Shared Control of a Robot Arm Using BCI and Computer Vision

Abstract

Brain-Computer Interface (BCI) is a device that can transform human thoughts into control commands. However, BCI aggravates the common problems of robot teleoperation due to its low-dimensional and noisy control commands, particularly when utilized to control high-DOF robots. Thus, a shared control strategy can enhance the BCI performance and reduce the workload for humans. This paper presents a shared control scheme that assists disabled people to control a robotic arm through a non-invasive Brain-Computer Interface (BCI) for reach and grasp activities. A novel algorithm is presented which generates a trajectory (position and orientation) for the end-effector to reach and grasp an object based on a specially designed color-coded tag placed on the object. A single camera is used for tag detection. The simulation is performed using the CoppeliaSim robot simulator in conjunction with MATLAB to implement the tag detection algorithm and Python script to receive the commands from the BCI. The human-in-the-loop simulation results prove the effectiveness of the proposed algorithm to reach and grasp objects.