Earticle

초록

영어

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 RF-localization behavior in autonomous mobile robots.

목차

Abstract
 1. Introduction
 2. Methodology
  2.1. ANN Representation
  2.2. The PDE-EMO
 3. Evaluation Function
 4. Experimental Setup
 5. Evolution Results
 6. Testing Results
 7. Conclusion and Future Research
 8. References

저자

• Chin Kim On [ Evolutionary Computing Laboratory, School of Engineering and Information Technology, Universiti Malaysia Sabah ]
• Jason Teo [ Evolutionary Computing Laboratory, School of Engineering and Information Technology, Universiti Malaysia Sabah ]

참고문헌

발행기관

간행물

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• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605