Implementation of Reinforcement Learning Simulated Madel on Physical UGV Using Robot Operating System for Continual Learning

Artificial intelligence (AI) has been an issue in robotics, since AI is based on iterative algorithms. In general, simulations of physical models are used to show the outcome of learning algorithms or show proof of concepts. Since models are generated based on parameter estimations of training data, it is crucial to iterate a significant amount of times in order to model an accurate classification function. Thus, it would take a substantial amount of time for a robot to generate such a function. In this research, an implementation of reinforced learning will be applied on a unmanned ground vehicle (UGV) learning simulated model that will be translated into a physical UGV using Robot Operating System (ROS) to test performance of the given model.