Abstract
This paper describes a system for learning and autonomous navigation of mobile robots on ROS (Robot Operating System) platform. Applying learning of autonomous navigation in the ROS platform will increase availability for autonomous navigation in actual environments, because the ROS platform is often utilized for navigation system in actual environments such as Tsukuba Challenge. Especially, in this study, a learning system based on Deep Q-Networks, that is effective for learning tasks in high-dimensional state spaces, is introduces. At first, simple maze problems as simplified robot navigation environment are solved. Then, the maze problems are applied to simulated robot navigation tasks on ROS platform. Learning results show that the robot obtained a policy to reach any destinations in the maps of the ROS simulator.
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