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A novel mobile robot navigation method based on deep reinforcement learning

Content Provider	SAGE Publishing
Author	Quan, Hao Li, Yansheng Zhang, Yi
Copyright Year	2020
Abstract	At present, the application of mobile robots is more and more extensive, and the movement of mobile robots cannot be separated from effective navigation, especially path exploration. Aiming at navigation problems, this article proposes a method based on deep reinforcement learning and recurrent neural network, which combines double net and recurrent neural network modules with reinforcement learning ideas. At the same time, this article designed the corresponding parameter function to improve the performance of the model. In order to test the effectiveness of this method, based on the grid map model, this paper trains in a two-dimensional simulation environment, a three-dimensional TurtleBot simulation environment, and a physical robot environment, and obtains relevant data for peer-to-peer analysis. The experimental results show that the proposed algorithm has a good improvement in path finding efficiency and path length.
Related Links	https://journals.sagepub.com/doi/pdf/10.1177/1729881420921672?download=true
ISSN	17298806
Issue Number	3
Volume Number	17
Journal	International Journal of Advanced Robotic Systems (ARX)
e-ISSN	17298814
DOI	10.1177/1729881420921672
Language	English
Publisher	Sage Publications UK
Publisher Date	2020-06-05
Publisher Place	London
Access Restriction	Open
Rights Holder	© The Author(s) 2020
Subject Keyword	robot exploration Deep reinforcement learning recurrent neural network DDQN
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Science Applications Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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