Automated acquisition of anisotropic friction

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

Automated acquisition of anisotropic friction. / Dressel, Keno; Erleben, Kenny; Kry, Paul; Andrews, Sheldon.

Proceedings - 2019 16th Conference on Computer and Robot Vision, CRV 2019. IEEE, 2019. s. 159-165 8781610.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Dressel, K, Erleben, K, Kry, P & Andrews, S 2019, Automated acquisition of anisotropic friction. i Proceedings - 2019 16th Conference on Computer and Robot Vision, CRV 2019., 8781610, IEEE, s. 159-165, 16th Conference on Computer and Robot Vision, CRV 2019, Kingston, Canada, 29/05/2019. https://doi.org/10.1109/CRV.2019.00029

APA

Dressel, K., Erleben, K., Kry, P., & Andrews, S. (2019). Automated acquisition of anisotropic friction. I Proceedings - 2019 16th Conference on Computer and Robot Vision, CRV 2019 (s. 159-165). [8781610] IEEE. https://doi.org/10.1109/CRV.2019.00029

Vancouver

Dressel K, Erleben K, Kry P, Andrews S. Automated acquisition of anisotropic friction. I Proceedings - 2019 16th Conference on Computer and Robot Vision, CRV 2019. IEEE. 2019. s. 159-165. 8781610 https://doi.org/10.1109/CRV.2019.00029

Author

Dressel, Keno ; Erleben, Kenny ; Kry, Paul ; Andrews, Sheldon. / Automated acquisition of anisotropic friction. Proceedings - 2019 16th Conference on Computer and Robot Vision, CRV 2019. IEEE, 2019. s. 159-165

Bibtex

@inproceedings{16a9f11675354a5bab40d54874502f6f,
title = "Automated acquisition of anisotropic friction",
abstract = "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.",
keywords = "Automated friction measurement, Computer vision, Friction, Robot arm, Static friction",
author = "Keno Dressel and Kenny Erleben and Paul Kry and Sheldon Andrews",
year = "2019",
doi = "10.1109/CRV.2019.00029",
language = "English",
pages = "159--165",
booktitle = "Proceedings - 2019 16th Conference on Computer and Robot Vision, CRV 2019",
publisher = "IEEE",
note = "16th Conference on Computer and Robot Vision, CRV 2019 ; Conference date: 29-05-2019 Through 31-05-2019",

}

RIS

TY - GEN

T1 - Automated acquisition of anisotropic friction

AU - Dressel, Keno

AU - Erleben, Kenny

AU - Kry, Paul

AU - Andrews, Sheldon

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Automated friction measurement

KW - Computer vision

KW - Friction

KW - Robot arm

KW - Static friction

UR - http://www.scopus.com/inward/record.url?scp=85070944950&partnerID=8YFLogxK

U2 - 10.1109/CRV.2019.00029

DO - 10.1109/CRV.2019.00029

M3 - Article in proceedings

SP - 159

EP - 165

BT - Proceedings - 2019 16th Conference on Computer and Robot Vision, CRV 2019

PB - IEEE

T2 - 16th Conference on Computer and Robot Vision, CRV 2019

Y2 - 29 May 2019 through 31 May 2019

ER -

ID: 227141468