Daniel Bruder

University of Michigan
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Articles (9)

Year:

2025

Year:

2024

Year:

2024

Soft robot arms offer safety and adaptability due to their passive compliance, but this compliance typically limits their payload capacity and prevents them from performing many tasks. This paper presents a model-based design approach to effectively increase the payload capacity of soft robot arms. The proposed approach uses localized body stiffening to decrease the compliance at the end effector without sacrificing the robot’s range of motion. This approach is validated on both a simulated and a real soft robot arm, where experiments show that increasing the stiffness of localized regions of their bodies reduces the compliance at the end effector and increases the height to which the arm can lift a payload. By increasing the payload capacity of soft robot arms, this approach has the potential to improve their efficacy in a variety of tasks including object manipulation and exploration of cluttered environments.

Year:

2023

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Collaborators (4)

C David Remy

University of Michigan

UNITED STATES
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Robert J. Wood

Harvard University

UNITED STATES
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Brent Gillespie

University of Michigan

UNITED STATES
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Ramanarayan Vasudevan

Associate Professor

University of Michigan

UNITED STATES
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