Vision-Based Shape Reconstruction of Soft Continuum Arms Using a Geometric Strain Parametrization

TL;DR

This paper introduces a vision-based shape estimation method for soft continuum arms using a geometric strain parametrization, enabling accurate and efficient 3D shape sensing essential for autonomous soft robot control.

Contribution

It proposes a novel geometric strain based representation that reduces shape complexity, improving shape estimation accuracy and computational efficiency for soft continuum arms.

Findings

Achieves end-effector position accuracy less than the arm's radius.

Effectively estimates 3D shape using a finite set of strain basis functions.

Analyzes and compares multiple basis functions for optimal shape reconstruction.

Abstract

Interest in soft continuum arms has increased as their inherent material elasticity enables safe and adaptive interactions with the environment. However to achieve full autonomy in these arms, accurate three-dimensional shape sensing is needed. Vision-based solutions have been found to be effective in estimating the shape of soft continuum arms. In this paper, a vision-based shape estimator that utilizes a geometric strain based representation for the soft continuum arm's shape, is proposed. This representation reduces the dimension of the curved shape to a finite set of strain basis functions, thereby allowing for efficient optimization for the shape that best fits the observed image. Experimental results demonstrate the effectiveness of the proposed approach in estimating the end effector with accuracy less than the soft arm's radius. Multiple basis functions are also analyzed and compared for the specific soft continuum arm in use.