Wireless and Flexible Tactile Sensing Array Based on an Adjustable Resonator with Machine‐Learning Perception

Abstract

Abstract Intelligent soft robotics and wearable electronics require flexible, wireless radio frequency (RF) pressure sensors for human‐like tactile perception of their moving parts. Existing devices face two challenges for array extension: the construction of sensitive units over a limited area and the handling of resonant peaks overlapping within the channel width. Herein, a simply adjustable RF‐resonator‐based tactile array (RFTA) is reported, in which the initial frequency of each resonator unit is regulated by doping polydimethylsiloxane (PDMS) dielectric layers with various concentrations of multiwalled carbon nanotubes (MWCNTs). An array is constructed using four sensor units with a frequency interval of 15 MHz and a multi‐layer micropyramid structure is employed to obtain a low detection limit (<1 Pa) and high sensitivity (17.49 MHz kPa‐1 in the low‐pressure range). A machine‐learning‐based strategy identifies tactile positions precisely via a one‐time S11 reading, achieving 98.5% accuracy with six stimulation modes. Furthermore, the RFTA distinguishes six objects during the grasping process when installed on a soft manipulator. The device shows considerable potential to be extended for flexible moving scenarios and high‐integrated tactile sensing systems for soft robotics.

Keywords

• adjustable frequency
• machine learning
• radio‐frequency resonators
• tactile sensors