Fuzzy logic based nonlinear Kalman filter applied to mobile robots modelling

In order to reduce the false alarms in fault detection systems for mobile robots, accurate state estimation is needed. Through this work, a new method for localization of a mobile robot is presented. First, a Takagi-Sugeno fuzzy model of a mobile robot is determined, which is optimized using genetic algorithms, creating a precise representation of the kinematic equations of the robot. Then, the fuzzy model is used to design a new extension of the Kalman filter, based on several linear Kalman filters. Finally, the fuzzy filter is compared to the conventional extended Kalman filter, showing an improvement over the estimation made. The fuzzy filter also presents advantages in implementation, due to the fact that the covariance matrices needed are easier to estimate, increasing the estimation frequency.