Large-Scale Integrated Flexible Tactile Sensor Array for Sensitive Smart Robotic Touch

TL;DR

This paper presents a large-scale, high-resolution flexible tactile sensor array integrated with advanced computing hardware, enabling sensitive robotic touch and high-accuracy recognition of complex patterns, advancing smart robotics capabilities.

Contribution

The work introduces a 64x64 flexible tactile sensor array with record-high spatial resolution and integrates it with memristor-based computing hardware for real-time pattern recognition.

Findings

Achieved 0.9 mm spatial resolution in a flexible tactile sensor array.

Demonstrated high pressure sensitivity (~385 kPa-1) and fast response (~3 ms).

Real-time recognition of handwritten digits and Chinese calligraphy with over 97% accuracy.

Abstract

In the long pursuit of smart robotics, it has been envisioned to empower robots with human-like senses, especially vision and touch. While tremendous progress has been made in image sensors and computer vision over the past decades, the tactile sense abilities are lagging behind due to the lack of large-scale flexible tactile sensor array with high sensitivity, high spatial resolution, and fast response. In this work, we have demonstrated a 64x64 flexible tactile sensor array with a record-high spatial resolution of 0.9 mm (equivalently 28.2 pixels per inch), by integrating a high-performance piezoresistive film (PRF) with a large-area active matrix of carbon nanotube thin-film transistors. PRF with self-formed microstructures exhibited high pressure-sensitivity of ~385 kPa-1 for MWCNTs concentration of 6%, while the 14% one exhibited fast response time of ~3 ms, good linearity, broad detection range beyond 1400 kPa, and excellent cyclability over 3000 cycles. Using this fully integrated tactile sensor array, the footprint maps of an artificial honeybee were clearly identified. Furthermore, we hardware-implemented a smart tactile system by integrating the PRF-based sensor array with a memristor-based computing-in-memory chip to record and recognize handwritten digits and Chinese calligraphy, achieving high classification accuracies of 98.8% and 97.3% in hardware, respectively. The integration of sensor networks with deep learning hardware may enable edge or near-sensor computing with significantly reduced power consumption and latency. Our work could pave the road to building large-scale intelligent sensor networks for next-generation smart robotics.