Proprioceptive Sensing of Soft Tentacles with Model Based Reconstruction for Controller Optimization

TL;DR

This paper introduces a proprioceptive sensing method for soft underwater robots using embedded pressure sensors and a learning-based shape reconstruction pipeline, enabling improved control and performance assessment.

Contribution

It presents a novel embedded pressure sensor approach combined with model-based reconstruction for shape sensing in soft robots, enhancing control and performance evaluation.

Findings

Achieved shape reconstruction with less than 9% error in tip deflection.

Demonstrated successful swimming at 9.5 cm/s with robust sensing.

Compared modeling techniques to optimize pose accuracy.

Abstract

The success of soft robots in displaying emergent behaviors is tightly linked to the compliant interaction with the environment. However, to exploit such phenomena, proprioceptive sensing methods which do not hinder their softness are needed. In this work we propose a new sensing approach for soft underwater slender structures based on embedded pressure sensors and use a learning-based pipeline to link the sensor readings to the shape of the soft structure. Using two different modeling techniques, we compare the pose reconstruction accuracy and identify the optimal approach. Using the proprioceptive sensing capabilities we show how this information can be used to assess the swimming performance over a number of metrics, namely swimming thrust, tip deflection, and the traveling wave index. We conclude by demonstrating the robustness of the embedded sensor on a free swimming soft robotic squid swimming at a maximum velocity of 9.5 cm/s, with the absolute tip deflection being predicted within an error less than 9% without the aid of external sensors.