A Fuzzy Logic Controller for Path Navigation of Brain-Controlled Mobile Robots in Complex Simulated Environments

Electroencephalogram (EEG)-based mobile robots can be powerful everyday aids for persons with severe disabilities, especially if they need assistance in moving voluntarily. With the amalgamation of EEG signal processing, artificial intelligence algorithm and mobile robotics trajectory planning, it is possible to drive a mobile robot using noninvasively recorded brain activity. In this research work, with the help of MATLAB simulations, mobile robot path navigation has been shown where the target location was retrieved using EEG signal data processing. A fuzzy logic controller has been designed and presented to conduct the obstacle avoidance mechanism based on fuzzy logic. The fuzzy logic approach has been made functional with 25 sets of rules, dictating the movement positions. The autonomous navigation system uses this position to direct the mobile robot to the intended place while avoiding collisions with outside barriers. This approach could prove very useful in effective mobile robot path planning in brand new and unpredictable situations.