Mobile Robot Localization Using Fuzzy Neural Network Based Extended Kalman Filter
This addresses localization accuracy for mobile robots, but it is incremental as it builds on existing EKF methods.
The paper tackled mobile robot localization by proposing a fuzzy neural network to adjust noise covariance matrices in an extended Kalman filter, resulting in improved accuracy and reduced divergence compared to the standard EKF in simulations and experiments.
This paper proposes a novel approach to improve the performance of the extended Kalman filter (EKF) for the problem of mobile robot localization. A fuzzy logic system is employed to continuous-ly adjust the noise covariance matrices of the filter. A neural network is implemented to regulate the membership functions of the antecedent and consequent parts of the fuzzy rules. The aim is to gain the accuracy and avoid the divergence of the EKF when the noise covariance matrices are fixed or wrongly determined. Simulations and experiments have been conducted. The results show that the proposed filter is better than the EKF in localizing the mobile robot.