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http://dx.doi.org/10.6109/jkiice.2021.25.2.187

Proposal of autonomous take-off drone algorithm using deep learning  

Lee, Jong-Gu (School of Computer Inf. & Comm., Kunsan National University)
Jang, Min-Seok (School of Computer Inf. & Comm., Kunsan National University)
Lee, Yon-Sik (School of Computer Inf. & Comm., Kunsan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.2, 2021 , pp. 187-192 More about this Journal
Abstract
This study proposes a system for take-off in a forest or similar complex environment using an object detector. In the simulator, a raspberry pi is mounted on a quadcopter with a length of 550mm between motors on a diagonal line, and the experiment is conducted based on edge computing. As for the images to be used for learning, about 150 images of 640⁎480 size were obtained by selecting three points inside Kunsan University, and then converting them to black and white, and pre-processing the binarization by placing a boundary value of 127. After that, we trained the SSD_Inception model. In the simulation, as a result of the experiment of taking off the drone through the model trained with the verification image as an input, a trajectory similar to the takeoff was drawn using the label.
Keywords
Drone; Deep learning; Free space detection; Autonomous flight;
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