Zeng Hong, Shen Yitao, Hu Xuhui, Song Aiguo, Xu Baoguo, Li Huijun, Wang Yanxin, Wen Pengcheng
School of Instrument Science and Engineering, Southeast University, Nanjing, China.
State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China.
Front Neurorobot. 2020 Jan 24;13:111. doi: 10.3389/fnbot.2019.00111. eCollection 2019.
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.
非肌肉人机接口（HRI）和共享控制策略的最新进展表明，对于上肢没有残余运动或肌肉活动的人来说，控制辅助机器人手臂具有潜力。然而，大多数非肌肉HRI只产生离散值命令，导致对灵巧辅助机器人手臂的控制不直观且效果不佳。此外，在这类应用的共享控制策略中，用户命令和机器人自主命令通常会切换。根据之前的用户研究，发现这一特性会降低用户的能动感以及导致用户产生挫败感。在本研究中，我们首先通过结合脑机接口（BMI）和注视跟踪接口，提出了一种直观且易于学习和使用的混合HRI。对于所提出的混合注视-BMI，通过运动意图对运动速度进行连续调制，与利用注视信号进行无约束运动方向控制无缝且同时地发生。然后，我们提出一种共享控制范式，该范式始终通过动态组合调节将用户输入和自主性结合起来。所提出的混合注视-BMI和共享控制范式在健康受试者执行的机器人手臂伸展任务中得到了验证。所有用户都能够使用混合注视-BMI，将末端执行器依次移动以在水平面上到达目标，同时还能避免与障碍物碰撞。共享控制范式在为任务中最困难的部分提供协助的同时，尽可能保持了大量的意志控制。所呈现的半自主机器人系统为末端执行器接近目标产生了连续、平滑且无碰撞的运动轨迹。与没有机器人自主协助的系统相比，它显著降低了失败率以及用户完成任务所花费的时间和精力。
