalman filter”, J. Intell Robot syst. vol. 88, pp.97-127. 2017.
• [9] MPU6050 +-16 g Accel&Gyro, datasheet. In: Analog Devices [online]. 2022. Available at: www.analog.com.
• [10] H. Li, J. Liu, Y. Sun. “Bionic robot based on internet of things”, Inter. Conf. on Information Tech. Big Data and Artifical Intelligence. IEEE Access, China, 2020.
• [11] S. Kardos, P. Balog, S. Slosarcik. “Gait dynamics sensing using IMU sensor array system”, Journal of Advances in Elec. & Electronic Eng. (AEEE), vol.15, pp.71-76, 2017.
• [12] S. Kardos, S. Slosarcik, P. Balog. “Sensor array for evaluation of gait cycle. Journal of Advances in Elec. & Electronic Eng. (AEEE), vol.17, pp.459-465, 2019.
• [13] NRF24L01 2.4 GHz Transceiver, datasheet. In: Nordic Semiconductor [online]. 2022. Available at: www.alldatasheet.com.
• [14] P. Brandstetter, M. Dobrovsky. “Speed estimation motor using model reference adaptive system with Kalman filter”, Journal of Advances in Elec. & Electronic Eng. (AEEE), 2013, vol.11, pp.22-28, 2013.
• [15] M. Ghanai, A. Medjghou, K. Chafaa. “Extended Kalman filter states estimation of unmanned quadrotors for altitude-attidue tracking control”, Journal of Advances in Elec. & Electronic Eng. (AEEE), vol.16, pp.446-458, 2018.
• [16] Z. Peter, V. Wieser, M. Ghanai, A. Medjghou, K. Chafaa. “Radio channel state prediction by Kalman filter”, Journal of Advances in Elec. & Electronic Eng. (AEEE), vol.4, pp.237-240, 2005.