Search results

1 – 1 of 1
Per page
102050
Citations:
Loading...
Access Restricted. View access options
Article
Publication date: 18 April 2020

Mohamed Khalil Mezghiche and Noureddine Djedi

The purpose of this study is to explore using real-observation quantum genetic algorithms (RQGAs) to evolve neural controllers that are capable of controlling a…

124

Abstract

Purpose

The purpose of this study is to explore using real-observation quantum genetic algorithms (RQGAs) to evolve neural controllers that are capable of controlling a self-reconfigurable modular robot in an adaptive locomotion task.

Design/methodology/approach

Quantum-inspired genetic algorithms (QGAs) have shown their superiority against conventional genetic algorithms in numerous challenging applications in recent years. The authors have experimented with several QGAs variants and real-observation QGA achieved the best results in solving numerical optimization problems. The modular robot used in this study is a hybrid simulated robot; each module has two degrees of freedom and four connecting faces. The modular robot also possesses self-reconfiguration and self-mobile capabilities.

Findings

The authors have conducted several experiments using different robot configurations ranging from a single module configuration to test the self-mobile property to several disconnected modules configuration to examine self-reconfiguration, as well as snake, quadruped and rolling track configurations. The results demonstrate that the robot was able to perform self-reconfiguration and produce stable gaits in all test scenarios.

Originality/value

The artificial neural controllers evolved using the real-observation QGA were able to control the self-reconfigurable modular robot in the adaptive locomotion task efficiently.

Details

World Journal of Engineering, vol. 17 no. 3
Type: Research Article
ISSN: 1708-5284

Keywords

1 – 1 of 1
Per page
102050