Toward Reliable Robot Navigation Using Deep Reinforcement Learning

Abstract

Reliable indoor navigation in the presence of dynamic obstacles is an essential capability for mobile robot deployment. Previous work on robot navigation focuses on expanding the network structure and hardware setup leading to more complex and costly systems. The accompanying physical demonstrations are often limited to slow-moving agents and simplistic obstacle configurations. In this thesis, we develop an end-to-end navigation system with a focus on real-world transferability and produce a low-cost and customizable robot platform. Instead of expanding the network structure, we rely on external capabilities such as backward motion, frame stacking, and behavioral reward design to improve performance while preserving transferability. By convention, these methods have been largely disregarded in previous works on deep reinforcement learning (DRL) for unmanned ground vehicle (UGV) navigation. We analyze the effect on performance in simulation of different off-policy algorithms with hyperparameter and reward function configurations. Experimental results show that our agent can achieve state-of-the-art performance in challenging and unseen simulated environments. In addition, physical robot demonstrations show that our system is capable of dealing with fast-moving and unpredictable agents in a real-world environment.