Fuzzy Cognitive Map Applied in Low-Cost Mobile Robot Exploring Unknown Environment

Autonomous Navigation Systems still have some barriers to break. The decision-making
process of autonomous robots tend to have high computational costs, and still feel distant to being used in
everyday tasks. The major aspect discerning both papers is the objective finding, as the presented robot
does not know its destination anymore. We propose using Fuzzy Cognitive Maps (FCM) to control the
decision-making process of a low-cost autonomous mobile robot, that can be used for modeling and
testing small scale prototypes. The system is embedded using an ATMega2560 microcontroller and the
motors' actuation will be inferred directly from sensors reading. This approach presents a smoother
detour and optimized trajectory, when compared to a multivalued logic controller. With low
computational costs, and minimal adjustments, FCMs have proven to be useful as an embedded decisionmaking mechanism. Future works will conclude this research.