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A navigation method for mobile robots using interval type-2 fuzzy neural network fitting Q-learning in unknown environments

A navigation method for mobile robots using interval type-2 fuzzy neural network fitting...  This paper presents a navigation algorithm based on interval type-2 fuzzy neural network fitting Q-learning (IT2FNN-Q), and succeeds in providing a solution for mobile robot navigation in complex environments. The algorithm utilizes the fuzzy reasoning adaptive ability and extensive functional approximation features of IT2FNN to solve this problem, mapped from state space to action space, of the Q-learning algorithm in unknown environments. Compared with the BP fitting Q-learning algorithm (BP-Q), IT2FNN-Q endows the robot with better adaptive and real-time decision-making abilities and solves the slow convergence and nonconvergence problems, through its local approximation. By comparison with the fuzzy neural network fitting Q-learning algorithm (FNN-Q), this proposed algorithm has more advantages for dealing with the external uncertainty, enabling the robot to complete a better path with less fuzzy rules. The results of the simulation and comparison of the proposed method with FNN-Q and BP-Q revealed that the mobile robot can navigate itself in complex environments with fewer steps, obtaining more reward values by adopting the algorithm presented in this paper. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent & Fuzzy Systems IOS Press

A navigation method for mobile robots using interval type-2 fuzzy neural network fitting Q-learning in unknown environments

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References (20)

Publisher
IOS Press
Copyright
Copyright © 2019 © 2019 – IOS Press and the authors. All rights reserved
ISSN
1064-1246
eISSN
1875-8967
DOI
10.3233/JIFS-182560
Publisher site
See Article on Publisher Site

Abstract

 This paper presents a navigation algorithm based on interval type-2 fuzzy neural network fitting Q-learning (IT2FNN-Q), and succeeds in providing a solution for mobile robot navigation in complex environments. The algorithm utilizes the fuzzy reasoning adaptive ability and extensive functional approximation features of IT2FNN to solve this problem, mapped from state space to action space, of the Q-learning algorithm in unknown environments. Compared with the BP fitting Q-learning algorithm (BP-Q), IT2FNN-Q endows the robot with better adaptive and real-time decision-making abilities and solves the slow convergence and nonconvergence problems, through its local approximation. By comparison with the fuzzy neural network fitting Q-learning algorithm (FNN-Q), this proposed algorithm has more advantages for dealing with the external uncertainty, enabling the robot to complete a better path with less fuzzy rules. The results of the simulation and comparison of the proposed method with FNN-Q and BP-Q revealed that the mobile robot can navigate itself in complex environments with fewer steps, obtaining more reward values by adopting the algorithm presented in this paper.

Journal

Journal of Intelligent & Fuzzy SystemsIOS Press

Published: Jan 1, 2019

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