Reinforcement Learning Approach for Navigation of Ground Robotic Platform in Statically and Dynamically Generated Environments
Dmitry Dudarenko, Julia Rubtsova, Artem Kovalev, Oleg Sivchenko · IFAC-PapersOnLine · 2019
This paper considers robotic platform navigation in terms of logistics, movement and track routing within indoor environments. Smart navigation and platform routing using a neural network are investigated. The paper discusses environment modeling with Unity ML software suite in static (prefabricated) and dynamically generated environments. Along with reinforcement learning, a procedural generation approach and its possible industrial applications are considered. The proposed algorithm for environment generation is characterized by higher performance comparing to analogues and allows to avoid model overfitting.