Automated acquisition of anisotropic friction

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Keno Drebel, Kenny Erleben, Paul Kry, Sheldon Andrews

Automated acquisition of friction data is an interesting approach to more successfully bridge the reality gap in simulation than conventional mathematical models. To advance this area of research, we present a novel inexpensive computer vision platform as a solution for collecting and processing friction data, and we make available the open source software and data sets collected with our vision robotic approach. This paper is focused on gathering data on anisotropic static friction behavior as this is ideal for inexpensive vision approach we propose. The data set and experimental setup provide a solid foundation for a wider robotics simulation community to conduct their own experiments.

Original languageEnglish
Title of host publicationProceedings - 2019 16th Conference on Computer and Robot Vision, CRV 2019
Number of pages7
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date2019
Pages159-165
Article number8781610
ISBN (Electronic)9781728118383
DOIs
Publication statusPublished - 2019
Event16th Conference on Computer and Robot Vision, CRV 2019 - Kingston, Canada
Duration: 29 May 201931 May 2019

Conference

Conference16th Conference on Computer and Robot Vision, CRV 2019
LandCanada
ByKingston
Periode29/05/201931/05/2019
SponsorCanadian Image Processing and Pattern Recognition Society / Association Canadienne de Traitement d�Images et de Reconnaissance des Formes (CIPPRS/ACTIRF)

    Research areas

  • Automated friction measurement, Computer vision, Friction, Robot arm, Static friction

ID: 227141468