2026-09-19T05:36:10Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/781832022-01-13T07:54:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Edward H. Adelson and Mandayam A. Srinivasan.
Jia, Xiaodan (Xiaodan Stella)
Massachusetts Institute of Technology. Dept. of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2013-03-28T18:12:03Z
2013-03-28T18:12:03Z
2012
2012
http://hdl.handle.net/1721.1/78183
829815484
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 99-111).
GelSight, namely, elastomeric sensor, is a novel tactile sensor to get the 3D information of contacting surfaces. Using GelSight, some tactile properties, such as softness and roughness, could be gained through image processing techniques. In this thesis, I implemented GelSight principle to reconstruct surface geometry of tested surfaces, based on which, the roughness comparison and lump detection experiment are conducted. Roughness of five different types of sandpapers are successfully compared using GelSight Ra value. In the lump detection experiment, a visual display for tactile information is presented. To get binary feedback of lump presence or not, a simple threshold method is introduced in this thesis. To evaluate the performance of GelSight sensor, human psychological experiments are conducted. In similar tasks, GelSight sensor outperforms humans in lump detection.
by Xiaodan (Stella) Jia.
S.M.
111 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Tactile sensing using elastomeric sensors
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="8944df87-a982-40b9-8e76-8e59c23d5984">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Tactile sensing using elastomeric sensors</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2012</PublicationDate>
<Authors>
<Author>
<DisplayName>Jia, Xiaodan (Xiaodan Stella)</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Mechanical Engineering.</Keyword>
<Abstract>GelSight, namely, elastomeric sensor, is a novel tactile sensor to get the 3D information of contacting surfaces. Using GelSight, some tactile properties, such as softness and roughness, could be gained through image processing techniques. In this thesis, I implemented GelSight principle to reconstruct surface geometry of tested surfaces, based on which, the roughness comparison and lump detection experiment are conducted. Roughness of five different types of sandpapers are successfully compared using GelSight Ra value. In the lump detection experiment, a visual display for tactile information is presented. To get binary feedback of lump presence or not, a simple threshold method is introduced in this thesis. To evaluate the performance of GelSight sensor, human psychological experiments are conducted. In similar tasks, GelSight sensor outperforms humans in lump detection.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>