Deep Reinforcement Learning Based Mobile Robot Navigation: A Review

被引：159
|
作者：
Zhu, Kai [1 ]
Zhang, Tao [1 ,2 ]
机构：
[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Beijing 100084, Peoples R China
来源：
TSINGHUA SCIENCE AND TECHNOLOGY | 2021年 / 26卷 / 05期
关键词：
mobile robot navigation; obstacle avoidance; deep reinforcement learning; DYNAMIC ENVIRONMENT; VISUAL NAVIGATION; NEURAL-NETWORKS; UAV; AVOIDANCE;
D O I：
10.26599/TST.2021.9010012
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：674 / 691
页数：18
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