Multi-objective controller evolution of RF localization for mobile autonomous robots

In this study, we investigate the utilization of a multi-objective approach in evolving artificial neural networks (ANNs) for an autonomous mobile robot. The ANN acts as a controller for radio frequency (RF)-localization behavior of a Khepera robot simulated in a 3D physics-based environment. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal sets of ANNs that optimize two 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 ANNs controller. Little work has been done on using the evolutionary multi-objective approach in evolving robot controllers. We mainly demonstrate and verify the evolved controllers´ performances and robustness in an obstacle-laden environment. Two obstacles are included in the simulation environment to block the most common paths used for the robot to home in towards the signal source. In the testing phase, the robot´s robustness was tested with a different positioning of the obstacles from its original position used during evolution. The testing results show that the controllers were still able to navigate successfully, hence demonstrating the evolved controllers´ robustness. This study has thus shown that a multi-objective approach to evolutionary robotics in the form of the elitist PDE-EMO algorithm can be practically used to automatically generate robust controllers for RF-localization behavior in autonomous mobile robots.