Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze-Brain Machine Interface.

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ID: 93418
2019
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Ranked #44 of 124 articles by views in frontiers in neurorobotics

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Abstract
Recent developments in the non-muscular human-robot interface (HRI) and shared control strategies have shown potential for controlling the assistive robotic arm by people with no residual movement or muscular activity in upper limbs. However, most non-muscular HRIs only produce discrete-valued commands, resulting in non-intuitive and less effective control of the dexterous assistive robotic arm. Furthermore, the user commands and the robot autonomy commands usually switch in the shared control strategies of such applications. This characteristic has been found to yield a reduced sense of agency as well as frustration for the user according to previous user studies. In this study, we firstly propose an intuitive and easy-to-learn-and-use hybrid HRI by combing the Brain-machine interface (BMI) and the gaze-tracking interface. For the proposed hybrid gaze-BMI, the continuous modulation of the movement speed via the motor intention occurs seamlessly and simultaneously to the unconstrained movement direction control with the gaze signals. We then propose a shared control paradigm that always combines user input and the autonomy with the dynamic combination regulation. The proposed hybrid gaze-BMI and shared control paradigm were validated for a robotic arm reaching task performed with healthy subjects. All the users were able to employ the hybrid gaze-BMI for moving the end-effector sequentially to reach the target across the horizontal plane while also avoiding collisions with obstacles. The shared control paradigm maintained as much volitional control as possible, while providing the assistance for the most difficult parts of the task. The presented semi-autonomous robotic system yielded continuous, smooth, and collision-free motion trajectories for the end effector approaching the target. Compared to a system without assistances from robot autonomy, it significantly reduces the rate of failure as well as the time and effort spent by the user to complete the tasks.
Reference Key
zeng2019semiautonomousfrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zeng, Hong;Shen, Yitao;Hu, Xuhui;Song, Aiguo;Xu, Baoguo;Li, Huijun;Wang, Yanxin;Wen, Pengcheng;
Journal frontiers in neurorobotics
Year 2019
DOI
10.3389/fnbot.2019.00111
URL
Keywords
brain–machine interface continuous shared control gaze tracking human–robot interface robotic arm reaching

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