MASHINE REINFORCEMENT LEARNING FOR NAVIGATION OF MOBILE ROBOTS
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Abstract
New algorithm of machine learning for navigation of mobile robot navigation is introduced. It based on combination of Deep-Q-Learning and Double Q-Learning. Model is considered the movement of a mobile robot in some environment (environment is set Gazebo program package), known robot location and prevented obstackle collisions by navigation. Mobile Robotics Stimulation Toolbox and Gazebo visualization packages are used as Software. It is shown that the testing of new algorithm more than 10 times improved time characteristics in comparative with traditional algorithms of machine learning. The present algorithm may be to integrate in the apparatus means.
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References
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