Study of Autonomous Driving of a Small Robot Using Deep Reinforcement Learning in Post-Disaster Rubble

Abstract

This paper, proposes a method for autonomous driving of a robot using deep reinforcement at a disaster site in a narrow space without a preliminary environmental map. RGB-D images are acquired from a camera attached to the robot and are inputted to a neural network of a deep reinforcement so as to determine the robot's action. Here, to deal with temporal relationship between images, a deep learning that can handle time-series data is also used. In addition, back function using the tether connected to the robot is exploited. As a result of experiments in simulation environment, success rate of the robot’s arrival to the goal is better than the conventional method. In addition, with the back function, a goal success rate of 98% was achieved.