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.
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Notes
- 1.
The letter R denotes a revolute joint and underlining actuation. The actuated prismatic joint of the parallel robot is kept constant and is therefore not considered in the modeling.
- 2.
For the sake of readability, dependencies on \(\boldsymbol{q}\) and \(\boldsymbol{x}\) are omitted.
- 3.
Estimated matrices/vectors of dynamics effects are denoted by \(\hat{(\cdot )}\).
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This work received support by the German Research Foundation (Deutsche Forschungsgemeinschaft) under grant number 444769341.
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Mohammad, A., Muscheid, H., Schappler, M., Seel, T. (2024). Quantifying Uncertainties of Contact Classifications in a Human-Robot Collaboration with Parallel Robots. In: Piazza, C., Capsi-Morales, P., Figueredo, L., Keppler, M., Schütze, H. (eds) Human-Friendly Robotics 2023. HFR 2023. Springer Proceedings in Advanced Robotics, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-031-55000-3_10
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