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

TL;DR

This paper presents a data-driven approach using neural networks to classify contact types in human-robot collaboration with parallel robots, quantifying uncertainty to improve safety and reaction strategies.

Contribution

It introduces a method for quantifying classification uncertainty in contact detection, enhancing safety responses in human-robot interactions.

Findings

Uncertainty quantification reduces dangerous misclassifications.

The approach improves reaction safety in contact scenarios.

Experimental validation shows effective distinction between safe and unsafe contacts.

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.