Wheeled Robot Design with Brain Wave Headset Control System

Arif Wibisono

Abstract


The development of the world of robotics is inevitable with the rapid development of supporting science and technology. There are various types and classifications of robots, although the basic development is not much different. One type of robot that is in demand and the most widely developed is the wheeled robot. The robot component itself is generally divided into 3 parts, the first sensor, the second processor or component processor and actuator, in this study which behaves as an actuator is a wheel, while that behaves as a sensor, researchers utilize brainwave reader headsets from neurosky, and those that served as a processor component or processor using Arduino Uno R3. The neurosky headset works wirelessly using a Bluetooth connection, and the data sent is in the form of a brain wave power level (blink streght level). Before it can be translated into a telepathic brain command, this signal is first captured and processed using an android handset using an application that is built based on blynk IoT, then after that the command is sent to Arduino as a robot processing component that has previously been fitted with HC-06 bluetooth module hardware. to capture wireless broadcasts from an android device, only after that the signal is processed by Arduino becomes a command to move forward, backward, left, right wheeled robot by the L298N motor driver. The test results in an ideal environment showed an average system success of 85%, while testing in a non-ideal environment (with obstacles of space and distance) showed an average system success of 40% with each test carried out 10 times.

Keywords


Wheeled Robot, Control System, Brain Wave Headset.

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References


H. Hiraishi, “Designing a robot controller by using a simple brain-wave sensor and a machine learning technique,” Artif. Life Robot., vol. 20, no. 3, pp. 217–221, 2015, doi: 10.1007/s10015-015-0224-y.

S. Hadi, A. Sholahuddin, and L. Rahmawati, “The design and preliminary implementation of low-cost brain-computer interface for enable moving of rolling robot,” in 2016 International Conference on Informatics and Computing (ICIC), 2016, pp. 283–287, doi: 10.1109/IAC.2016.7905730.

M. Nakirekanti, R. M. Prasad, E. Mahammad, K. Narsimha Reddy, and D. Khalandar Basha, “Brain wave controlled robot using matlab,” Int. J. Mech. Eng. Technol., vol. 8, no. 12, pp. 750–759, 2017.

D. Nurseitov, A. Serekov, A. Shintemirov, and B. Abibullaev, “Design and evaluation of a P300-ERP based BCI system for real-time control of a mobile robot,” in 2017 5th International Winter Conference on Brain-Computer Interface (BCI), 2017, pp. 115–120, doi: 10.1109/IWW-BCI.2017.7858177.

S. Rames, K. H. krishna, and J. K. Chaitanya, “Brainwave Controlled Robot Using Bluetooth,” Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 03, no. 08, pp. 11572–11578, 2014, doi: 10.15662/ijareeie.2014.0308072.

A. Butt and M. Stanacevic, “Implementation of Mind Control Robot,” in IEEE Long Island Systems, Applications and Technology (LISAT) Conference 2014, 2014, pp. 1–6, doi: 10.1109/LISAT.2014.6845218.

A. Dev, M. A. Rahman, and N. Mamun, “Design of an EEG-Based Brain Controlled Wheelchair for Quadriplegic Patients,” in 2018 3rd International Conference for Convergence in Technology (I2CT), 2018, pp. 1–5, doi: 10.1109/I2CT.2018.8529751.

S. S. Salunke, “Brain Computer Interface based Robot Design for Physically Disabled Person,” no. June, pp. 109–112, 2017.

S. Taksande and D. V Padole, “Brain Machine Interface System for,” vol. 3, no. 6, pp. 339–344, 2014.

W. Budiharto, “Menguasai Pemrograman Arduino dan Robot,” p. 92, 2020.

Q. Fu, Kinematics of Articulated Wheeled Robots: Exploiting Reconfigurability and Redundancy. State University of New York at Buffalo, 2008.

A. Al-Meshal, Self Balancing Two-Wheeled Robot. LAP Lambert Acad. Publ., 2011.

G. L. Holmes, Trajectory Control of a Wheeled Robot Using Interaction Forces for Intuitive Overground Human-robot Interaction. Missouri University of Science and Technology, 2020.

W. Budiharto, Belajar Sendiri : Membuat Robot Cerdas. Elex Media Komputindo.

H. D. Unbehauen, CONTROL SYSTEMS, ROBOTICS AND AUTOMATION -- Volume XXII: Robotics. EOLSS Publications, 2009.

B. K. Ghosh, T. J. T. N. X. Bijoy K. Ghosh, B. K. Ghosh, and T. J. Tarn, Control in Robotics and Automation: Sensor-based Integration. Academic Press, 1999.

G. A. Bekey, Autonomous Robots: From Biological Inspiration to Implementation and Control. MIT Press, 2005.

K. Dobosz and P. Wittchen, “Brain-computer interface for mobile devices,” J. Med. Informatics Technol., vol. 24, pp. 215–222, 2015.

X. Gao et al., “Analysis of EEG activity in response to binaural beats with different frequencies,” Int. J. Psychophysiol., vol. 94, no. 3, pp. 399–406, 2014, doi: https://doi.org/10.1016/j.ijpsycho.2014.10.010.

J. J. Park, I. Stojmenovic, H. Y. Jeong, and G. Yi, Computer Science and its Applications: Ubiquitous Information Technologies. Springer Berlin Heidelberg, 2014.

J. J. Park, Y. Pan, C. Kim, and Y. Yang, Future Information Technology - II. Springer Netherlands, 2015.

C. Gallo, Talk Like TED: The 9 Public Speaking Secrets of the World’s Top Minds. Pan Macmillan, 2014.

“EEG Algorithm Descriptions,” 2016.

T. Issa, P. Kommers, T. Issa, P. Isa’ias, and T. B. Issa, Smart Technology Applications in Business Environments. IGI Global, 2017.

I. R. Management Association, Unmanned Aerial Vehicles: Breakthroughs in Research and Practice: Breakthroughs in Research and Practice. IGI Global, 2019.

Y. M. Dinata, Arduino Itu Pintar. Elex Media Komputindo, 2016.

M. Nafea, A. Aisha, A.-K. Nurul Ashikin, and F. Harun, “Brainwave-Controlled System for Smart Home Applications,” 2018, pp. 75–80, doi: 10.1109/ICBAPS.2018.8527397.

S. M. Tiwari, N. Panwar, and S. Tripathi, “Robot Controlled by Mind Wave,” vol. 4, no. 1, 2018.

F. T. Patiung, A. S. M. L. St, S. R. U. A. Sompie, B. A. S. St, and M. T. J. T. Elektro-ft, “Rancang Bangun Robot Beroda Dengan Pengendali Suara,” E-Journal Tek. Elektro Dan Komput., vol. 2, no. 4, pp. 48–52, 2013, doi: 10.35793/jtek.2.4.2013.2858.

X. Gao et al., “Analysis of EEG activity in response to binaural beats with different frequencies,” Int. J. Psychophysiol., vol. 94, no. 3, pp. 399–406, 2014, doi: https://doi.org/10.1016/j.ijpsycho.2014.10.010.


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