Brain Teleoperation Control of a Nonholonomic Mobile Robot Using Quadrupole Potential Function

This paper presents the development of a brain–machine interfacing teleoperation control framework of a mobile robot using quadrupole potential function (QPF). The online brain–computer interface is based on steady-state visually evoked potentials, which employs the multivariate synchronization index classification algorithm to decode the human electroencephalograph (EEG) signals. In this way, human intentions can be recognized and EEG control commands can be produced for the mobile robot. Besides, a novel artificial potential function named QPF is designed to produce the potential fields to parameterize the EEG control commands. The classification results of the EEG signal are related to the distribution of the potential fields, and the collision-free path to a target position is automatically planned by the potential field. Such semi-automatic design builds the direct mapping from human intentions to the mobile robot’s behavior and reduces mental effort or exhaustion on subject’s part. Besides, based on the QPF with the capability of handling nonholonomic constraints, the integrated system can not only achieve obstacle avoidance but is also able to automatically stabilize the robot to a target configuration. Extensive experiment studies were conducted on several subjects to demonstrate the effectiveness of the proposed approaches.