A Neural Network-based Framework for Fast and Smooth Posture Reconstruction of a Soft Continuum Arm

TL;DR

This paper introduces a neural network framework that efficiently reconstructs smooth postures of soft continuum arms from noisy measurements, significantly reducing computation time while maintaining accuracy, applicable to various soft robotic systems.

Contribution

It presents a novel neural network-based method for fast, smooth posture reconstruction of soft arms, overcoming computational challenges of previous approaches.

Findings

Five orders of magnitude faster reconstruction time

Comparable accuracy to traditional methods

Validated on simulated and physical soft robotic arms

Abstract

A neural network-based framework is developed and experimentally demonstrated for the problem of estimating the shape of a soft continuum arm (SCA) from noisy measurements of the pose at a finite number of locations along the length of the arm. The neural network takes as input these measurements and produces as output a finite-dimensional approximation of the strain, which is further used to reconstruct the infinite-dimensional smooth posture. This problem is important for various soft robotic applications. It is challenging due to the flexible aspects that lead to the infinite-dimensional reconstruction problem for the continuous posture and strains. Because of this, past solutions to this problem are computationally intensive. The proposed fast smooth reconstruction method is shown to be five orders of magnitude faster while having comparable accuracy. The framework is evaluated on two testbeds: a simulated octopus muscular arm and a physical BR2 pneumatic soft manipulator.