Research Papers

Soft Tactile Skin Using an Embedded Ionic Liquid and Tomographic Imaging

[+] Author and Article Information
Jean-Baptiste Chossat

École de Technologie Supérieure,
Montréal, QC H3C 1K3, Canada
e-mail: jean-baptiste.chossat.1@ens.etsmtl.ca

Hee-Sup Shin

Department of Mechanical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: heesups@andrew.cmu.edu

Yong-Lae Park

Robotics Institute,
School of Computer Science,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: ylpark@cs.cmu.edu

Vincent Duchaine

École de Technologie Supérieure,
Montréal, QC H3C 1K3, Canada
e-mail: vincent.duchaine@etsmtl.ca

A.vrml file generated in abaqus was converted to a.stl file using meshlab [46], and then the.stl file was imported to eidors.

Manuscript received August 29, 2014; final manuscript received December 22, 2014; published online February 27, 2015. Assoc. Editor: Aaron M. Dollar.

J. Mechanisms Robotics 7(2), 021008 (May 01, 2015) (9 pages) Paper No: JMR-14-1234; doi: 10.1115/1.4029474 History: Received August 29, 2014; Revised December 22, 2014; Online February 27, 2015

Whole-body-contact sensing will be crucial in the quest to make robots capable of safe interaction with humans. This paper describes a novel design and a fabrication method of artificial tactile sensing skin for robots. The manufacturing method described in this paper allows easy filling of a complex microchannel network with a liquid conductor (e.g., room temperature ionic liquid (RTIL)). The proposed sensing skin can detect the magnitude and location of surface contacts using electrical impedance tomography (EIT), an imaging technique mostly used in the medical field and examined recently in conjunction with sensors based on a piezoresistive polymer sheet for robotic applications. Unlike piezoresistive polymers, our IL-filled artificial skin changes its impedance in a more predictable manner, since the measured value is determined by a simple function of the microchannel geometry only, rather than complex physical phenomena. As a proof of concept, we demonstrate that our EIT artificial skin can detect surface contacts and graphically show their magnitudes and locations.

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Fig. 1

Artificial skin prototype with an embedded IL microchannel network

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Fig. 2

Example of a typical EIT acquisition protocol with eight electrodes. EIT requires eight consecutive current injection electrode pairs and 48 distinct voltage measurements.

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Fig. 3

Example of a real conductive chain path that makes a longer route than a shortest path in a piezoresistive composite

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Fig. 4

Complete skin sensor prototype. Internal lines are microchannels filled with RTIL, and black ellipses are conductive polymer patches as electrical interfaces to measurement electrodes.

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Fig. 5

Manufacturing process. (a) Cast soft electrodes using a mixture of nickel strands and CFs with liquid silicone. (b) Pour liquid silicone to cast a base sensor layer with a microchannel pattern and embed electrodes. (c) Micromachine electrodes for increased conductivity using low-power laser. (d) Spin coat liquid silicone on a mesh layer mold. (e) Laminate the base layer on the uncured mesh layer for bonding. (f) Remove the mesh-bonded base layer from the mold and inject an RTIL. (g) Pour another silicone layer for top mesh sealing.

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Fig. 6

Laser cut microchannel mold (left) and its 3D microscopic view (right) of engraved details

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Fig. 7

Mesh layer mold (top) and 3D image reconstitution (bottom) with an optodigital microscope (Olympus DSX-100). Color scale indicates height of structures after laser ablation.

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Fig. 8

Voltage pattern from the first stimulation injecting a constant current into electrode 1 and 2. Each electrode is denoted with the green bars and is placed at the channels' extremities with its acquisition order. Right legend bar represents voltage difference in the network.

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Fig. 9

Conductivity patterns with different pressure values, 35 kPa (left) and 50 kPa (right). Legend bars represents conductivity gradient (S · m−1). The coefficient μ determines the boundary of the effective pressure area (R3 at 35 kPa and R4 at 50 kPa). At 35 kPa, μ ≈ 0.4433, 0.452, 0.5048, and 0.7154 for R1 = 3 mm, R2 = 4 mm, and R3 = 5 mm, respectively, and μ ≈ 1 (i.e., σ = σ0) for Ri (i ≥ 4); At 50 kPa, μ ≈ 0.2047, 0.2171, 0.2926, 0.5935, and 0.8949 for R1 = 3 mm, R2 = 4 mm, R3 = 5 mm, and R4 = 6 mm, respectively, and μ ≈ 1 (i.e., σ = σ0) for Ri (i ≥ 5). The coefficients are calculated based on the dimensions and material properties of the prototype.

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Fig. 10

(a) Conductivity distributions with one point load (p = 35 kPa) centered at (x = −6 mm and y = −6 mm) and two point loads (p = 50 kPa) centered at (x = −12 mm and y = 12 mm) and (x = 12 mm and y = −12 mm). (b) Images of reconstructed conductivity of the loads.

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Fig. 11

Experimental setup for pressure response measurement using a high accuracy force gauge (left) and the close-up view of the sensor and the pressure tip (right)

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Fig. 12

Pressure application experiment (left) and its postprocessed estimated pressure image

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Fig. 13

matlab generated images of three distinct forces applied at the same skin position

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Fig. 14

matlab images and cross-sectional view caused by the two contact points of the force gauge end-effector with a force of 0.5 N and demonstrating skin ability to detect multiple contact location. Both images were built from the differences in resistance between the calibration matrix and the deformed skin's matrix. (The y-axis scale of both plots is not linear, since it is based on bicubic interpolation of image processing).




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