Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release

M.L.S. Lubbers; J. van Voorst; M.J. Jongeneel; Alessandro Saccon

doi:10.1109/IROS47612.2022.9982211

Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release

M.L.S. Lubbers, J. van Voorst, M.J. Jongeneel, Alessandro Saccon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

4 Downloads (Pure)

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.

Original language	English
Title of host publication	2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher	Institute of Electrical and Electronics Engineers
Pages	1541-1547
Number of pages	7
ISBN (Electronic)	:978-1-6654-7927-1
DOIs	https://doi.org/10.1109/IROS47612.2022.9982211
Publication status	Published - 26 Dec 2022
Event	2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 - Kyoto, Japan Duration: 23 Oct 2022 → 27 Oct 2022 https://iros2022.org/

Conference

Conference	2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
Abbreviated title	IROS 2022
Country/Territory	Japan
City	Kyoto
Period	23/10/22 → 27/10/22
Internet address	https://iros2022.org/

Access to Document

10.1109/IROS47612.2022.9982211

Cite this

Lubbers, M. L. S., van Voorst, J., Jongeneel, M. J., & Saccon, A. (2022). Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1541-1547). Article 9982211 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IROS47612.2022.9982211

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title = "Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release",

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.",

author = "M.L.S. Lubbers and {van Voorst}, J. and M.J. Jongeneel and Alessandro Saccon",

year = "2022",

month = dec,

day = "26",

doi = "10.1109/IROS47612.2022.9982211",

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booktitle = "2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022, IROS 2022 ; Conference date: 23-10-2022 Through 27-10-2022",

url = "https://iros2022.org/",

}

Lubbers, MLS, van Voorst, J, Jongeneel, MJ & Saccon, A 2022, Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release. in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)., 9982211, Institute of Electrical and Electronics Engineers, pp. 1541-1547, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022, Kyoto, Japan, 23/10/22. https://doi.org/10.1109/IROS47612.2022.9982211

Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release. / Lubbers, M.L.S.; van Voorst, J.; Jongeneel, M.J. et al.
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Institute of Electrical and Electronics Engineers, 2022. p. 1541-1547 9982211.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release

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AU - Saccon, Alessandro

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N2 - 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.

AB - 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.

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BT - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Impact Aware Manipulation (I.AM.) archive containing suction cup release experiments

Cite this

Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Datasets

Impact Aware Manipulation (I.AM.) archive containing suction cup release experiments

Cite this