A Laser-Induced Graphene-Based Flexible Multimodal Sensor for Material and Texture Perception

Humans can perceive and interact with their surroundings through multiple senses. For intelligent robots, multimodal sensors are crucial for them to perceive and understand the environment. In this work, we propose a multi-layered flexible multimodal sensor based on laser-induced graphene, capable of detecting both touchless signals (such as the distance from external objects and their material) and tactile signals (three-dimensional force). The sensor has advantages in durability and stability. Under normal force, the sensitivity is -7.449% N^-1 in the range of 0 N to 1.5 N and -0.273% N^-1 in the range of 1.5 N to 30 N, with fast response (17 ms) and recovery (37 ms). Furthermore, using the Convolutional Neural Networks (CNN) model, we develop an intelligent soft robot system capable of distinguishing objects of different materials and fabric textures with accuracies of 99% and 88.75%, respectively. The proposed flexible multimodal sensor holds a significant effect on the perception and interaction of intelligent robots with the environment.