Session: Rising Stars of Mechanical Engineering Celebration & Showcase

Paper Number: 148625

148625 - Reconstruction of Multimodal Deformations of Soft Robots via Distributed Strain Sensing 

Proprioception of soft robots is essential to realize closed-loop control of their motions. Sensorization of soft robots with embedded shape sensing capabilities provides an alternative approach to vision-based tracking for proprioception. Existing shape sensing of sensorized soft robots mostly rely on data-driven strategies, which have limitations in their broad applicability. In this work, we present an optimization-based general framework to reconstruct 3D deformations of soft robots via distributed strain sensing. The strain sensing unit comprises a rosette pattern of interdigitated capacitive electrode design, capable of measuring plane strain states with low hysteresis. Strategic placement of a small number of soft sensing units on a soft robot provides local strain data. By developing an optimization-based framework that incorporates numerical methods such as the method of moving asymptotes and dimension reduction, we have achieved high-accuracy (<5% maximum normalized displacement error) shape reconstruction of multimodal deformations of soft robots, including bending, elongation, twisting, and hybrid modes, as demonstrated through numerical modeling and experimental validation.

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The abstract above is repeated to meet 400 words min requirement: 
Proprioception of soft robots is essential to realize closed-loop control of their motions. Sensorization of soft robots with embedded shape sensing capabilities provides an alternative approach to vision-based tracking for proprioception. Existing shape sensing of sensorized soft robots mostly rely on data-driven strategies, which have limitations in their broad applicability. In this work, we present an optimization-based general framework to reconstruct 3D deformations of soft robots via distributed strain sensing. The strain sensing unit comprises a rosette pattern of interdigitated capacitive electrode design, capable of measuring plane strain states with low hysteresis. Strategic placement of a small number of soft sensing units on a soft robot provides local strain data. By developing an optimization-based framework that incorporates numerical methods such as the method of moving asymptotes and dimension reduction, we have achieved high-accuracy (<5% maximum normalized displacement error) shape reconstruction of multimodal deformations of soft robots, including bending, elongation, twisting, and hybrid modes, as demonstrated through numerical modeling and experimental validation.

The abstract above is repeated to meet 400 words min requirement: 
Proprioception of soft robots is essential to realize closed-loop control of their motions. Sensorization of soft robots with embedded shape sensing capabilities provides an alternative approach to vision-based tracking for proprioception. Existing shape sensing of sensorized soft robots mostly rely on data-driven strategies, which have limitations in their broad applicability. In this work, we present an optimization-based general framework to reconstruct 3D deformations of soft robots via distributed strain sensing. The strain sensing unit comprises a rosette pattern of interdigitated capacitive electrode design, capable of measuring plane strain states with low hysteresis. Strategic placement of a small number of soft sensing units on a soft robot provides local strain data. By developing an optimization-based framework that incorporates numerical methods such as the method of moving asymptotes and dimension reduction, we have achieved high-accuracy (<5% maximum normalized displacement error) shape reconstruction of multimodal deformations of soft robots, including bending, elongation, twisting, and hybrid modes, as demonstrated through numerical modeling and experimental validation.

Presenting Author: Hangbo Zhao University of Southern California

Presenting Author Biography: Dr. Hangbo Zhao is an assistant professor in the Department of Aerospace and Mechanical Engineering and the Afred E. Mann Department of Biomedical Engineering at the University of Southern California, working on micro- and nanomanufacturing, and bio-integrated electronics. Prior to joining USC, he was a postdoctoral researcher at Northwestern University. He received his M.S. and Ph.D. degrees in Mechanical Engineering at MIT, and his bachelor’s degree at Tsinghua University in China. Dr. Zhao has received several awards including the U.S. Office of Naval Research Young Investigator Award, the Society of Manufacturing Engineers (SME) Outstanding Young Manufacturing Engineer Award, and the ASME Haythornthwaite Foundation Young Investigator Award.

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