Quantifying the Uncertainties-Induced Errors in Robot Impact Detection Methods

Nolwenn Briquet-Kerestedjian (1) , Maria Makarov (1) , Pedro Rodriguez-Ayerbe (1) , Mathieu Grossard (2)

1 L2S - Laboratoire des signaux et systèmes
2 LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies

In the context of human-robot collaboration, an efficient impact detection is essential for safe operation. Residual-based collision detection relies on the difference between the estimated and actual motor torques. However, in these model-based methods uncertainties affect the residual in the same structural way as a collision does, leading to potential false alarms. This paper proposes to quantify the influence of uncertainties on residual generation methods based on the inverse dynamic model for both rigid and elastic-joint robots. These uncertainties-induced errors are investigated depending on their origin (parameters estimation or numerical differentiation). Boundaries of these errors are determined along a given trajectory and account as the minimum threshold of detectability of a collision. These results are illustrated in simulation.

Domaines: Automatique / Robotique

Nolwenn Briquet-Kerestedjian, Maria Makarov, Pedro Rodriguez-Ayerbe, Mathieu Grossard. Quantifying the Uncertainties-Induced Errors in Robot Impact Detection Methods. IECON 2016 - 42nd Annual Conference of IEEE Industrial Electronics Society, Oct 2016, Florence, Italy. pp.5328-5334, ⟨10.1109/iecon.2016.7793186⟩. ⟨hal-01416124⟩