Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution
This study developed and effectively implemented an efficient navigation control of a mobile robot in unknown environments. The proposed navigation control method consists of mode manager, wall-following mode, and towards-goal mode. The interval type-2 neural fuzzy controller optimized by the dynami...
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/1687814017752483 |
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doaj-46f77ca0b00c4808b33467ef3b6c75f32020-11-25T03:40:42ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402018-01-011010.1177/1687814017752483Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolutionTzu-Chao Lin0Chao-Chun Chen1Cheng-Jian Lin2Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan City, Taiwan, ROCInstitute of Manufacturing Information and Systems, National Cheng Kung University, Tainan City, Taiwan, ROCDepartment of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan, ROCThis study developed and effectively implemented an efficient navigation control of a mobile robot in unknown environments. The proposed navigation control method consists of mode manager, wall-following mode, and towards-goal mode. The interval type-2 neural fuzzy controller optimized by the dynamic group differential evolution is exploited for reinforcement learning to develop an adaptive wall-following controller. The wall-following performance of the robot is evaluated by a proposed fitness function. The mode manager switches to the proper mode according to the relation between the mobile robot and the environment, and an escape mechanism is added to prevent the robot falling into the dead cycle. The experimental results of wall-following show that dynamic group differential evolution is superior to other methods. In addition, the navigation control results further show that the moving track of proposed model is better than other methods and it successfully completes the navigation control in unknown environments.https://doi.org/10.1177/1687814017752483 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tzu-Chao Lin Chao-Chun Chen Cheng-Jian Lin |
spellingShingle |
Tzu-Chao Lin Chao-Chun Chen Cheng-Jian Lin Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution Advances in Mechanical Engineering |
author_facet |
Tzu-Chao Lin Chao-Chun Chen Cheng-Jian Lin |
author_sort |
Tzu-Chao Lin |
title |
Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution |
title_short |
Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution |
title_full |
Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution |
title_fullStr |
Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution |
title_full_unstemmed |
Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution |
title_sort |
navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8140 |
publishDate |
2018-01-01 |
description |
This study developed and effectively implemented an efficient navigation control of a mobile robot in unknown environments. The proposed navigation control method consists of mode manager, wall-following mode, and towards-goal mode. The interval type-2 neural fuzzy controller optimized by the dynamic group differential evolution is exploited for reinforcement learning to develop an adaptive wall-following controller. The wall-following performance of the robot is evaluated by a proposed fitness function. The mode manager switches to the proper mode according to the relation between the mobile robot and the environment, and an escape mechanism is added to prevent the robot falling into the dead cycle. The experimental results of wall-following show that dynamic group differential evolution is superior to other methods. In addition, the navigation control results further show that the moving track of proposed model is better than other methods and it successfully completes the navigation control in unknown environments. |
url |
https://doi.org/10.1177/1687814017752483 |
work_keys_str_mv |
AT tzuchaolin navigationcontrolofmobilerobotusingintervaltype2neuralfuzzycontrolleroptimizedbydynamicgroupdifferentialevolution AT chaochunchen navigationcontrolofmobilerobotusingintervaltype2neuralfuzzycontrolleroptimizedbydynamicgroupdifferentialevolution AT chengjianlin navigationcontrolofmobilerobotusingintervaltype2neuralfuzzycontrolleroptimizedbydynamicgroupdifferentialevolution |
_version_ |
1724533323882037248 |