Continuous ant optimized type-2 fuzzy controller for accurate mobile robot wall-following control

2012 ◽  
Author(s):  
Chia-Hung Hsu ◽  
Chia-Feng Juang
Keyword(s):  
Mobile Robot ◽  
Fuzzy Controller ◽  
Wall Following

scholarly journals Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775248 ◽  
Author(s):  
Tzu-Chao Lin ◽  
Chao-Chun Chen ◽  
Cheng-Jian Lin
Keyword(s):  
Mobile Robot ◽  
Differential Evolution ◽  
Fuzzy Controller ◽  
Dynamic Group ◽  
Unknown Environments ◽  
Navigation Control ◽  
Interval Type ◽  
Wall Following ◽  
Neural Fuzzy

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.

scholarly journals WALL-FOLLOWING BEHAVIOR-BASED MOBILE ROBOT USING PARTICLE SWARM FUZZY CONTROLLER

2016 ◽  
Vol 9 (1) ◽  
pp. 9 ◽  
Author(s):  
Andi Adriansyah ◽  
Shamsudin H. Mohd. Amin
Keyword(s):  
Mobile Robot ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Control Architecture ◽  
Learning Abilities ◽  
Fuzzy Variables ◽  
Behavior Based Control ◽  
Behavior Based ◽  
Two Stages ◽  
Wall Following

Behavior-based control architecture has been broadly recognized due to their compentence in mobile robot development. Fuzzy logic system characteristics are appropriate to address the behavior design problems. Nevertheless, there are problems encountered when setting fuzzy variables manually. Consequently, most of the efforts in the field, produce certain works for the study of fuzzy systems with added learning abilities. This paper presents the improvement of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO). A wall-following behaviors used on Particle Swarm Fuzzy Controller (PSFC) are developed using the modified PSO with two stages of the PSFC process. Several simulations have been accomplished to analyze the algorithm. The promising performance have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment.

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