Qquadricopter methodology with control and imu stability, using application in mobile android device
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Published:
Jun 2, 2018
Keywords:
App-inventor, arduino, drone, microcontroller, smart phone
Main Article Content
Abstract
The exposed methodology is presented as a technology grouping for the development of a functional prototype type drone, which is controlled from an application for Android devices that makes its management and start-up more flexible. The conjugation of the wireless technology from a Smartphone to the model airplane and its referenced answers, locate the IMU (Inertia Measurement Unit) as an appropriate option for the definition of stability and control in the servomotors of the ailerons. The IMU uses the accelerometer and the gyroscope of the mobile device to send the control signals to a XBEE that communicates with an Arduino microcontroller, in charge of executing the PID control commands and in turn feeding the loops respectively. App-inventor as software for on-line use, it is versatile in its programming for the design of applications, it allows to capture the signals of the IMU in a simple way and with them the points of reference necessary for the control of the quadrocopters.
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Pineda Torres, F., & Cardona López, A. (2018). Qquadricopter methodology with control and imu stability, using application in mobile android device. Ingenio Magno, 8(2), 21 - 32. Retrieved from http://revistas.ustatunja.edu.co/index.php/ingeniomagno/article/view/1347
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Artículos Vol. 8-2
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Extension. (2017). Wi-fi Manager Extension. [online]: https://puravidaapps.com/wifi.php
Ferrer D. (2015), Adquisición de Datos IMU, en un sistema embebido. Proyecto de Grado. Universidad Politécnica de Valencia.
Figueiredo H., Saotome O. (2012) Simulation Paltaform for quadricopter: using Matlab and X-plane. SBR-LARS ISBN 978146734650-4. IEEE Brazil.
Fuentes J. (2015) Arquitectura cognitiva híbrida para la navegación autónoma de UAVs mediante mapas topológicos visuales”, [online] http://oa.upm.es/22579/1/JUANPABLO_FUENTES_ BREA.pdf
Guzmán A. (2016) Cálculo de variables de control PID para Drones Cuadcopter. Reaxion Ciencia y Tecnología Universitaria.
Hoflinger, F., Muller, J., Rui Zhang., Reindl, L.M., Burgard, W. (2013) “A Wireless Micro Inertial Measurement Unit (IMU),” in Instru¬mentation and Measurement, IEEE Transac-tions on , vol.62, no.9, pp.2583-2595, Sept. 2013 doi: 10.1109/TIM.2013.2255977
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Ident (2017), System Identification Toolbox, Trial Software [online]: https://www.mathworks.com/products/sysid.html
Inventor. (2017), Software App-Inventor. [onli¬ne]: http://appinventor.mit.edu/explore/
Linares J. (2017) Introducción a Processing. Departamento de Sistemas y Computación, Escuela Politécnica Superior d’Alcoi. [online] http://users.dsic.upv.es/~jlinares/grafics/pro¬cessing_spa_1.pdf
Pineda F. (2012) Control de dispositivos de se¬guridad a través de internet, utilizando el proto¬colo IEEE 802.15.4. Revista Clepsidra No.15. ISSN: 1900-1355
Robora. (2017), realidad Aumentada Aplicada a Competiciones de Robótica. [online] https://robora.wordpress.com
Sánchez B., Tapia J., Rosa P. (2016) Drones Social Aspects and Social Aplications, Visión Electrónica Universidad Distrital Vol.10 No.2.
Sensinger, J.W., Clark, S.D., Schorsch, J.F. (2011) “Exterior vs. interior rotors in robotic brushless motors,” in Robotics and Automa¬tion (ICRA), 2011 IEEE International Confe-rence. pp.2764-2770, 9-13 May 2011 doi: 10.1109/ICRA.2011.5979940
Sevilla L., (2014), Modelado y Control de un Cuadricòptero. Proyecto fin de Carrera Univer¬sidad Pontificia de Comillas.
Siso (2014), Control System Design Toolbox, Manual de Usuario. Mathworks.
Sumantri B. (2016) Least square based sli¬ding mode control for a quad-rotor helicopter and energy saving by chattering reduction. Vol 66-67. Mechanical Systems and Signal Processing.
Vila O. (2011) Modelización de aeronaves no tripuladas con Simulink. Proyecto de Grado Escuela Universitaria de Ingeniería. España. [online] http://upcommons.upc.edu/bitstream/handle/2099.1/12919/memoria.pdf