ORCID Profile
0000-0002-5025-4688
Current Organisation
Universiti Malaysia Sabah
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Publisher: IEEE
Date: 06-2006
Publisher: IEEE
Date: 10-2008
DOI: 10.1109/CSA.2008.14
Publisher: IEEE
Date: 2008
Publisher: IGI Global
Date: 2010
DOI: 10.4018/978-1-60566-766-9.CH028
Abstract: The utilization of a multi-objective approach for evolving artificial neural networks that act as the controllers for phototaxis and radio frequency (RF) localization behaviors of a virtual Khepera robot simulated in a 3D, physics-based environment is discussed in this chapter. It explains the comparison performances among the elitism without archive and elitism with archive used in the evolutionary multi-objective optimization (EMO) algorithm in an evolutionary robotics study. Furthermore, the controllers’ moving performances, tracking ability and robustness also have been demonstrated and tested with four different levels of environments. The experimentation results showed the controllers allowed the robots to navigate successfully, hence demonstrating the EMO algorithm can be practically used to automatically generate controllers for phototaxis and RF-localization behaviors, respectively. Understanding the underlying assumptions and theoretical constructs through the utilization of EMO will allow the robotics researchers to better design autonomous robot controllers that require minimal levels of human-designed elements.
Publisher: IEEE
Date: 04-2009
Publisher: IEEE
Date: 06-2008
Publisher: IEEE
Date: 08-2008
Publisher: IEEE
Date: 06-2008
Publisher: IEEE
Date: 06-2006
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Azali Saudi.