Details
| Original language | English |
|---|---|
| Pages | 137-150 |
| Number of pages | 14 |
| Publication status | Published - 10 Mar 2024 |
Abstract
Keywords
- cs.RO, cs.SY, eess.SY, parallel robots, human-robot collaboration, data-driven modeling
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Artificial Intelligence
- Engineering(all)
- Engineering (miscellaneous)
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Science Applications
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2024. 137-150.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - Quantifying Uncertainties of Contact Classifications in a Human-Robot Collaboration with Parallel Robots
AU - Mohammad, Aran
AU - Muscheid, Hendrik
AU - Schappler, Moritz
AU - Seel, Thomas
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/3/10
Y1 - 2024/3/10
N2 - In human-robot collaboration, unintentional physical contacts occur in the form of collisions and clamping, which must be detected and classified separately for a reaction. If certain collision or clamping situations are misclassified, reactions might occur that make the true contact case more dangerous. This work analyzes data-driven modeling based on physically modeled features like estimated external forces for clamping and collision classification with a real parallel robot. The prediction reliability of a feedforward neural network is investigated. Quantification of the classification uncertainty enables the distinction between safe versus unreliable classifications and optimal reactions like a retraction movement for collisions, structure opening for the clamping joint, and a fallback reaction in the form of a zero-g mode. This hypothesis is tested with experimental data of clamping and collision cases by analyzing dangerous misclassifications and then reducing them by the proposed uncertainty quantification. Finally, it is investigated how the approach of this work influences correctly classified clamping and collision scenarios.
AB - In human-robot collaboration, unintentional physical contacts occur in the form of collisions and clamping, which must be detected and classified separately for a reaction. If certain collision or clamping situations are misclassified, reactions might occur that make the true contact case more dangerous. This work analyzes data-driven modeling based on physically modeled features like estimated external forces for clamping and collision classification with a real parallel robot. The prediction reliability of a feedforward neural network is investigated. Quantification of the classification uncertainty enables the distinction between safe versus unreliable classifications and optimal reactions like a retraction movement for collisions, structure opening for the clamping joint, and a fallback reaction in the form of a zero-g mode. This hypothesis is tested with experimental data of clamping and collision cases by analyzing dangerous misclassifications and then reducing them by the proposed uncertainty quantification. Finally, it is investigated how the approach of this work influences correctly classified clamping and collision scenarios.
KW - cs.RO
KW - cs.SY
KW - eess.SY
KW - parallel robots
KW - human-robot collaboration
KW - data-driven modeling
UR - http://www.scopus.com/inward/record.url?scp=85188745666&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-55000-3_10
DO - 10.1007/978-3-031-55000-3_10
M3 - Paper
SP - 137
EP - 150
ER -