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

Yuquan Wang (1) , Niels Dehio (1) , Abderrahmane Kheddar (1)

1 LIRMM | IDH - Interactive Digital Humans

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.

Mots clés: Contact modeling; Dynamics; Industrial Robots

Domaines: Robotique [cs.RO]

Yuquan Wang, Niels Dehio, Abderrahmane Kheddar. Predicting Impact-Induced Joint Velocity Jumps on Kinematic-Controlled Manipulator. IEEE Robotics and Automation Letters, 2022, 7 (3), pp.6226-6233. ⟨10.1109/LRA.2022.3167614⟩. ⟨hal-03587261⟩