Exploring Multi-Objective Evolution of Robot Brains in Obstacle and Maze Environments with Varying Complexities

S.C. Ni and J. Teo (Malaysia)


Evolutionary robotics, multiobjective evolutionary algorithm, neural network, phototaxis, Khepera


This paper explores a new approach of using a multi objective 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.

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