Design of a Biomimetic Tactile Sensor for Material Classification
ICRA• 2022
Abstract
Tactile sensing typically involves active exploration of unknown surfaces and
objects, making it especially effective at processing the characteristics of
materials and textures. A key property extracted by human tactile perception is
surface roughness, which relies on measuring vibratory signals using the
multi-layered fingertip structure. Existing robotic systems lack tactile
sensors that are able to provide high dynamic sensing ranges, perceive material
properties, and maintain a low hardware cost. In this work, we introduce the
reference design and fabrication procedure of a miniature and low-cost tactile
sensor consisting of a biomimetic cutaneous structure, including the artificial
fingerprint, dermis, epidermis, and an embedded magnet-sensor structure which
serves as a mechanoreceptor for converting mechanical information to digital
signals. The presented sensor is capable of detecting high-resolution magnetic
field data through the Hall effect and creating high-dimensional time-frequency
domain features for material texture classification. Additionally, we
investigate the effects of different superficial sensor fingerprint patterns
for classifying materials through both simulation and physical experimentation.
After extracting time series and frequency domain features, we assess a
k-nearest neighbors classifier for distinguishing between different materials.
The results from our experiments show that our biomimetic tactile sensors with
fingerprint ridges can classify materials with more than 8% higher accuracy and
lower variability than ridge-less sensors. These results, along with the low
cost and customizability of our sensor, demonstrate high potential for lowering
the barrier to entry for a wide array of robotic applications, including
model-less tactile sensing for texture classification, material inspection, and
object recognition.