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

TL;DR

This paper presents a novel method for accurately predicting joint velocity jumps during impact events in kinematic-controlled manipulators, significantly reducing prediction errors and enhancing control reliability.

Contribution

It introduces a new approach that avoids inverting ill-conditioned matrices and models the end-effector as a composite rigid body for better impact prediction accuracy.

Findings

81.98% reduction in prediction error

Effective for high-gain kinematic-controlled manipulators

Validated on 250 benchmark experiments

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.