Adaptive Tracking Control of Soft Robots using Integrated Sensing Skin and Recurrent Neural Networks

TL;DR

This paper presents a novel integrated sensing and control approach for soft robots, utilizing a stretchable piezoresistive skin and recurrent neural networks to estimate curvature and enable adaptive tracking in real-time.

Contribution

It introduces a new sensing skin and neural network-based estimation method, along with an adaptive controller for improved soft robot proprioception and control.

Findings

Successful curvature estimation using the sensing skin

Effective adaptive control for dynamic uncertainties

Experimental validation of curvature tracking

Abstract

In this paper, we study integrated estimation and control of soft robots. A significant challenge in deploying closed loop controllers is reliable proprioception via integrated sensing in soft robots. Despite the considerable advances accomplished in fabrication, modelling, and model-based control of soft robots, integrated sensing and estimation is still in its infancy. To that end, this paper introduces a new method of estimating the degree of curvature of a soft robot using a stretchable sensing skin. The skin is a spray-coated piezoresistive sensing layer on a latex membrane. The mapping from the strain signal to the degree of curvature is estimated by using a recurrent neural network. We investigate uni-directional bending as well as bi-directional bending of a single-segment soft robot. Moreover, an adaptive controller is developed to track the degree of curvature of the soft robot in the presence of dynamic uncertainties. Subsequently, using the integrated soft sensing skin, we experimentally demonstrate successful curvature tracking control of the soft robot.