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http://dx.doi.org/10.9717/kmms.2017.20.6.927

Machine Learning Model of Gyro Sensor Data for Drone Flight Control  

Ha, Hyunsoo (Department of Computer Science and Engineering, The Catholic University of Korea)
Hwang, Byung-Yeon (Department of Computer Science and Engineering, The Catholic University of Korea)
Publication Information
Journal of Korea Multimedia Society / v.20, no.6, 2017 , pp. 927-934 More about this Journal
Abstract
As the technology of drone develops, the use of drone is increasing, In addition, the types of sensors that are inside of smart phones are becoming various and the accuracy is enhancing day by day. Various of researches are being progressed. Therefore, we need to control drone by using smart phone's sensors. In this paper, we propose the most suitable machine learning model that matches the gyro sensor data with drone's moving. First, we classified drone by it's moving of the gyro sensor value of 4 and 8 degree of freedom. After that, we made it to study machine learning. For the method of machine learning, we applied the One-Rule, Neural Network, Decision Tree, and Navie Bayesian. According to the result of experiment that we designated the value from gyro sensor as the attribute, we had the 97.3 percent of highest accuracy that came out from Naive Bayesian method using 2 attributes in 4 degree of freedom. On and the same, in 8 degree of freedom, Naive Bayesian method using 2 attributes showed the highest accuracy of 93.1 percent.
Keywords
Machine Learning; Gyro Sensor; Drone Flight; Drone Control; Data Mining;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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