A spatially structured genetic algorithm over complex networks for mobile robot localisation

One of the most important problems in Mobile Robotics is to realize the complete robot's autonomy. In order to achieve this goal several tasks have to be accomplished. Among them, the robot's ability to localise itself turns out to be critical. The research community has provided, through the years, different methodologies to face the localisation problem, such as the Kalman Filter or the Monte Carlo Integrations methods. In this paper a different approach relying on a specialisation of the genetic algorithms is proposed. The novelty of this approach is to take advantage of the complex networks theory for the spatial deployment of the population to more quickly find out the optimal solutions. In fact, modelling the search space with complex networks and exploiting their typical connectivity properties, results in a more effective exploration of such space.