Towards vision-based robotic skins: a data-driven, multi-camera tactile sensor
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Date
2020
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
This paper describes the design of a multi-camera optical tactile sensor that provides information about the contact force distribution applied to its soft surface. This information is contained in the motion of spherical particles spread within the surface, which deforms when subject to force. The small embedded cameras capture images of the different particle patterns that are then mapped to the three-dimensional contact force distribution through a machine learning architecture. The design proposed in this paper exhibits a larger contact surface and a thinner structure than most of the existing camera-based tactile sensors, without the use of additional reflecting components such as mirrors. A modular implementation of the learning architecture is discussed that facilitates the scalability to larger surfaces such as robotic skins. © 2020 IEEE.
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Publication status
published
Editor
Book title
2020 3rd IEEE International Conference on Soft Robotics (RoboSoft)
Journal / series
Volume
Pages / Article No.
333 - 338
Publisher
IEEE
Event
3rd IEEE International Conference on Soft Robotics (RoboSoft 2020) (virtual)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03758 - D'Andrea, Raffaello / D'Andrea, Raffaello
Notes
Due to the Corona virus (COVID-19) the conference was conducted virtually.