Predicting Impact-Induced Joint Velocity Jumps on Kinematic-Controlled Manipulator
This work addresses a specific issue in robotics for enabling on-purpose impact tasks, but it is incremental as it improves upon an existing method.
The paper tackled the problem of predicting joint-velocity jumps during robotic impacts to ensure controller feasibility and hardware integrity, reducing the average prediction error by 81.98% compared to a common approach in 250 benchmark experiments with the Panda manipulator.
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.