Towards Multidimensional Textural Perception and Classification Through
Whisker
- URL: http://arxiv.org/abs/2209.03750v1
- Date: Thu, 1 Sep 2022 11:14:17 GMT
- Title: Towards Multidimensional Textural Perception and Classification Through
Whisker
- Authors: Prasanna Kumar Routray, Aditya Sanjiv Kanade, Pauline Pounds,
Manivannan Muniyandi
- Abstract summary: Whisker-based multidimensional surface texture data is missing in the literature.
We present a novel sensor design to acquire multidimensional texture information.
We experimentally validate that the sensor can classify texture with roughness as low as $2.5mu m$ at an accuracy of $90%$ or more.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Texture-based studies and designs have been in focus recently. Whisker-based
multidimensional surface texture data is missing in the literature. This data
is critical for robotics and machine perception algorithms in the
classification and regression of textural surfaces. In this study, we present a
novel sensor design to acquire multidimensional texture information. The
surface texture's roughness and hardness were measured experimentally using
sweeping and dabbing. Three machine learning models (SVM, RF, and MLP) showed
excellent classification accuracy for the roughness and hardness of surface
textures. We show that the combination of pressure and accelerometer data,
collected from a standard machined specimen using the whisker sensor, improves
classification accuracy. Further, we experimentally validate that the sensor
can classify texture with roughness depths as low as $2.5\mu m$ at an accuracy
of $90\%$ or more and segregate materials based on their roughness and
hardness. We present a novel metric to consider while designing a whisker
sensor to guarantee the quality of texture data acquisition beforehand. The
machine learning model performance was validated against the data collected
from the laser sensor from the same set of surface textures. As part of our
work, we are releasing two-dimensional texture data: roughness and hardness to
the research community.
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