Abstract
AbstractIn this paper, a human-machine shared control strategy is developed for the navigation of a wheelchair. The shared controller switches between a brain-machine control mode and an autonomous control mode. In the brain-machine control mode, a novel brain-machine interface (BMI) using only two command signals produced by steady state visual evoked potentials (SSVEP) instead of traditional four-direction command signals is developed. These two brain signals are involved to generate a polar polynomial trajectory (PPT), which is continuous in curvature without violating dynamic constraints of the wheelchair. In the autonomous control mode, the synthesis of angle-based potential field (APF) and vision-based simultaneous localization and map-building (SLAM) technique is proposed to guide the robot navigating in environments where obstacles exist. Experimental studies have been carried out with a number of volunteers and the effectiveness of the proposed shared control scheme has been verified.KeywordsShared controlBrain-machine interfacesDynamic constraints
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