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

Tzu-Chao Lin,Cheng-Jian Lin,Chao-Chun Chen

doi:10.1177/1687814017752483

Abstract

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.

Highlights

IntroductionMobile robots detect the obstacles through the sensors to avoid collision in the navigation control
Mobile robots have been used to solve many problems in recent years, and it helps human solving many problems such as environmental exploration, object handling, and navigation.[1,2,3] To achieve these missions in a complex environment, the navigation technology of mobile robot is a very important topic and the design of controller becomes a major subject.Mobile robots detect the obstacles through the sensors to avoid collision in the navigation control
The proposed navigation control method consists of mode manager, wall-following (WF) mode, and towards-goal (TG) mode

Summary

Introduction

Mobile robots detect the obstacles through the sensors to avoid collision in the navigation control. Many novel designs[4,5,6] for intelligent robot control have been developed to improve obstacle avoidance in robot navigation. Researchers have used fuzzy logic control (FLC) and neural network (NN) to apply in robot navigation control. Al-Sahib and Ahmed[4] applied the information detected by the sensors directly into the designed fuzzy controller, and it makes the robot successfully perform the obstacle avoidance. Dutta[5] combined FLC, NN, and self-adaptive learning to adjust the parameters in fuzzy neural network (FNN).

Objectives

Methods

Results

Conclusion