TY - JOUR
T1 - Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution
AU - Lin, Tzu Chao
AU - Chen, Chao Chun
AU - Lin, Cheng Jian
N1 - Publisher Copyright:
© 2018, © The Author(s) 2018.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85041595122&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041595122&partnerID=8YFLogxK
U2 - 10.1177/1687814017752483
DO - 10.1177/1687814017752483
M3 - Article
AN - SCOPUS:85041595122
SN - 1687-8132
VL - 10
JO - Advances in Mechanical Engineering
JF - Advances in Mechanical Engineering
IS - 1
ER -