Journal article Open Access
Albini Alessandro;
Giorgio Cannata
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <identifier identifierType="URL">https://zenodo.org/record/3706665</identifier> <creators> <creator> <creatorName>Albini Alessandro</creatorName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1562-7044</nameIdentifier> <affiliation>University of Genoa</affiliation> </creator> <creator> <creatorName>Giorgio Cannata</creatorName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7932-5411</nameIdentifier> <affiliation>University of Genoa</affiliation> </creator> </creators> <titles> <title>Pressure Distribution Classification and Segmentation of Human Hands in Contact with the Robot Body</title> </titles> <publisher>Zenodo</publisher> <publicationYear>2020</publicationYear> <subjects> <subject>tactile sensing, robot skin, human robot interaction</subject> </subjects> <dates> <date dateType="Issued">2020-03-10</date> </dates> <language>en</language> <resourceType resourceTypeGeneral="Text">Journal article</resourceType> <alternateIdentifiers> <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3706665</alternateIdentifier> </alternateIdentifiers> <relatedIdentifiers> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1177/0278364920907688</relatedIdentifier> <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/collaborate_project</relatedIdentifier> </relatedIdentifiers> <rightsList> <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights> <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights> </rightsList> <descriptions> <description descriptionType="Abstract"><p>This paper deals with the problem of the recognition of human hand touch by a robot equipped with large area tactile<br> sensors covering its body. This problem is relevant in the domain of physical human-robot interaction for discriminating<br> between human and non-human contacts and to trigger and to drive cooperative tasks or robot motions, or to ensure a<br> safe interaction. The underlying assumption, used in this paper, is that voluntary physical interaction tasks involve hand<br> touch over the robot body, and therefore the capability of recognizing hand contacts is a key element to discriminate a<br> purposive human touch from other types of interaction.<br> The proposed approach is based on a geometric transformation of the tactile data, formed by pressure measurements<br> associated to a non uniform cloud of 3D points (taxels) spread over a non linear manifold corresponding to the robot<br> body, into tactile images representing the contact pressure distribution in 2D. Tactile images can be processed using<br> deep learning algorithms to recognize human hands and to compute the pressure distribution applied by the various<br> hand segments: palm and single fingers.<br> Experimental results, performed on a real robot covered with robot skin, show the effectiveness of the proposed<br> methodology. Moreover, to evaluate its robustness, various types of failures have been simulated. A further analysis<br> concerning the transferability of the system has been performed, considering contacts occurring on a different<br> sensorized robot part.</p></description> </descriptions> <fundingReferences> <fundingReference> <funderName>European Commission</funderName> <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier> <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/820767/">820767</awardNumber> <awardTitle>Co-production CeLL performing Human-Robot Collaborative AssEmbly</awardTitle> </fundingReference> </fundingReferences> </resource>
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