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Adaptive Residual Filtering for Safe Human-Robot Collision Detection under Modeling Uncertainties

Abstract : This paper presents an innovative collision detection strategy for robot manipulators in the context of the human-robot interaction. Classical approaches consisting of a comparison of the applied motor torques with those provided by a dynamic model can be sensitive to model uncertainties, leading to conservative detection thresholds. In this work, a “gray-box” model is designed based on a use-case study to shape the on-line evaluation of the residuals. This approach takes into account unstructured uncertainties relative to the speed-dependent non-linearities (e.g. friction phenomena) and the acceleration, both of particular interest when dealing with highly time-varying dynamics. Taking advantage of proprioceptive measurements of the robot state, the residual is adaptively filtered regarding these model uncertainties, and the evaluation step is improved by considering a dynamic threshold. The proposed multi-variable algorithm is implemented on the CEA robot arm ASSIST and the experimental results illustrate the enhancement of the detection sensitivity.
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Contributor : Josiane Dartron Connect in order to contact the contributor
Submitted on : Tuesday, May 28, 2013 - 8:55:27 AM
Last modification on : Monday, December 13, 2021 - 9:16:26 AM




A. Caldas, Maria Makarov, M. Grossard, Pedro Rodriguez-Ayerbe. Adaptive Residual Filtering for Safe Human-Robot Collision Detection under Modeling Uncertainties. 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Jul 2013, Wollongong, Australia. pp.CD-Rom, ⟨10.1109/AIM.2013.6584178⟩. ⟨hal-00826631⟩



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