Evolving neural-based cognition of RF signals in autonomous Khepera robots

Little work has been done on using the evolutionary multi-objective approach in evolving the robot controllers. In this study, a multi-objective approach is utilized in evolving the artificial neural networks (ANNs) for autonomous mobile robot controller. The neural network acts as a controller for radio frequency (RF)-localization behavior of a Khepera robot simulated in a 3D physics-based environment. The Pareto optimal sets of ANNs are generated with elitist Pareto-frontier Differential Evolution (PDE) algorithm. The algorithm used to optimize two conflicting objectives; (1) minimize the virtual Khepera robot’s behavior for homing towards a RF signal source and (2) minimize the number of hidden neurons used in its ANNs. In this paper, we demonstrate and verify the evolved controllers’ moving performances, tracking performances and robustness in a random RF localization environment. In the testing phase, the robot’s tracking performances and robustness were tested with five different positioning of the RF signal source from its original position used during evolution. The testing results showed that the controllers were still able to navigate successfully to track the signal source with least possible used of permitted hidden neurons, hence demonstrating the evolved controllers’ robustness and tracking ability.