Physics guided machine learning approach to safe quasi-static impact situations in human-robot collaboration following the power and force limiting method of the ISO/TS 15066 standard

Nemanja Kovincic; Hubert Gattringer; Andreas Müller; Mathias Brandstötter

doi:10.1115/DETC2023-109517

Physics guided machine learning approach to safe quasi-static impact situations in human-robot collaboration following the power and force limiting method of the ISO/TS 15066 standard

Nemanja Kovincic
, Hubert Gattringer
, Andreas Müller
, Mathias Brandstötter

Institute of Robotics

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Original language	English
Title of host publication	19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC) / ASME International Design Engineering Technical Conferences (IDETC), August 20-23, 2023, Boston, MA
Publisher	ASME
Number of pages	10
ISBN (Electronic)	9780791887387
DOIs	https://doi.org/10.1115/DETC2023-109517
Publication status	Published - 2023

Publication series

Name	Proceedings of the ASME Design Engineering Technical Conference
Volume	10

Fields of science

203015 Mechatronics
203022 Technical mechanics
202 Electrical Engineering, Electronics, Information Engineering
202035 Robotics
203013 Mechanical engineering

JKU Focus areas

Digital Transformation

Access to Document

10.1115/DETC2023-109517

Cite this

Kovincic, N., Gattringer, H., Müller, A., & Brandstötter, M. (2023). Physics guided machine learning approach to safe quasi-static impact situations in human-robot collaboration following the power and force limiting method of the ISO/TS 15066 standard. In 19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC) / ASME International Design Engineering Technical Conferences (IDETC), August 20-23, 2023, Boston, MA Article V010T10A024 (Proceedings of the ASME Design Engineering Technical Conference; Vol. 10). ASME. https://doi.org/10.1115/DETC2023-109517

Kovincic, Nemanja ; Gattringer, Hubert ; Müller, Andreas et al. / Physics guided machine learning approach to safe quasi-static impact situations in human-robot collaboration following the power and force limiting method of the ISO/TS 15066 standard. 19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC) / ASME International Design Engineering Technical Conferences (IDETC), August 20-23, 2023, Boston, MA. ASME, 2023. (Proceedings of the ASME Design Engineering Technical Conference).

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title = "Physics guided machine learning approach to safe quasi-static impact situations in human-robot collaboration following the power and force limiting method of the ISO/TS 15066 standard",

author = "Nemanja Kovincic and Hubert Gattringer and Andreas M{\"u}ller and Mathias Brandst{\"o}tter",

year = "2023",

doi = "10.1115/DETC2023-109517",

language = "English",

series = "Proceedings of the ASME Design Engineering Technical Conference",

publisher = "ASME",

booktitle = "19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC) / ASME International Design Engineering Technical Conferences (IDETC), August 20-23, 2023, Boston, MA",

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Kovincic, N, Gattringer, H , Müller, A & Brandstötter, M 2023, Physics guided machine learning approach to safe quasi-static impact situations in human-robot collaboration following the power and force limiting method of the ISO/TS 15066 standard. in 19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC) / ASME International Design Engineering Technical Conferences (IDETC), August 20-23, 2023, Boston, MA., V010T10A024, Proceedings of the ASME Design Engineering Technical Conference, vol. 10, ASME. https://doi.org/10.1115/DETC2023-109517

Physics guided machine learning approach to safe quasi-static impact situations in human-robot collaboration following the power and force limiting method of the ISO/TS 15066 standard. / Kovincic, Nemanja; Gattringer, Hubert ; Müller, Andreas et al.
19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC) / ASME International Design Engineering Technical Conferences (IDETC), August 20-23, 2023, Boston, MA. ASME, 2023. V010T10A024 (Proceedings of the ASME Design Engineering Technical Conference; Vol. 10).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

TY - GEN

T1 - Physics guided machine learning approach to safe quasi-static impact situations in human-robot collaboration following the power and force limiting method of the ISO/TS 15066 standard

AU - Kovincic, Nemanja

AU - Gattringer, Hubert

AU - Müller, Andreas

AU - Brandstötter, Mathias

PY - 2023

Y1 - 2023

UR - https://www.scopus.com/pages/publications/85178663374

U2 - 10.1115/DETC2023-109517

DO - 10.1115/DETC2023-109517

M3 - Conference proceedings

T3 - Proceedings of the ASME Design Engineering Technical Conference

BT - 19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC) / ASME International Design Engineering Technical Conferences (IDETC), August 20-23, 2023, Boston, MA

PB - ASME

ER -

Kovincic N, Gattringer H , Müller A, Brandstötter M. Physics guided machine learning approach to safe quasi-static impact situations in human-robot collaboration following the power and force limiting method of the ISO/TS 15066 standard. In 19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC) / ASME International Design Engineering Technical Conferences (IDETC), August 20-23, 2023, Boston, MA. ASME. 2023. V010T10A024. (Proceedings of the ASME Design Engineering Technical Conference). doi: 10.1115/DETC2023-109517

Physics guided machine learning approach to safe quasi-static impact situations in human-robot collaboration following the power and force limiting method of the ISO/TS 15066 standard

Publication series

Fields of science

JKU Focus areas

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