Deep reinforcement learning based mobile robot navigation: A review
469 citations recorded by the source corpus.
: 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.
Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/c456be593e1cd306a7794c5906b9a7800a2fcfec
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Luca Carlone
- José A. Castellanos
- Julio A. Placed
- Vadim Indelman
- Henry Carrillo
- Jared Strader
- Nikolay A. Atanasov
- Davide Scaramuzza
In Artificial Intelligence
- José A. Castellanos
- Luca Carlone
- Julio A. Placed
- Vadim Indelman
- Henry Carrillo
- Jared Strader
- Nikolay A. Atanasov
In Computer Vision and Pattern Recognition
- José A. Castellanos
- Luca Carlone
- Julio A. Placed
- Vadim Indelman
- Henry Carrillo
- Jared Strader
- Nikolay A. Atanasov
- Davide Scaramuzza
Citations in the corpus
- A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, methodology): “, using random starting locations [57], [204], considering noise in the observations [209]) or by using sparser network inputs [207].”