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.
End-Effector Stabilization of a Wearable Robotic Arm Using Time Series Modeling of Human Disturbances
For a wearable robotic arm to autonomously assist a human, it has to be able to stabilize its end-effector in light of the human’s independent activities. This paper presents a method for stabilizing the end-effector in planar assembly and pick-and-place tasks. Ideally, given an accurate positioning of the end effector and the wearable robot attachment point, human disturbances could be compensated by using a simple feedback control strategy. Realistically, system delays in both sensing and actuation suggest a predictive approach. In this work, we characterize the actuators of a wearable robotic arm and estimate these delays using linear models. We then consider the motion of the human arm as an autoregressive process to predict the deviation in the robot’s base position at a time horizon equivalent to the estimated delay. Generating set points for the end-effector using this predictive model, we report reduced position errors of 19.4% (x) and 20.1% (y) compared to a feedback control strategy without prediction.
- Award ID(s):
- Publication Date:
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- The ASME 2019 Dynamic Systems and Control Conference (DSCC)
- Sponsoring Org:
- National Science Foundation
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