Educating Future Computer Science Teachers to use the DQN Machine Learning Algorithm with ROS

Abstract

This research paper discusses the importance of training future computer science teachers in machine learning techniques using the robotic operating system (ROS)-based autonomous navigation robots. During the study, the flexibility of a mobile robot using the SLAM (simultaneous localization and mapping) method in mapping and navigation in the ROS system based on the Deep Q-Network (DQN) machine learning algorithm is considered. Research work is being conducted with students of educational programs for training future informatics teachers at Eurasian National University named after L.N. Gumilev and working informatics teachers. The considered works provide a foundation for future computer science teachers to learn and use the flexibility of mobile robots in the ROS operating system for robots based on computer vision methods.