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.
['Suna Zhao', 'Zhijun Li', 'Rongxin Cui', 'Yu Kang', 'Fuchun Sun', 'Rong Song']
Brain–Machine Interfacing-Based Teleoperation of Multiple Coordinated Mobile Robots