Abstract

Using an Evolutionary Fuzzy Neural Network for Sensor-based Wall-following Control of a Mobile Robot

Highlights

  • The navigation control,(1,2) wall-following behavior control,(3,4) parallel parking control,(5) and path tracking control[6,7] of mobile robots are essential issues for implementing behavior-based control in unknown environments

  • To demonstrate the proposed fuzzy neural network (FNN) based on the improved artificial bee colony (IABC) algorithm, wall-following control was performed using the Pioneer 3-DX robot and the results were compared with those of other algorithms

  • This subsection describes the actual wall-following control of the Pioneer 3-DX mobile robot using the FNN based on the IABC algorithm

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Summary

Introduction

The navigation control,(1,2) wall-following behavior control,(3,4) parallel parking control,(5) and path tracking control[6,7] of mobile robots are essential issues for implementing behavior-based control in unknown environments. Wall-following behavior control is critical for a mobile robot. The control performance depends on the accuracy of its sensors because it is affected by noise interference. Fuzzy logic was developed in 1965 by Zadeh[8] to overcome the complication, uncertainty, and nonlinearity of systems. It is useful for solving uncertainty in real problems by simulating the human experience in fuzzy logic rules. Fuzzy logic controllers (FLCs) have been used by numerous researchers in mobile robot wall-following tasks[9,10] and obstacle avoidance.[11] To improve the performance of FLCs, many optimization algorithms, such as supervised learning,(12,13) population-based learning,(14,15) and reinforcement learning,(16) have

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