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Automatic generation of Swarm Robotic behaviors using multi-objective evolution

Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.

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Bibliographic Details
Main Authors: Chin, Kim On, Teo, Jason, Azali, Saudi
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis 2009
Subjects:
Neural networks (Computer science)
Mobile robots
Robotics
Robots > Design and construction
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/7292
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spelling my.unimap-72922010-01-21T01:42:54Z Automatic generation of Swarm Robotic behaviors using multi-objective evolution Chin, Kim On Teo, Jason Azali, Saudi Neural networks (Computer science) Mobile robots Robotics Robots -- Design and construction Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia. This paper investigates the utilization of a multiobjective approach for evolving artificial neural networks (ANNs) that act as controllers for a collective box-pushing task based on radio frequency (RF)-localization of a group of virtual E-puck robots simulated in a 3D, physics-based environment. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal sets of ANN that optimize the conflicting objectives of maximizing the virtual Epuck robots’ behaviors for pushing a box towards a wall based on RF-localization as well as minimizing the number of hidden neurons used in its feed-forward ANN controller. A new fitness function which combines two different behaviors (1) RFlocalization behavior and (2) box-pushing behavior is also proposed. The experimentation results showed that the virtual Epuck robots were capable of moving towards to the target and thereafter push the box towards the target wall with very small neural network architecture. Hence, the results demonstrated that the utilization of the PDE approach in evolutionary robotics can be practically used to generate neural-based controllers that display intelligent collective behaviors in swarming autonomous mobile robots. 2009-11-13T08:28:18Z 2009-11-13T08:28:18Z 2009-10-11 Working Paper http://hdl.handle.net/123456789/7292 en Proceedings of the International Conference on Man-Machine Systems (ICoMMS 2009) Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Neural networks (Computer science)
Mobile robots
Robotics
Robots -- Design and construction
spellingShingle Neural networks (Computer science)
Mobile robots
Robotics
Robots -- Design and construction
Chin, Kim On
Teo, Jason
Azali, Saudi
Automatic generation of Swarm Robotic behaviors using multi-objective evolution
description Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.
format Working Paper
author Chin, Kim On
Teo, Jason
Azali, Saudi
author_facet Chin, Kim On
Teo, Jason
Azali, Saudi
author_sort Chin, Kim On
title Automatic generation of Swarm Robotic behaviors using multi-objective evolution
title_short Automatic generation of Swarm Robotic behaviors using multi-objective evolution
title_full Automatic generation of Swarm Robotic behaviors using multi-objective evolution
title_fullStr Automatic generation of Swarm Robotic behaviors using multi-objective evolution
title_full_unstemmed Automatic generation of Swarm Robotic behaviors using multi-objective evolution
title_sort automatic generation of swarm robotic behaviors using multi-objective evolution
publisher Universiti Malaysia Perlis
publishDate 2009
url http://dspace.unimap.edu.my/xmlui/handle/123456789/7292
_version_ 1643788750937915392
score 13.214268

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