Neural Network-Based Shape Analysis and Control of Continuum Objects

Yuqiao Dai, Shilin Zhang, Wei Cheng, Peng Li, Yong Zhong, Pei Jiang, Sun Yi · 2024

Read Neural Network-Based Shape Analysis and Control of Continuum Objects by Yuqiao Dai, Shilin Zhang, Wei Cheng, Peng Li, Yong Zhong, Pei Jiang, Sun Yi (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11673849.txt.

Description

Yuqiao Dai leads a team on shape control of soft robots. Their work from twenty twenty-four uses neural networks for shape reading. Soft robots bend in a smooth line, unlike stiff arm robots.

To know their shape is hard, which blocks fine control. The team pulls outlines from camera views to get shape traits. A neural net learns how motor inputs map to shape output. A sim world then tests shape guesses and shape control. Results show the method can guess and copy soft shapes well. It offers a path to better soft robot control.

Catalog record

CREATORYuqiao Dai, Shilin Zhang, Wei Cheng, Peng Li, Yong Zhong, Pei Jiang, Sun Yi
YEAR2024
COLLECTIONPAPERS
FORMATTXT
FILE SIZE34 KB
VERSION1.0
LICENSECC BY 4.0
RIGHTSCC BY
SOURCEEurope PMC

Source: https://europepmc.org/articles/PMC11673849

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