AUTONOMOUS WANDERING OF MOBILE ROBOT USING DEEP Q LEARNING

Abstract

Every robotic navigation application need to ensure that the robots avoid collisions with the obstacles in its path. Traditional path planning algorithms can be used to drive a robot from one point to the other in a static environment but they fail in dynamic environment cases. Ensuring the continuous motion of the robot without colliding with the obstacle is necessary for exploration. Hence to enhance the area exploration by self learning a novel method based on deep reinforcement learning is proposed. The current state of the environ ment are obtained from a 360 degree laser reading and the robot chooses an action from a predefined set of actions. Each action contains a combination of linear and angular velocity helping the robot to move smoothly in the environment. The trained robot is tested under an unknown environment and has shown good generalization of learning. The experiments were conducted on ROS-gazebo framework integrated with openai gym