Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
