Saturday, February 24

Chinese Scientists Develop Human-Like Sensory System for Robots and Prosthetics

Image credit: SCMP

A team of Chinese scientists has developed an innovative sensory system resembling a human fingertip, aiming to bring the tactile experience of touch to robots or amputees.

The cutting-edge sensor, created by researchers from SUSTech, Xidian University, and the University of Houston, can identify various textures in real time, including wool, linen, nylon, polyester, and twill, with remarkable precision.

Published in Nature Communications, the study demonstrates the sensor’s capacity to recognize 20 different textiles with 100% accuracy at a fixed sliding rate. Integrated into a prosthetic fingertip, this system holds potential for advancing sensory technologies in robotics, aiding patients using artificial limbs, enhancing haptic-based virtual reality experiences, and augmenting consumer electronics.

The sensor, affixed to a prosthetic hand’s fingertip, relays signals to a computer for analysis using machine learning techniques. Presently, the recognition outcomes are displayed on a screen.

The team’s innovative sensor detects both static pressure and high-frequency vibrations, offering a more streamlined and robust single-sensor system compared to previous models that required dual sensors and data acquisition systems.

Lead author Guo Chuanfei, a professor at SUSTech, envisions the next phase involving efforts to enable users with prosthetics to experience the sensor’s feedback.

While direct transmission of electrical signals to the brain remains a technological challenge, the team explores alternate pathways, such as transmitting signals to skin on other body parts for neural processing.

Beyond applications in robotics and prosthetics, the technology holds promise in virtual reality, potentially enabling remote tactile experiences during video calls and facilitating texture sensing for online shoppers.

Guo emphasises the interdisciplinary nature of the project, highlighting its fusion of AI, materials science, and robotics to achieve high-precision material analysis and its integration into advanced robotics.

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