Implementation of the centralized control system for drone training

  • Authors

    • Bong Hyun Kim
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.14190
  • Drone Test Software, Multiwii, Drone Training, Arduino, Flight Control Board, Signal Process

  • Abstract

    Background/Objectives: The drones became a representative item in the IoT era. However, there is no drone pilot test system that can safely train this in the education field. Drones have very dangerous structural problems, so it is very necessary to practice them easily. Therefore, it is necessary to develop a system that can control the drones safely and easily while controlling them.

    Methods/Statistical analysis: In this paper, we will develop software for controlling a dedicated board platform that can securely perform ground testing by mounting four drones of motor and drive on a board (PCB). To this end, we supported various control IMU (Inertial Measurement Unit) boards for attitude control by using sensor which is the core technology of drone flight control. Also, Acceleration Data, Angular Velocity Data, Earth Magnetic Field Data, and Atmospheric Pressure Data for maintaining the altitude were used for the drone flight.

    Findings: In the implemented central control system, the AT chip is built in and designed to perform all control related to the flight of the drone. In addition, since it is an embedded system, we have programmed the attitude control using the sensor, the motor output setting, and the controller connection information. The CPU required for drones control can be replaced with various types of controllers besides Fno Arduino, UNO, Muiltiwii. For this purpose, the main PCB is designed so that the power supply terminal can be used for each CPU. Finally, it was developed as a setup program to correct the sensor and output of the drone.

    Improvements/Applications: The system implemented in this paper can easily control the drone. In addition, acceleration, angular velocity, geomagnetic field, air pressure sensor, GPS, etc. necessary for drone control can be utilized by stabilizing the initial set value. In other words, the zero point of the sensor can be captured and the signal appropriate to the current state of the drone can be stored in the processor.

     

     

  • References

    1. [1] Henry, H. Perritt, Jr. Eliot, &O. Sprague. (2015). Drones. VAND. J. ENT. & TECH. L., 17(3), 5-15.

      [2] Melekhova, A.&Vinicio, V. (2015). Cloud and grid. Part I: Difference and convergence. Indian Journal of Science and Technology, 8(29), 1–10.

      [3] Eresmaa, N. Karppinen, A. M.Joffre, S. Rasanen, J. &Talvitie, H. (2006). Mixing height determinationby ceilometer.Atmospheric Chemistry and Physics, 6, 1485-1493.

      [4] Kim, B. H. (2016). Development of Young Children Coding Drone using Block Game. Indian Journal of Science & Technology, 9(44), 1-4.

      [5] Bart C. (2016). The Future of Drone Use: Opportunities and Threats from Ethical and Legal Perspectives. Asser press.

      [6] Sajal, S. Ankit, M. Rajesh, S. & Anita, G. (2016).UAV for Surveillance and Environmental Monitoring.Indian Journal of Science and Technology, 9 (43), 1–3.

      [7] Katsu Y. (2003). Controlling a Marionette with Human Motion Capture Data. Proceedings of the 2003 IEEE International Conference on Robotics and Automation, 3834-3841.

      [8] Banu, Y. Zumrut, A.Hakan, B. & Cigdem, S. (2011). The Effects of Apilarnil (Drone Bee Larvae) Administration on Growth Performance and Secondary Sex Characteristics of Male Broilers. Journal of Animal and Veterinary Advances, 10 (17), 2263-2266.

      [9] Altug, E. Ostrowski, J. P. & Taylor, C. J. (2005). Quadrotor Control Using Dual Camera Visual Feedback.The International Journal of Robotics Research, 24 (5), 329-341.

      [10] Abdelhamid, T. & Stephen, M. (2006). Attitude Stabilization of a VTOL Quadrotor Aircraft. IEEE Trans. On Control System Technology, 14 (3).

      [11] Oke, T. R. (1982). The energetic basis of the urban heat island, Quarterly journal of the royal meteorological society, 108(455), 1-24.

      [12] Brouwer. (2015). Surfzone Monitoring Using Rotary Wing Unmanned Aerial Vehicles. Journal of atmospheric and oceanic technology, 32(4), 855-863.

      [13] Sørensen, L. Y. Lars Toft Jacobsen, & John Paulin Hansen. (2017). Low Cost and Flexible UAV Deployment of Sensors. MDPI Sensors, 17 (1), 154.

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  • How to Cite

    Hyun Kim, B. (2018). Implementation of the centralized control system for drone training. International Journal of Engineering & Technology, 7(2.33), 379-383. https://doi.org/10.14419/ijet.v7i2.33.14190

    Received date: 2018-06-17

    Accepted date: 2018-06-17

    Published date: 2018-06-08