Deep Reinforcement Learning Supervised Autonomous Exploration in Office Environments

Abstract

Exploration region selection is an essential decision making process in autonomous robot exploration task. While a majority of greedy methods are proposed to deal with this problem, few efforts are made to investigate the importance of predicting long-term planning. In this paper, we present an algorithm that utilizes deep reinforcement learning (DRL) to learn exploration knowledge over office blueprints, which enables the agent to predict a long-term visiting order for unexplored subregions. On the basis of this algorithm, we propose an exploration architecture that integrates a DRL model, a next-best-view (NBV) selection approach and a structural integrity measurement to further improve the exploration performance. At the end of this paper, we evaluate the proposed architecture against other methods on several new office maps, showing that the agent can efficiently explore uncertain regions with a shorter path and smarter behaviors.