Exploring multi-objective evolution of robot brains in obstacle and maze environments with varying complexities

Cheng, N. Song and Teo, Jason Tze Wi (2008) Exploring multi-objective evolution of robot brains in obstacle and maze environments with varying complexities. In: 4th IASTED International Conference on Advances in Computer Science and Technology (ACST 2008), 2 - 4 April 2008, Langkawi, Malaysia.

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Abstract

This paper explores a new approach of using a multiobjective evolutionary algorithm (MOEA) to evolve robot controllers in performing phototaxis task while avoiding obstacles or navigating through a maze in a simulated environment, to overcome problems involving more than one objective, where these objectives usually trade-off among each other and are expressed in different units. Experiments were conducted in six sets within a 10% noise environment with different task environment complexities to investigate whether the MOEA is effective for controller synthesis. A simulated Khepera robot is evolved by a Pareto-frontier Differential Evolution (PDE) algorithm, and learned through a 3-layer feed-forward artificial neural network, attempting to simultaneously fulfill two conflicting objectives of maximizing robot phototaxis behavior while minimizing the neural network's hidden neurons by generating a Pareto optimal set of controllers. Results showed that robot controllers could be successfully developed using the MOEA.

Item Type:Conference Paper (UNSPECIFIED)
Uncontrolled Keywords:Evolutionary robotics, Khepera, Multi-objective evolutionary algorithm, Neural network, Phototaxis
Subjects:?? TJ210.2-211.47 ??
Divisions:SCHOOL > School of Engineering and Information Technology
ID Code:1621
Deposited By:IR Admin
Deposited On:25 Mar 2011 09:31
Last Modified:30 Dec 2014 14:47

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