Leather-based printed tactile sensor array for robotic interactive skin

Abstract

Tactile sensation serves as a crucial interface for robots to interact with humans and the environment, enabling the perception of object properties, human interaction intentions, and risks of impact and collision. In order to meet these interactive challenges, an array-type electronic skin need to possess higher performances, such as skin-like flexibility, large-area low-cost fabrication, and more importantly, wider range, higher linearity and consistency. Here, we developed a leather surface screen printing technology to fabricate a flexible fiber network pressure sensor array. The multi-layered three-dimensional conductive force-sensitive network exhibits excellent performance such as an ultra-wide range (0–4.5 MPa), high linearity (99.3 %), and high consistency (99.98 %). This leather-based tactile device could be easy to conformally attach onto the surface of robots. Additionally, using Tet-Net Convolutional Neural Network as the backbone network, combined with depthwise separable convolution, multi-scale modules, asymmetric convolutions, etc., we established a Contact Object Recognition Residual Network (COR-Net) and an Interaction Gesture Recognition Residual Network (IGR-Net) based on the attention mechanism to successfully recognize the hardness material of different objects for collision warning and judge human interaction intentions, with accuracy rates of 95 % and 98.48 %. Robots can complete object grasping, handling, and other operations according to human interaction intentions. This study exhibits the significant application potential of flexible printed leather devices in the field of human-robot interaction.