Artificial Neural Controller Synthesis in Autonomous Mobile Cognition

This paper describes a new approach in using multi-objective evolutionary algorithms in evolving the neural network that acts as a controller for the phototaxis and radio frequency localization behaviors of a virtual Khepera robot simulated in a 3D, physics-based environment. The Pareto-frontier Dif...

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Main Authors: Kim On Chin, Jason Teo
Format: Article
Language:English
Published: International Scientific Academy of Engineering and Technology (ISAET) 2009
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Online Access:https://eprints.ums.edu.my/id/eprint/21707/1/Artificial%20Neural%20Controller%20Synthesis%20in%20Autonomous%20Mobile%20Cognition.pdf
https://eprints.ums.edu.my/id/eprint/21707/
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Institution: Universiti Malaysia Sabah
Language: English
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spelling my.ums.eprints.217072019-04-01T00:39:42Z https://eprints.ums.edu.my/id/eprint/21707/ Artificial Neural Controller Synthesis in Autonomous Mobile Cognition Kim On Chin Jason Teo QA Mathematics This paper describes a new approach in using multi-objective evolutionary algorithms in evolving the neural network that acts as a controller for the phototaxis and radio frequency localization behaviors of a virtual Khepera robot simulated in a 3D, physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is utilized to generate the Pareto optimal sets through a 3-layer feed-forward artificial neural network that optimize the conflicting objectives of robot behavior and network complexity, where the two different types of robot behaviors are phototaxis and RF-localization, respectively. Thus, there are two fitness functions proposed in this study. The testing results showed the robot was able to track the light source and also home-in towards the RF-signal source successfully. Furthermore, three additional testing results have been incorporated from the robustness perspective: different robot localizations, inclusion of two obstacles, and moving signal source experiments, respectively. The testing results also showed that the robot was robust to these different environments used during the testing phases. Hence, the results demonstrated that the utilization of the evolutionary multi-objective approach in evolutionary robotics can be practically used to generate controllers for phototaxis and RF-localization behaviors in autonomous mobile robots. International Scientific Academy of Engineering and Technology (ISAET) 2009 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/21707/1/Artificial%20Neural%20Controller%20Synthesis%20in%20Autonomous%20Mobile%20Cognition.pdf Kim On Chin and Jason Teo (2009) Artificial Neural Controller Synthesis in Autonomous Mobile Cognition. International Journal of Computer Science and Electronics Engineering (IJCSEE). ISSN 2320 - 4028
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Kim On Chin
Jason Teo
Artificial Neural Controller Synthesis in Autonomous Mobile Cognition
description This paper describes a new approach in using multi-objective evolutionary algorithms in evolving the neural network that acts as a controller for the phototaxis and radio frequency localization behaviors of a virtual Khepera robot simulated in a 3D, physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is utilized to generate the Pareto optimal sets through a 3-layer feed-forward artificial neural network that optimize the conflicting objectives of robot behavior and network complexity, where the two different types of robot behaviors are phototaxis and RF-localization, respectively. Thus, there are two fitness functions proposed in this study. The testing results showed the robot was able to track the light source and also home-in towards the RF-signal source successfully. Furthermore, three additional testing results have been incorporated from the robustness perspective: different robot localizations, inclusion of two obstacles, and moving signal source experiments, respectively. The testing results also showed that the robot was robust to these different environments used during the testing phases. Hence, the results demonstrated that the utilization of the evolutionary multi-objective approach in evolutionary robotics can be practically used to generate controllers for phototaxis and RF-localization behaviors in autonomous mobile robots.
format Article
author Kim On Chin
Jason Teo
author_facet Kim On Chin
Jason Teo
author_sort Kim On Chin
title Artificial Neural Controller Synthesis in Autonomous Mobile Cognition
title_short Artificial Neural Controller Synthesis in Autonomous Mobile Cognition
title_full Artificial Neural Controller Synthesis in Autonomous Mobile Cognition
title_fullStr Artificial Neural Controller Synthesis in Autonomous Mobile Cognition
title_full_unstemmed Artificial Neural Controller Synthesis in Autonomous Mobile Cognition
title_sort artificial neural controller synthesis in autonomous mobile cognition
publisher International Scientific Academy of Engineering and Technology (ISAET)
publishDate 2009
url https://eprints.ums.edu.my/id/eprint/21707/1/Artificial%20Neural%20Controller%20Synthesis%20in%20Autonomous%20Mobile%20Cognition.pdf
https://eprints.ums.edu.my/id/eprint/21707/
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