This work is an evolution of a previous work in which we used a knowledge-based system to autonomous navigation area. Two navigation systems are developed: one uses Fuzzy Cognitive Maps (FCM) and the other is based on a Fuzzy Logic Controller (FLC). For the first system, a variant of the classical FCM, named Hybrid-Dynamic Fuzzy Cognitive Maps (HDFCM), is used to model decision tasks and/or to make inference into a mobile navigation context. Fuzzy Cognitive Maps are a tool that model qualitative structured knowledge through concepts and causal relationships. The proposed model allows representing the dynamic behavior of a mobile robot in presence of changes in the environment. A Hierarchical Weighted Fuzzy Logic Controller (HW-FLC) composes the second navigation system. Simulation results are presented allowing a comparison among both systems and showing the ability of the mobile robot to navigate among obstacles in different scenarios (navigation environment). Finally, initial real results with Arduino micro-controller are showed.