Predicting the Post-Impact Velocity of a Robotic Arm via Rigid Multibody Models: an Experimental Study

TL;DR

This study evaluates the accuracy of predicting post-impact velocities of a robotic arm using rigid multibody models and experimental data, demonstrating promising results for impact-aware robot control strategies.

Contribution

It introduces a methodology for quantitatively comparing recorded impact data with predictions from rigid-body models, enhancing impact prediction accuracy in robotics.

Findings

Prediction accuracy is promising for impact-aware control.

Recorded impact data and routines are publicly available.

Rigid-body models can effectively predict post-impact velocities.

Abstract

Accurate post-impact velocity predictions are essential in developing impact-aware manipulation strategies for robots, where contacts are intentionally established at non-zero speed mimicking human manipulation abilities in dynamic grasping and pushing of objects. Starting from the recorded dynamic response of a 7DOF torque-controlled robot that intentionally impacts a rigid surface, we investigate the possibility and accuracy of predicting the post-impact robot velocity from the pre-impact velocity and impact configuration. The velocity prediction is obtained by means of an impact map, derived using the framework of nonsmooth mechanics, that makes use of the known rigid-body robot model and the assumption of a frictionless inelastic impact.The main contribution is proposing a methodology that allows for a meaningful quantitative comparison between the recorded post-impact data, that exhibits a damped oscillatory response after the impact, and the post-impact velocity prediction derived via the readily available rigid-body robot model, that presents no oscillations and that is the one typically obtained via mainstream robot simulator software. The results of this new approach are promising in terms of prediction accuracy and thus relevant for the growing field of impact-aware robot control. The recorded impact data (18 experiments) is made publicly available, together with the numerical routines employed to generate the quantitative comparison, to further stimulate interest/research in this field.