Synthetic Evolution for Wheeled Robot Cognition in RF-Localization Behavior

This paper discussed 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 Pareto-frontier Differential Evolution (PDE) algorithm is 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 feed-forward ANNs controller. A fitness function used for mobile robot RF-localization behavior is proposed. The experimentation results showed the virtual Khepera robot was able to navigate towards signal source with using only a small number of hidden neurons. Furthermore, the Pareto-frontier solutions have been utilized for robustness testing purposes in the environment differs as that used during evolution. The results showed the PDE-EMO algorithm can be practically used in generating the required robot controllers for RF-localization behavior.