Deep Learning Based on Smooth Driving for Autonomous Navigation

Abstract

This paper proposes a new autonomous navigation method of a two-wheeled mobile robot using LiDAR sensor in an unknown environment. Recently, Deep Q Network(DQN) which is a combination of deep learning and Q-learning theory is attracting attention as a reinforcement learning algorithm. It is used to learn the robot itself to recognize obstacles and to avoid collisions while it is moving to a designated destination. The existing DQN method can handle only discrete and low dimensional space work, which is not suitable for continuous, especially the control of mobile robot. The existing LiDAR sensor method uses the distance value as the state used for the input of the learning, and therefore the system determines the next action only by the distance of the obstacle from the mobile robot. In this process, due to the frequent fluctuation of the action value, unnatural acceleration / deceleration actions are required, which cause not only physical shocks to the robot but also low driving power efficiency. In this paper, the problem has been solved by applying the replay buffer to store the output of the network. That is, the action values are stored in the memory and fed back to the input again following the action order of the network. Experiments are carried out on an actual robot after reinforcement learning in the ROS-GAZEBO simulations and the validity of the algorithm is verified through the analysis of the experimental data.