Impact-friendly robust control design with task-space quadratic optimization

Yuquan Wang 1 Abderrahmane Kheddar 2, 1
1 IDH - Interactive Digital Humans
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Almost all known robots fear impacts. Unlike humans , robots keep guarded motions to near zero-velocity prior to establishing contacts with their surroundings. This significantly slows down robotic tasks involving physical interaction. Two main ingredients are necessary to remedy this limitation: impact-friendly hardware design, and impact-friendly controllers. Our work focuses on the controller aspect. Task-space controllers formulated as quadratic programming (QP) are widely used in robotics to generate modular and reactive motion for a large range of task specifications under various constraints. We explicitly introduce discrete impact dynamics model into the QP-based controllers to generate robot motions that are robust to impact-induced state jumps in the joint velocities and joint torques. Our simulations, validate that our proposed impact-friendly QP controller is robust to contact impacts, shall they be expected or not. Therefore, we can exploit it for establishing contacts with high velocities, and explicitly generate task-purpose impulsive forces.
Keywords : Impact QP control
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Conference papers
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-02301269
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Submitted on : Monday, September 30, 2019 - 12:20:30 PM
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Yuquan Wang, Abderrahmane Kheddar. Impact-friendly robust control design with task-space quadratic optimization. Robotics: Science and Systems (RSS), Jun 2019, Freiburg im Breisgau, Germany. ⟨lirmm-02301269⟩

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