Daniel Bruder
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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
Robert J. Wood
Harvard University
Brent Gillespie
University of Michigan
Ramanarayan Vasudevan
Associate Professor
University of Michigan
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