Next Article in Journal
Contextualizing Human—Automated Vehicle Interactions: A Socio-Ecological Framework
Previous Article in Journal
Nonlinear Model Predictive Horizon for Optimal Trajectory Generation
 
 
Article

The Wearable Robotic Forearm: Design and Predictive Control of a Collaborative Supernumerary Robot

Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850, USA
*
Author to whom correspondence should be addressed.
Current affiliation: Tata Consultancy Services, Bengaluru 560066, India.
Academic Editor: Fernando Torres
Robotics 2021, 10(3), 91; https://doi.org/10.3390/robotics10030091
Received: 13 June 2021 / Revised: 12 July 2021 / Accepted: 13 July 2021 / Published: 16 July 2021
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. View Full-Text
Keywords: wearable robotics; human augmentation; robot design and control wearable robotics; human augmentation; robot design and control
Show Figures

Figure 1

MDPI and ACS Style

Vatsal, V.; Hoffman, G. The Wearable Robotic Forearm: Design and Predictive Control of a Collaborative Supernumerary Robot. Robotics 2021, 10, 91. https://doi.org/10.3390/robotics10030091

AMA Style

Vatsal V, Hoffman G. The Wearable Robotic Forearm: Design and Predictive Control of a Collaborative Supernumerary Robot. Robotics. 2021; 10(3):91. https://doi.org/10.3390/robotics10030091

Chicago/Turabian Style

Vatsal, Vighnesh, and Guy Hoffman. 2021. "The Wearable Robotic Forearm: Design and Predictive Control of a Collaborative Supernumerary Robot" Robotics 10, no. 3: 91. https://doi.org/10.3390/robotics10030091

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop