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arXiv · 2202.12646

Predicting Impact-Induced Joint Velocity Jumps on Kinematic-Controlled Manipulator

Yuquan Wang·Niels Dehio·Abderrahmane Kheddar
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Abstract

In order to enable on-purpose robotic impact tasks, predicting joint-velocity jumps is essential to enforce controller feasibility and hardware integrity. We observe a considerable prediction error of a commonly-used approach in robotics compared against 250 benchmark experiments with the Panda manipulator. We reduce the average prediction error by 81.98% as follows: First, we focus on task-space equations without inverting the ill-conditioned joint-space inertia matrix. Second, before the impact event, we compute the equivalent inertial properties of the end-effector tip considering that a high-gains (stiff) kinematic-controlled manipulator behaves like a composite-rigid body.

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Yuquan Wang, Niels Dehio, Abderrahmane Kheddar. 2022-02-25. Predicting Impact-Induced Joint Velocity Jumps on Kinematic-Controlled Manipulator. https://doi.org/10.1109/lra.2022.3167614

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