A physics-informed, vision-based method to reconstruct all deformation modes in slender bodies
Abstract
This paper is concerned with the problem of estimating (interpolating and smoothing) the shape (pose and the six modes of deformation) of a slender flexible body from multiple camera measurements. This problem is important in both biology, where slender, soft, and elastic structures are ubiquitously encountered across species, and in engineering, particularly in the area of soft robotics. The proposed mathematical formulation for shape estimation is physics-informed, based on the use of the special Cosserat rod theory whose equations encode slender body mechanics in the presence of bending, shearing, twisting and stretching. The approach is used to derive numerical algorithms which are experimentally demonstrated for fiber reinforced and cable-driven soft robot arms. These experimental demonstrations show that the methodology is accurate (<5 mm error, three times less than the arm diameter) and robust to noise and uncertainties.
Cite
@article{arxiv.2109.08372,
title = {A physics-informed, vision-based method to reconstruct all deformation modes in slender bodies},
author = {Seung Hyun Kim and Heng-Sheng Chang and Chia-Hsien Shih and Naveen Kumar Uppalapati and Udit Halder and Girish Krishnan and Prashant G. Mehta and Mattia Gazzola},
journal= {arXiv preprint arXiv:2109.08372},
year = {2026}
}
Comments
This work has been submitted to the IEEE RA-L with ICRA 2022 for possible publication. For associated data and code, see https://github.com/GazzolaLab/BR2-vision-based-smoothing