Force and Speed in a Soft Stewart Platform

Many soft robots struggle to produce dynamic motions with fast, large displacements. We develop a parallel 6 degree-of-freedom (DoF) Stewart-Gough mechanism using Handed Shearing Auxetic (HSA) actuators. By using soft actuators, we are able to use one third as many mechatronic components as a rigid Stewart platform, while retaining a working payload of 2kg and an open-loop bandwidth greater than 16Hz. We show that the platform is capable of both precise tracing and dynamic disturbance rejection when controlling a ball and sliding puck using a Proportional Integral Derivative (PID) controller. We develop a machine-learning-based kinematics model and demonstrate a functional workspace of roughly 10cm in each translation direction and 28 degrees in each orientation. This 6DoF device has many of the characteristics associated with rigid components—power, speed, and total workspace— while capturing the advantages of soft mechanisms.

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Publication Details

Published
2025-04-22
DOI
https://doi.org/10.1109/robosoft63089.2025.11020875
Citations
3
Primary Topic
Robotic Mechanisms and Dynamics
Type
article
Field-Weighted Citation Impact
3.64

Funders

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article

Force and Speed in a Soft Stewart Platform

Taekyoung Kim, Millicent Schlafly, James Avtges, Ryan L. Truby et al.
3 citations
Robotic Mechanisms and Dynamics
3.64
article

Force and Speed in a Soft Stewart Platform

Taekyoung Kim, Millicent Schlafly, James Avtges, Ryan L. Truby, Todd D. Murphey, Helena Young, J. Ketchum
article en
3 citations

Abstract

Many soft robots struggle to produce dynamic motions with fast, large displacements. We develop a parallel 6 degree-of-freedom (DoF) Stewart-Gough mechanism using Handed Shearing Auxetic (HSA) actuators. By using soft actuators, we are able to use one third as many mechatronic components as a rigid Stewart platform, while retaining a working payload of 2kg and an open-loop bandwidth greater than 16Hz. We show that the platform is capable of both precise tracing and dynamic disturbance rejection when controlling a ball and sliding puck using a Proportional Integral Derivative (PID) controller. We develop a machine-learning-based kinematics model and demonstrate a functional workspace of roughly 10cm in each translation direction and 28 degrees in each orientation. This 6DoF device has many of the characteristics associated with rigid components—power, speed, and total workspace— while capturing the advantages of soft mechanisms.

Northwestern University (US)
Office of Naval Research, Army Research Office
Openalex Percentile: Top 7%
Robotic Mechanisms and Dynamics
3.64
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