Ground robot navigation with Deep Reinforcement Learning in immersive environment
Larisa A. Zherdeva, E. Yu. Minaev, Denis Alekseevich Zherdev, Leonid Abakumov, Timofey Kazarkin · 2021 International Conference on Information Technology and Nanotechnology (ITNT) · 2021
The article presents studies of deep reinforcement learning method for the autonomous positioning problem of a small robot in a simulation environment. In our experiments, the open source game engine Unreal Engine is used to simulate a physically adequate 3D scene with obstacles. Images obtained by a virtual robot camera in the simulation environment are entered into a neural network to determine the required direction of the target and obstacles localization in 3D environment and then analyze the training of a real robot with reinforcement. In this study, we investigate the agent’s ability to learn free movement without interacting and colliding with other static or moving objects on the scene.