Deep Reinforcement Learning Based Mobile Robot Navigation:A Review

被引：9
|
作者：
Kai Zhu [1 ]
Tao Zhang [1 ,2 ]
机构：
[1] the Department of Automation,Tsinghua University
[2] the Beijing National Research Center for Information Science and Technology, Tsinghua University
来源：
Tsinghua Science and Technology | 2021年 / 26卷 / 05期
关键词：
D O I：
暂无
中图分类号：
TP242 [机器人]; TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1111 ; 1405 ;
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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