Compressed Sensing for Tactile Skins

TL;DR

This paper introduces a scalable compressed sensing approach for tactile skins in robots, enabling real-time data acquisition with reduced wiring and improved accuracy over traditional methods.

Contribution

The paper presents a novel compressed sensing technique tailored for tactile sensor networks, enhancing data acquisition efficiency and accuracy in robotic tactile systems.

Findings

Compressed sensing achieves a 3:1 compression ratio.

Higher accuracy than full data acquisition of noisy data.

Potential for reducing wiring complexity in tactile skins.

Abstract

Whole body tactile perception via tactile skins offers large benefits for robots in unstructured environments. To fully realize this benefit, tactile systems must support real-time data acquisition over a massive number of tactile sensor elements. We present a novel approach for scalable tactile data acquisition using compressed sensing. We first demonstrate that the tactile data is amenable to compressed sensing techniques. We then develop a solution for fast data sampling, compression, and reconstruction that is suited for tactile system hardware and has potential for reducing the wiring complexity. Finally, we evaluate the performance of our technique on simulated tactile sensor networks. Our evaluations show that compressed sensing, with a compression ratio of 3 to 1, can achieve higher signal acquisition accuracy than full data acquisition of noisy sensor data.