scholarly journals An Experience Aggregative Reinforcement Learning With Multi-Attribute Decision-Making for Obstacle Avoidance of Wheeled Mobile Robot

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 108179-108190
Author(s):  
Chunyang Hu ◽  
Bin Ning ◽  
Meng Xu ◽  
Qiong Gu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Wheeled Mobile Robot ◽  
Multi Attribute Decision Making

scholarly journals A Hybrid Tracking Control Strategy for Nonholonomic Wheeled Mobile Robot Incorporating Deep Reinforcement Learning Approach

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 15592-15602
Author(s):  
Xueshan Gao ◽  
Rui Gao ◽  
Peng Liang ◽  
Qingfang Zhang ◽  
Rui Deng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robot ◽  
Control Strategy ◽  
Tracking Control ◽  
Wheeled Mobile Robot ◽  
Learning Approach

Study of obstacle avoidance based on fuzzy planner for wheeled mobile robot

2011 ◽  
Author(s):  
Suolin Duan ◽  
Yunfeng Li ◽  
Shuyue Chen ◽  
Lanping Chen ◽  
Ling Zou ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Wheeled Mobile Robot

A Task of Miniature Mobile Robot Learning for Obstacle Avoidance through Neural Networks

2012 ◽  
Vol 151 ◽  
pp. 498-502
Author(s):  
Jin Xue Zhang ◽  
Hai Zhu Pan
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Robot Learning ◽  
Q Learning ◽  
Robot Systems ◽  
The Neural Networks ◽  
Behavior Based

This paper is concerned with Q-learning , a very popular algorithm for reinforcement learning ,for obstacle avoidance through neural networks. The principle tells that the focus always must be on both ecological nice tasks and behaviours when designing on robot. Many robot systems have used behavior-based systems since the 1980’s.In this paper, the Khepera robot is trained through the proposed algorithm of Q-learning using the neural networks for the task of obstacle avoidance. In experiments with real and simulated robots, the neural networks approach can be used to make it possible for Q-learning to handle changes in the environment.

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