2017
DOI: 10.1109/tmech.2016.2606642
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Human Cooperative Wheelchair With Brain–Machine Interaction Based on Shared Control Strategy

Abstract: Abstract-In this paper, a human-machine shared control strategy is proposed for the navigation control of a wheelchair, employing both brain-machine control mode and autonomous control mode. In the brain-machine control mode, contrary to the traditional four-direction control signals, a novel brain-machine interface using steadystate visual evoked potentials is presented, which utilizes two brain signals to produce a polar polynomial trajectory. The produced trajectory is continuous in curvature without violat… Show more

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Cited by 91 publications
(41 citation statements)
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“…), while fully relying on the autonomy to generate the precise and safe low-level reactive robot motions (e.g., target approaching, collision avoidance, etc.). Researchers have exploited EEG signals for indoor navigation for a telepresence robot (Leeb et al, 2013) and the wheelchair Li Z. et al, 2017), combining left-right navigation signals with reactive robot behaviors to avoid obstacles. A system has been developed to enable users to specify a 2D end-effector path via a click-and-drag operation, and the collision avoidance is implemented with a sampling-based motion planner (Nicholas et al, 2013).…”
Section: Introductionmentioning
confidence: 99%
“…), while fully relying on the autonomy to generate the precise and safe low-level reactive robot motions (e.g., target approaching, collision avoidance, etc.). Researchers have exploited EEG signals for indoor navigation for a telepresence robot (Leeb et al, 2013) and the wheelchair Li Z. et al, 2017), combining left-right navigation signals with reactive robot behaviors to avoid obstacles. A system has been developed to enable users to specify a 2D end-effector path via a click-and-drag operation, and the collision avoidance is implemented with a sampling-based motion planner (Nicholas et al, 2013).…”
Section: Introductionmentioning
confidence: 99%
“…Earlier systems such as the Bremen Autonomous Wheelchair (Lankenau et al 1998) or NavChair (Simpson et al 1998) provided safety mechanisms such as nullifying potentially hazardous input signals or incorporating obstacle avoidance behaviours adapted from algorithms developed for autonomous robots, e.g. the Vector Field Histogram (Borenstein and Koren 1991) used recently in Ashley et al (2017) and Li et al (2017). A more recent approach in reactively assistive PMDs is the weighted fusion of robot command signals with that from the user, as investigated by Devigne et al (2016), Goil et al (2013) and Urdiales et al (2011) among others.…”
Section: Introductionmentioning
confidence: 99%
“…In particular, perceptual impairments can impact the ability to independently operate a wheelchair safely [4]. In this context, several research teams developed autonomous or semi-autonomous wheelchair navigation solutions [5] [6].…”
Section: Introductionmentioning
confidence: 99%