Research and Application Deep Q-Network Algorithm for Automatic Navigation for Omnidirectional Mobile Robots

International Journal of Electronics and Communication Engineering
© 2021 by SSRG - IJECE Journal
Volume 8 Issue 1
Year of Publication : 2021
Authors : Tran Thi Huong, Pham Thi Thu Ha, Pham Van Bang
How to Cite?

Tran Thi Huong, Pham Thi Thu Ha, Pham Van Bang, "Research and Application Deep Q-Network Algorithm for Automatic Navigation for Omnidirectional Mobile Robots," SSRG International Journal of Electronics and Communication Engineering, vol. 8,  no. 1, pp. 12-17, 2021. Crossref,


In this paper, the setting up of the Deep Q-Network (DQN) algorithm in the mobile robot's Gazebo simulation environment has been calculated, designed, and controlled. Building experiments aim to make the robot model learn the best actions to control and navigate in an environment with many obstacles. For the mobile robot to move in an obstacle environment, the robot will then automatically control to avoid these obstacles. After that, the robot can remain within a specific limit, the more rewards it accumulates. The authors have performed various tests with many parameters and demonstrated the performance curves on the simulation. The research results will be the basis for designing and establishing control algorithms for current and future mobile robots for application in programming techniques and automation control in industrial control. 


Artificial intelligence, mobile robots, Obstacle robots, autonomous navigation, reinforcement learning, deep Q-learning.


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