@article{SisLab2666,
          volume = {7},
          number = {1-2},
           month = {October},
          author = {Thi Thanh Van Nguyen and Vu Ha Le and Quang Vinh Tran},
           title = {A Robust Mobile Robot Navigation System using Neuro-Fuzzy Kalman Filtering and Optimal Fusion of Behavior-based Fuzzy Controllers},
       publisher = {REV},
            year = {2017},
         journal = {REV Journal on Electronics and Communications},
             doi = {doi:10.21553/rev-jec.128},
           pages = {47--56},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2666/},
        abstract = {This study proposes a control system model for mobile robots navigating in unknown environments. The proposed model includes a neuro-fuzzy Extended Kalman Filter for localization task and a behaviorbased fuzzy multi-controller navigation module. The neuro-fuzzy EKF, used for estimating the robot?s position from sensor readings, is an enhanced EKF whose noise covariance matrix is progressively adjusted by a fuzzy neural network. The navigation module features a series of independently-executed fuzzy controllers, each deals with a specific navigation sub-task, or behavior, and a multi-objective optimizer to coordinate all behaviors. The membership functions of all fuzzy controllers play the roles of objective functions for the optimizer, which produces an overall Pareto-optimal control signal to drive the robot. A number of simulations and real-world experiments were conducted to evaluate the performance of this model.}
}