A physics-informed, vision-based method to reconstruct all deformation modes in slender bodies

TL;DR

This paper introduces a physics-informed, vision-based method for accurately reconstructing all deformation modes of slender bodies using Cosserat rod theory, applicable to biological and soft robotic structures.

Contribution

It develops a novel shape estimation approach combining physics-based modeling with vision data, enabling precise deformation reconstruction of slender flexible bodies.

Findings

Achieves less than 5 mm error in shape estimation

Demonstrates robustness to noise and uncertainties

Applicable to soft robotics and biological structures

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.