Multi-objective evolution and hinton analysis of minimal neural control structures in an autonomous wheeled robot for RF-localization behaviors

A number of studies have demonstrated the capability of ANNs for the required robot behaviors by using an evolutionary optimization technique in generating more complex robot controllers. Interestingly however, there is still a serious lack of research in exploring the application of Evolutionary Multi-objective Optimization (EMO) algorithm in evolutionary robotics. In this paper, we investigate the utilization of a multi-objective approach in evolving artificial neural networks for a simulated autonomous Khepera robot. The generated neural network acts as a controller for radio frequency localization behavior of a Khepera robot. There are two conflicting objectives to be optimized during the evolution: (1) maximize the Khepera robot´s behavior for homing towards a RF signal source and (2) minimize the number of hidden neurons used in the robot. The testing results showed the robots were capable to achieve the objective with very few hidden neurons used. Furthermore, the genetic structures of the generated controllers have been further analyzed using the Hinton analysis and the results obtained are presented next.