Multi-objective artificial evolution of RF-localization behavior and neural structures in mobile robots

This paper investigates the utilization of a multi- objective approach for evolving artificial neural networks (ANNs) that act as a controller for radio frequency (RF)- localization behavior of a virtual Khepera robot simulated in a 3D, physics-based environment. The non-elitist and elitist Pareto-frontier Differential Evolution (PDE) algorithm are used to generate the Pareto optimal sets of ANNs that optimize the conflicting objectives of maximizing the virtual Khepera robot´s behavior for homing towards a RF signal source and minimizing the number of hidden neurons used in its feedforward ANNs controller. A new fitness function which involved maximizing average wheels speed and detection of the RF signal source is also proposed. The experimentation results showed that the virtual Khepera robot was able to move towards to the target with using only a small number of hidden neurons. Furthermore, the testing results also showed that the success rate for the robot to achieve the signal source was higher when the elitist PDE-EMO algorithm was used. The path analysis of the Pareto controllers elucidated many different behaviors in terms of providing a successful homing behavior for the robot to attain the RF signal source.