OPTIMUM SHORT PATH FINDER FOR ROBOT USING Q-LEARNING

Abstract

Programming robots is a useful tedious task, so there is growing interest in building robots which can learn by themselves. This paper describes the Reinforcement Learning and teaching approach like Queue Learning (Q-Learning) to be implemented for robotics technology environment navigation and exploration. Q – Learning algorithm is one of the widely used online learning methods in robotics; it is simple, efficient, and not need to complex process as in adaptive system. The aim of this work is to empower the agent to learn a certain goal directed navigation strategy and to generate a shortest path in static environment which contain static obstacles; it uses one of the important intelligent search methods the “heuristic”. It makes a necessary modification for the search algorithm to suit the way of solving the problem. In our approach of learning from demonstration, the robot learns a reward function from the demonstration and a task model from repeated attempts (trials) to perform the task. A simplified reinforcement learning algorithm based on one-step Q-Learning that is optimized in speed and memory consumption is proposed and implemented in Visual Basic language (VB). The robot can be built using stepper motors and