scholarly journals Deep reinforcement learning for shared control of mobile robots

2021 ◽  
Author(s):  
Chong Tian ◽  
Shahil Shaik ◽  
Yue Wang
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Shared Control

scholarly journals VFH+ based shared control for remotely operated mobile robots

2020 ◽  
Author(s):  
Pantelis Pappas ◽  
Manolis Chiou ◽  
Georgios-Theofanis Epsimos ◽  
Grigoris Nikolaou ◽  
Rustam Stolkin
Keyword(s):  
Mobile Robots ◽  
Shared Control

State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots

2013 ◽  
Vol 14 (3) ◽  
pp. 167-178 ◽  
Author(s):  
Xin Ma ◽  
Ya Xu ◽  
Guo-qiang Sun ◽  
Li-xia Deng ◽  
Yi-bin Li
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Autonomous Mobile Robots

Decentralized Reinforcement Learning Applied to Mobile Robots

2017 ◽  
pp. 368-379 ◽  
Author(s):  
David L. Leottau ◽  
Aashish Vatsyayan ◽  
Javier Ruiz-del-Solar ◽  
Robert Babuška
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots

scholarly journals A Teleoperation Framework for Mobile Robots Based on Shared Control

2020 ◽  
Vol 5 (2) ◽  
pp. 377-384 ◽  
Author(s):  
Jing Luo ◽  
Zhidong Lin ◽  
Yanan Li ◽  
Chenguang Yang
Keyword(s):  
Mobile Robots ◽  
Shared Control

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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