The Wearable Robotic Forearm: Design and Predictive Control of a Collaborative Supernumerary Robot
The Wearable Robotic Forearm: Design and Predictive Control of a Collaborative Supernumerary Robot
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DOI：
10.3390/robotics10030091
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发表时间：
2021-07
期刊：
影响因子：
3.7
通讯作者：
Vighnesh Vatsal;Guy Hoffman
中科院分区：
文献类型：
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作者：
Vighnesh Vatsal;Guy Hoffman
本文介绍了一个多余的可穿戴机器人前臂（WRF）的设计过程，沿着的方法稳定机器人的末端执行器使用人体运动预测。该设备作为一个轻量级的“第三只手臂”的用户，扩大他们的范围，在近距离的协作活动中的操作和操纵。它是迭代开发的，遵循以用户为中心的设计过程，包括在线调查，上下文查询和面对面的可用性研究。模拟表明，WRF显著提高了佩戴者的可达工作空间体积，同时在典型使用场景期间保持在生物力学人体工程学负载限制内。当在这种情况下操作设备时，用户由于他们的身体移动而在其姿势中引入干扰。我们提出了两种方法来克服这些干扰：自回归（AR）时间序列和递归神经网络（RNN）。这些模型用于预测佩戴者的身体运动以补偿干扰，预测范围通过线性系统识别来确定。这些模型在KIT人体运动数据库的一个子集上进行了离线训练，并在五种使用场景中进行了测试，以保持WRF末端执行器的3D姿态静态。与直接反馈控制相比，预测模型的加入将末端执行器位置误差降低了26%。
This article presents the design process of a supernumerary wearable robotic forearm (WRF), along with methods for stabilizing the robot’s end-effector using human motion prediction. The device acts as a lightweight “third arm” for the user, extending their reach during handovers and manipulation in close-range collaborative activities. It was developed iteratively, following a user-centered design process that included an online survey, contextual inquiry, and an in-person usability study. Simulations show that the WRF significantly enhances a wearer’s reachable workspace volume, while remaining within biomechanical ergonomic load limits during typical usage scenarios. While operating the device in such scenarios, the user introduces disturbances in its pose due to their body movements. We present two methods to overcome these disturbances: autoregressive (AR) time series and a recurrent neural network (RNN). These models were used for forecasting the wearer’s body movements to compensate for disturbances, with prediction horizons determined through linear system identification. The models were trained offline on a subset of the KIT Human Motion Database, and tested in five usage scenarios to keep the 3D pose of the WRF’s end-effector static. The addition of the predictive models reduced the end-effector position errors by up to 26% compared to direct feedback control.