Superior Flexible Tactile Sensor With AI-Based Unit Microstructure Design for Human and Robot Parameters Monitoring Application

Abstract

Due to the development of wearable monitoring systems and the gradually expanding application demand, research interest has turned to free-form microstructures design capable of realizing superior and stable piezoresistive responses. The deep learning methods can accelerate and improve microstructure design with data-driven precision and increased efficiency. In this study, the concept of programmable microstructure is introduced to digitalize microstructure unit, and convolutional neural network (CNN) model and Tabu-Search algorithm are, respectively, used to predict and search iteratively novel stress-voltage (S-V) responses from the finite element modeling (FEM) training set. Therefore, efficient exploration of parameter space and faster generating of novel microstructure designs can be achieved by the proposed algorithm framework, and the optimized microstructure has also been experimentally validated. Moreover, the working mechanism of the sensing medium layer is explicated by the resistance evolution of Ti2CTx-MXene atomic layers under the compressive strain via density functional theory (DFT) calculations. The proposed artificial intelligence (AI)-based tactile sensor exhibits high sensitivity, large linearity, fast response, and excellent cycling stability, and it leverages strong capabilities to detect human being’s subtle activities and terrain perception of snake robot. This study could achieve a higher degree in human physiological monitoring and robot tactile construction and gain a new insight for digital microstructure in sensor design.