Deep Reinforcement Learning Based Mobile Robot Navigation:A Review

• 作者: Kai Zhu Tao Zhang
• 来源期刊: 清华大学学报自然科学版（英文版）
• 年，卷（期）: 2021,（5）
• 所属期刊栏目: SPECIAL SECTION FOR 110th ANNIVERSARY OF TSINGHUA UNIVERSITY
• 页码范围: 674-691
• 页数: 18页
• 语种: 英文
• DOI: 10.26599/TST.2021.9010012

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.