Deep Reinforcement Learning Based Mobile Robot Navigation: A Review

Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and experience learning abilities. There is a growing trend of applying DRL to mobile robot navigation. In this paper, we review DRL methods and DRL-based navigation frameworks. Then we systematically compare and analyze the relationship and differences between four typical application scenarios: local obstacle avoidance, indoor navigation, multi-robot navigation, and social navigation. Next, we describe the development of DRL-based navigation. Last, we discuss the challenges and some possible solutions regarding DRL-based navigation.

Automated summary

This paper discusses the application of Deep Reinforcement Learning (DRL) in mobile robot navigation, compares the relationships and differences between four typical application scenarios, describes the development of DRL-based navigation, and addresses the challenges and possible solutions facing DRL-based navigation.