Using an Adaptive Fuzzy Neural Network Based on a Multi-Strategy-Based Artificial Bee Colony for Mobile Robot Control

Cheng-Hung Chen,Cheng-Jian Lin,Shiou-Yun Jeng

doi:10.3390/math8081223

Abstract

This study proposes an adaptive fuzzy neural network (AFNN) based on a multi-strategy artificial bee colony (MSABC) algorithm for achieving an actual mobile robot navigation control. During the navigation control process, the AFNN inputs are the distance between the ultrasonic sensors and the angle between the mobile robot and the target, and the AFNN outputs are the robot’s left- and right-wheel speeds. A fitness function in reinforcement learning is defined to evaluate the navigation control performance of AFNN. The proposed MSABC algorithm improves the poor exploitation disadvantage in the traditional artificial bee colony (ABC) and adopts the mutation strategies of a differential evolution to balance exploration and exploitation. To escape in special environments, a manual wall-following fuzzy logic controller (WF-FLC) is designed. The experimental results show that the proposed MSABC method has improved the performance of average fitness, navigation time, and travel distance by 79.75%, 33.03%, and 10.74%, respectively, compared with the traditional ABC method. To prove the feasibility of the proposed controller, experiments were carried out on the actual PIONEER 3-DX mobile robot, and the proposed navigation control method was successfully completed.

Highlights

The navigation control of mobile robots is a popular research topic in the robot study area.Navigation is an easy task for animals and humans as they have the ability to think
The traditional artificial bee colony (ABC) algorithm simulates the intelligent foraging behavior of During the navigation control process, the distance between the ultrasonic sensor and the obstacle honey-bee swarms, which are good at exploration but poor at exploitation
This section describes the Pinoneer3-DX implemented for navigation in an unknown environment and makes analysis between multi-strategy artificial bee colony (MSABC) and other algorithms

Summary

Introduction

Navigation is an easy task for animals and humans as they have the ability to think. It is difficult for robots because they lack this ability. The robot navigation method has been classified into the hybrid behavior [1,2] and the behavior-based methods [3,4,5,6,7,8]. Seraji and Howard [1] used a distance sensor and a camera for detecting the environment around a robot. The robot navigation strategy has been designed using three independent behaviors, regional traverse–terrain, local avoid–obstacle, and global seek–goal. The center of gravity method was used by three consecutive defuzzifier steer angles and speeds of a robot navigating in an unknown environment

Objectives

Results

Conclusion