Soft Robot Shape Estimation: A Load-Agnostic Geometric Method
Christian Sorensen, Marc D. Killpack
Abstract
In this paper we present a novel kinematic representation of a soft continuum robot to enable full shape estimation using a purely geometric solution. The kinematic representation involves using length varying piecewise constant curvature segments to describe the deformed shape of the robot. Based on this kinematic representation, we can use overlapping length sensors to estimate the shape of continuously deformable bodies without prior knowledge of the current loading conditions. We show an implementation that assumes one change in curvature along the length of a joint, using string potentiometers as an arc length sensor, and an orientation measurement from the tip of the continuum joint. For 56 randomized joint configurations, we estimate the shape of a 250 mm long continually deformable robot with less then 2.5 mm of average error. The average error is reported for each of the 10 different equally spaced points along the length, demonstrating the ability to accurately represent the full shape of the soft robot.
BibTeX
@inproceedings{iros2023_softrobotshapees,
title = {Soft Robot Shape Estimation: A Load-Agnostic Geometric Method},
author = {Christian Sorensen and Marc D. Killpack},
booktitle = {IROS 2023},
year = {2023}
}