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

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


Evolutionary robotics, multi-objective 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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