IEEE Trans Neural Syst Rehabil Eng. 2018 Aug;26(8):1556-1565. doi: 10.1109/TNSRE.2018.2848549. Epub 2018 Jun 18.
This paper presents the design and control of the intelligent sensing and force-feedback exoskeleton robotic glove to create a system capable of intelligent object grasping initiated by detection of the user's intentions through motion amplification. Using a combination of sensory feedback streams from the glove, the system has the ability to identify and prevent object slippage, as well as adapting grip geometry to the object properties. The slip detection algorithm provides updated inputs to the force controller to prevent an object from being dropped, while only requiring minimal input from a user who may have varying degrees of functionality in their injured hand. This paper proposes the use of a high dynamic range, low cost conductive elastomer sensor coupled with a negative force derivative trigger that can be leveraged in order to create a controller that can intelligently respond to slip conditions through state machine architecture, and improve the grasping robustness of the exoskeleton. The improvements to the previous design are described while the details of the controller design and the proposed assistive and rehabilitative applications are explained. Experimental results confirming the validity of the proposed system are presented. Finally, this paper concludes with topics for future exploration.
本文提出了一种智能传感和力反馈外骨骼机器人手套的设计和控制方法，旨在通过运动放大来检测用户意图，从而创建一个能够实现智能物体抓取的系统。该系统结合了手套的多个传感器反馈流，具有识别和防止物体滑动的能力，并且能够根据物体特性自适应地调整握持几何形状。滑动检测算法为力控制器提供更新的输入，以防止物体掉落，同时仅需要受伤手中功能程度不同的用户提供最小的输入。本文提出使用高动态范围、低成本的导电弹性体传感器和负力导数触发器，通过状态机架构创建一个能够智能响应滑动情况的控制器，从而提高外骨骼的抓取鲁棒性。本文描述了对以前设计的改进，并解释了控制器设计的详细信息以及提出的辅助和康复应用。实验结果证实了所提出系统的有效性。最后，本文总结了未来探索的主题。
