A Single‐Channel Tactile‐Slip Triboelectric Nanogenerator for the Intelligent Performance Evaluation of Humanoid Robots

Abstract

Abstract With the rapid advancement of humanoid robot technology, precise evaluation of its comprehensive performance is crucial for ensuring stable operation in applications. Here, a multimodal humanoid robot performance evaluation system based on a single‐channel tactile‐sliding triboelectric nanogenerator (SCTS‐TENG) is proposed. SCTS‐TENG enables 1D tactile position recognition using the skewness (SK) of the single‐channel signal, pressure perception mimicking human skin during sliding, and direction identification. Inspired by the process of ancient silk trade, a performance testing system for humanoid robots is constructed using the SCTS‐TENG and a camera. Testing specifications for rotational contact of the dexterous hand and contact‐sliding‐separation of the robotic arm are defined, and an in‐depth analysis is conducted on output signal characteristics under different influencing variables. The SCTS‐TENG signals are converted into 2D images using the gramian angular difference field (GADF) method, achieving 97.22% accuracy when used as a standalone modality. This is further fused with camera images, which achieved 75% accuracy as a standalone modality, to develop a multimodal deep learning (DL) system based on the Visual Geometry Group (VGG)19 model. This fusion improves the recognition accuracy to 99.03%. This system provides a low‐cost, self‐powered, and high‐precision solution for sensing and evaluation of humanoid robots.