Quantifying Uncertainties of Contact Classifications in a Human-Robot Collaboration with Parallel Robots

verfasst von

Aran Mohammad, Hendrik Muscheid, Moritz Schappler, Thomas Seel

Abstract

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.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Typ

Paper

Seiten

137-150

Anzahl der Seiten

14

Publikationsdatum

10.03.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Maschinenbau, Artificial intelligence, Ingenieurwesen (sonstige), Angewandte Mathematik, Elektrotechnik und Elektronik, Steuerungs- und Systemtechnik, Angewandte Informatik

Elektronische Version(en)

https://doi.org/10.1007/978-3-031-55000-3_10 (Zugang: Eingeschränkt)