A method of tactile detection
By arranging electrodes and flexible materials on the surface of mechanical devices and combining them with neural network deep learning, the problem of multiple sensing capabilities of traditional sensors in unstructured scenarios has been solved. This enables accurate identification of touch, temperature, humidity, and damage, and is suitable for touch detection in unstructured scenarios.
Patent Information
- Application Number
- CN202610316674.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-16
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional sensors struggle to detect various types of information, such as temperature, humidity, force, and object texture. Furthermore, spatial limitations make it difficult to stack different types of sensors, thus failing to meet the human tactile perception needs in unstructured scenarios.
Using a flexible material with certain electrical impedance characteristics, multiple electrodes are arranged and connected to the control and analysis system through an electrical signal acquisition and transmission module. Combined with neural network deep learning, it can identify tactile feedback from external stimuli.
It achieves accurate recognition of external touch, temperature and humidity, limb movements and injuries, is easy to integrate into the surface of mechanical devices, and improves recognition accuracy through deep learning.
Smart Images
Figure CN122408834A_ABST