Piecewise Affine Curvature model: a reduced-order model for soft robot-environment interaction beyond PCC

TL;DR

This paper introduces the Piecewise Affine Curvature (PAC) model, a higher-order reduced-order model for soft robots that improves accuracy in predicting deformations caused by external forces, surpassing the traditional PCC model.

Contribution

The paper proposes a novel PAC model that better captures soft robot configurations under external perturbations, validated through theoretical analysis and experiments.

Findings

PAC model reduces end-position error by up to 30% compared to PCC

Higher-order modeling improves accuracy in soft robot deformation prediction

Validated through both theoretical and experimental methods

Abstract

Soft robot are celebrated for their propensity to enable compliant and complex robot-environment interactions. Soft robotic manipulators, or slender continuum structure robots have the potential to exploit these interactions to enable new exploration and manipulation capabilities and safe human-robot interactions. However, the interactions, or perturbations by external forces cause the soft structure to deform in an infinite degree of freedom (DOF) space. To control such system, reduced order models are needed; typically models consider piecewise sections of constant curvature although external forces often deform the structure out of the constant curvature hypothesis. In this work we perform an analysis of the trade-off between computational treatability and modelling accuracy. We then propose a new kinematic model, the Piecewise Affine Curvature (PAC) which we validate theoretically and experimentally showing that this higher-order model better captures the configuration of a soft continuum body robot when perturbed by the external forces. In comparison to the current state of the art Piecewise Constant Curvature (PCC) model we demonstrate up to 30\% reduction in error for the end position of a soft continuum body robot.