Autonomous Navigation for Mobile Robot Based on Reinforcement Learning

International Journal of Electronics and Communication Engineering

© 2021 by SSRG - IJECE Journal

Volume 8 Issue 1

Year of Publication : 2021

Authors : Roan Van Hoa, Dinh Thi Hang, Tran Quoc Dat, Tran Dong, Tran Thi Huong

10.14445/23488549/IJECE-V8I1P101

How to Cite?

Roan Van Hoa, Dinh Thi Hang, Tran Quoc Dat, Tran Dong, Tran Thi Huong, "Autonomous Navigation for Mobile Robot Based on Reinforcement Learning," SSRG International Journal of Electronics and Communication Engineering, vol. 8,  no. 1, pp. 1-5, 2021. Crossref, https://doi.org/10.14445/23488549/IJECE-V8I1P101

Abstract:

Reinforcement learning is a subset of machine learning that deals with learning decisions from the environment's rewards. Classic reinforcement learning algorithms are usually applied to small sets of states and actions. However, in real applications, the state spaces are large, bringing the problems of generalization and the curse of dimensionality. In this paper, we integrate the neural network into reinforcement learning methods to generalize the value of all the states. Simulation results on the Gazebo framework show the feasibility of the proposed method. The Robot can complete navigation tasks safely in an unpredicted dynamic environment and becomes a truly intelligent system with strong self-learning and adaptive abilities. 

Keywords:

Artificial intelligence, autonomous navigation, mobile robots, reinforcement learning.

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