Autonomous Motion Control Using Deep Reinforcement Learning for Exploration Robot on Rough Terrain

摘要

In this paper, we propose a novel approach to allow exploration robots to solve navigation problems in complex environments including rough terrain. Previous studies show that it is difficult for robots to autonomously navigate without prior information such as an environmental map. Recently, advances in deep reinforcement learning (DRL) have made it possible to complete the autonomous motion control without the map. In this respect, we apply DRL to realize fully autonomous navigation on rough terrain for the exploration robot. The exploration robot can generate suitable control motion converted from observed depth information through a neural network. The simulation results show that our DRL-based approach can successfully solve the collision-free navigation of robots on rough terrain.