Evolution of RF-signal cognition for wheeled mobile robots using pareto multi-objective optimization

Chin, Kim On and Teo, Jason Tze Wi (2009) Evolution of RF-signal cognition for wheeled mobile robots using pareto multi-objective optimization. International Journal of Hybrid Information Technology, 2 (1). pp. 31-44. ISSN 1738-9968

Evolution of RF.pdf

Download (68kB) | Preview


This article describes a simulation model in which a multi-objective approach is utilized for evolving an artificial neural networks (ANNs) controller for an autonomous mobile robot. A mobile robot is simulated in a 3D, physics-based environment for the RF-localization behavior. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal set of ANNs that could optimize two objectives in a single run; (1) maximize the mobile robot homing behavior whilst (2) minimize the hidden neurons involved in the feed-forward ANN. The generated controllers are evaluated on its performances based on Pareto analysis. Furthermore, the generated controllers are tested with four different environments particularly for robustness assessment. The testing environments are different from the environment in which evolution was conducted. Interestingly however, the testing results showed some of the mobile robots are still robust to the testing environments. The controllers allowed the robots to home in towards the signal source with different movements’ behaviors. This study has thus revealed that the PDE-EMO algorithm can be practically used to automatically generate robust controllers for RFlocalization behavior in autonomous mobile robots.

Item Type: Article
Uncontrolled Keywords: artificial neural networks (ANNs), mobile robot, 3D, Pareto-frontier Differential Evolution (PDE)
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: FACULTY > Faculty of Engineering
Depositing User: Munira
Date Deposited: 28 Mar 2018 01:49
Last Modified: 28 Mar 2018 01:49
URI: http://eprints.ums.edu.my/id/eprint/19636

Actions (login required)

View Item View Item