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NAO robot obstacle avoidance based on fuzzy Q-learning

Shuhuan Wen (Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, China)
Xueheng Hu (Yanshan University, Qinhuangdao, China)
Zhen Li (Yanshan University, Qinhuangdao, China)
Hak Keung Lam (King’s College London, London, UK)
Fuchun Sun (Tsinghua University, Beijing, China)
Bin Fang (Tsinghua University, Beijing, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 18 October 2019

Issue publication date: 9 October 2020

293

Abstract

Purpose

This paper aims to propose a novel active SLAM framework to realize avoid obstacles and finish the autonomous navigation in indoor environment.

Design/methodology/approach

The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. The localization of the robot is based on FastSLAM algorithm.

Findings

Simulation results of avoiding obstacles using traditional Q-learning algorithm, optimized Q-learning algorithm and FOQL algorithm are compared. The simulation results show that the improved FOQL algorithm has a faster learning speed than other two algorithms. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.

Originality/value

The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.

Keywords

Citation

Wen, S., Hu, X., Li, Z., Lam, H.K., Sun, F. and Fang, B. (2020), "NAO robot obstacle avoidance based on fuzzy Q-learning", Industrial Robot, Vol. 47 No. 6, pp. 801-811. https://doi.org/10.1108/IR-01-2019-0002

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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