Brain-Machine Interfacing-Based Teleoperation of Multiple Coordinated Mobile Robots

This paper describes the development of a teleoperation control framework of multiple coordinated mobile robots through a brain-machine interface (BMI). Utilizing the remote images of an environment, transferred to the human operator, visual compressive feedback loop produces imagine errors in nonvector space, where images are considered as a set without image processing of feature extraction. Given an initial set and a goal set, visual evoked potentials are used to generate EEG motion commands to make the image set converge to the goal set. The online BMI, utilizing steady-state visually evoked potentials, analyzes the human EEG data in such a format that human intentions can be recognized by AdaBoostSVM classifier and motion commands produced for the teleoperated robot. Bezier curve is utilized to parameterize the motion commands and leader-follower formation control is proposed to guarantee a good reference trajectory tracking performance. Extensive experimental studies have been carried out to assess the effectiveness of the proposed approaches.