Reinforcement Learning Neural Network-Based Adaptive Control for State and Input Time-Delayed Wheeled Mobile Robots

2020 ◽  
Vol 50 (11) ◽  
pp. 4171-4182 ◽  
Author(s):  
Shu Li ◽  
Liang Ding ◽  
Haibo Gao ◽  
Yan-Jun Liu ◽  
Nan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Reinforcement Learning ◽  
Mobile Robots ◽  
Wheeled Mobile Robots ◽  
Input Time

scholarly journals A novel mobile robot navigation method based on deep reinforcement learning

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092167
Author(s):  
Hao Quan ◽  
Yansheng Li ◽  
Yi Zhang
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Mobile Robots ◽  
Recurrent Neural Network ◽  
Three Dimensional ◽  
Mobile Robot Navigation ◽  
Simulation Environment ◽  
Network Modules ◽  
Good Improvement ◽  
Dimensional Simulation

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.

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