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Somatosensation

Somatosensation

The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.

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A High-Repeatability Three-Dimensional Force Tactile Sensing System for Robotic Dexterous Grasping and Object

Yaoguang Shi¹, Xiaozhou Lü¹, Wenran Wang¹

¹School of Aerospace Science and Technology, Xidian University, Xi'an 710071, China.

|January 8, 2025

Summary

A new flexible tactile sensor improves robotic hand accuracy and durability by reducing initial value drift. This tactile sensing system enhances object recognition and delicate manipulation capabilities for robotic dexterous hands.

Keywords:

high-repeatability feature object recognition robotic dexterous fingertip three-dimensional force tactile sensing

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Area of Science:

Robotics
Sensor Technology
Artificial Intelligence

Background:

Robotic devices utilize tactile sensors for force, pressure, and sliding detection in human-robot interaction, manipulation, and recognition.
Challenges in current tactile sensing include initial value drift, durability, and accuracy limitations, particularly for robotic dexterous hands.

Purpose of the Study:

To design a flexible tactile sensor with high repeatability to overcome initial value drift and enhance tactile detection for robotic dexterous hands.
To develop a three-dimensional (3D) force decoupling detection method for improved tactile sensing.
To achieve accurate object classification and recognition using the developed tactile sensing system.

Main Methods:

A flexible tactile sensor was designed with a supporting layer for pre-separation to improve repeatability.
A 3D force decoupling detection method was implemented by distributing sensor units on a non-coplanar robotic fingertip.
A backpropagation neural network was employed for object classification and recognition.

Main Results:

The tactile sensor demonstrated a detection range of 0-5 N, a resolution of 0.2 N, and a repeatability error of 1.5%.
Response times for loading and unloading were 80 ms and 160 ms, respectively.
Object recognition accuracy exceeded 95% for nine different object types.

Conclusions:

The developed flexible tactile sensor and 3D force decoupling method significantly improve tactile detection accuracy and durability for robotic dexterous hands.
The tactile sensing system shows promise for enhancing delicate manipulation and object recognition in robotic applications.
The high accuracy achieved in object recognition highlights the potential of this system for advanced robotic tasks.