Reliable planning and execution of a human-robot cooperative system based on noninvasive brain-computer interface with uncertainty

A human-robot cooperative approach to reliable planning and execution is presented. The human-robot system consists of three components: human user, wheelchair robot and the noninvasive brain-computer interface (BCI) which can represent limit types of user´s intention patterns based on EEG signals, with insufficient decoding accuracy and time delay. To achieve efficient navigation and positioning under condition of decoding uncertainties of the BCI, three cooperative modes are proposed for specific situations based on trade-off of robot´s autonomy and user´s flexibility. The coding protocol in each mode is elucidated in detail, and strategies of mode switching are developed. To achieve continuous and smooth motion, a look-ahead visual feedback is applied, so that the user can adjust the intention and/or actively correct extraction error of the BCI before the robot reaches current path node, and consequently, reliable planning and execution are ensured. The effectiveness of the strategies is evaluated by simulations.