Brain–Computer Interface-Based Stochastic Navigation and Control of a Semiautonomous Mobile Robot in Indoor Environments

Abstract

In this paper, a brain-computer interface (BCI)based navigation and control strategy is developed for a mobile robot in indoor environments. It combines the simultaneous localization and mapping to achieve the navigation and positioning for a mobile robot in indoor environments, where the RGB landmarks are regarded as the environmental features learned by the FastSLAM algorithm. The online BCI, based on steady-state visually evoked potentials, exploits multivariate synchronization index classification algorithm to analyze the human electroencephalograph (EEG) signals so that the human intention can be recognized accurately, and then the EEG-based motion commands are produced for the mobile robot. Probability potential field approach based on the probability density function of 2-D normal distribution is connected with the brain signals to generate a collision-free trajectory for the mobile robot. The entire system is semiautonomous, since the robot's low level behaviors are autonomous and the stochastic navigation is executed by the BCI, and it is verified by the extensive experiments involving five volunteers. All the participants can successfully tele-operate the mobile robot, and the experimental results have verified the effectiveness of the proposed approach.