Scott-Russel Linkage-Based Triboelectric Self-Powered Sensor for Contact Material-Independent Force Sensing and Tactile Recognition

Abstract

The rapid growth of Internet of Things (IoT) in recent years has increased demand for various sensors to collect a wide range of data. Among various sensors, the demand for force sensors that can recognize physical phenomena in 3D space has notably increased. Recent research has focused on developing energy harvesting methods for sensors to address their maintenance problems. Triboelectric nanogenerator (TENG) based force sensors are a promising solution for converting external motion into electrical signals. However, conventional TENG-based force sensors that use the signal peak can negatively affect data accuracy. In this study, a Scott-Russell linkage-inspired TENG (SRI-TENG) is developed. The SRI-TENG has completely separate signal generation and measurement sections, and the number of peaks in the electrical output is measured to prevent disturbing output signals. In addition, the lubricant liquid enhances durability, enabling stable force signal measurements for 270 000 cycles. The SRI system demonstrates consistent peak counts and high accuracy across different contacting surfaces, indicating that it can function as a contact material-independent self-powered force sensor. Furthermore, using a deep learning method, it is demonstrated that it can function as a multimodal sensor by realizing the tactile properties of various materials. IoT is growing rapidly through the convergence of artificial intelligence, and force sensors are essential for 3D space applications. Conventional TENG-based force sensors suffer accuracy issues depending on contact materials. By introducing the Scott-Russell linkage, SRI-TENG separates signal generation and measurement, improving accuracy. Lubricant enhances durability, enabling stable measurements for 270 000 cycles. Also, utilizing deep learning SRI-TENG can recognize tactile properties. image