Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release
Menno Lubbers, Job van Voorst, Maarten Jongeneel, Alessandro Saccon
Abstract
Suction grippers are the most common pick-and-place end effectors used in industry. However, there is little literature on creating and validating models to predict their force interaction with objects in dynamic conditions. In this paper, we study the interaction dynamics of an active vacuum suction gripper during the vertical release of an object. Object and suction cup motions are recorded using a motion capture system. As the object's mass is known and can be changed for each experiment, a study of the object's motion can lead to an estimate of the interaction force generated by the suction gripper. We show that, by learning this interaction force, it is possible to accurately predict the object's vertical motion as a function of time. This result is the first step toward 3D motion prediction when releasing an object from a suction gripper.
BibTeX
@inproceedings{iros2022_learningsuctionc,
title = {Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release},
author = {Menno Lubbers and Job van Voorst and Maarten Jongeneel and Alessandro Saccon},
booktitle = {IROS 2022},
year = {2022}
}