Compensating Pose Uncertainties Through Appropriate Gripper Finger Cutouts

Adam Wolniakowski, Andrej Gams, Lilita Kiforenko, Aljaz Kramberger, Dimitrios-Chrysostomos Chrysostomou, Ole Madsen, Konstantsin Miatliuk, Henrik Gordon Petersen, Frederik Hagelskjær, Anders Glent Buch, Aleš Ude, Norbert Krüger

Publikation: Bidrag til tidsskriftKonferenceartikelForskningpeer review

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Resumé

The gripper finger design is a recurring problem in many robotic grasping platforms used in industry. The task of switching the gripper configuration to accommodate for a new batch of objects typically requires engineering expertise, and is a lengthy and costly iterative trial-and-error process. One of the open challenges is the need for the gripper to compensate for uncertainties inherent to the workcell, e.g. due to errors in calibration, inaccurate pose estimation from the vision system, or object deformation. In this paper, we present an analysis of gripper uncertainty compensating capabilities in a sample industrial object grasping scenario for a finger that was designed using an automated simulation-based geometry optimization method [1, 2]. We test the developed gripper with a set of grasps subjected to structured perturbation in a simulation environment and in the real-world setting. We provide a comparison of the data obtained by using both of these approaches. We argue that the strong correspondence observed in results validates the use of dynamic simulation for the gripper finger design and optimization.
OriginalsprogEngelsk
TidsskriftActa Mechanica et Automatica
Vol/bind12
Udgave nummer1
Sider (fra-til)78-83
ISSN1898-4088
DOI
StatusUdgivet - 2018
BegivenhedThe 12th International Conference Mechatronic Systems and Materials - Bialystok, Polen
Varighed: 3. jul. 20168. jul. 2016
http://www.msm2016.pb.edu.pl/

Konference

KonferenceThe 12th International Conference Mechatronic Systems and Materials
LandPolen
ByBialystok
Periode03/07/201608/07/2016
Internetadresse

Fingeraftryk

Grippers
Uncertainty
Robotics
Calibration
Geometry
Computer simulation
Industry

Citer dette

Wolniakowski, Adam ; Gams, Andrej ; Kiforenko, Lilita ; Kramberger, Aljaz ; Chrysostomou, Dimitrios-Chrysostomos ; Madsen, Ole ; Miatliuk, Konstantsin ; Petersen, Henrik Gordon ; Hagelskjær, Frederik ; Buch, Anders Glent ; Ude, Aleš ; Krüger, Norbert. / Compensating Pose Uncertainties Through Appropriate Gripper Finger Cutouts. I: Acta Mechanica et Automatica. 2018 ; Bind 12, Nr. 1. s. 78-83.
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Compensating Pose Uncertainties Through Appropriate Gripper Finger Cutouts. / Wolniakowski, Adam; Gams, Andrej ; Kiforenko, Lilita; Kramberger, Aljaz; Chrysostomou, Dimitrios-Chrysostomos; Madsen, Ole; Miatliuk, Konstantsin; Petersen, Henrik Gordon; Hagelskjær, Frederik; Buch, Anders Glent; Ude, Aleš; Krüger, Norbert.

I: Acta Mechanica et Automatica, Bind 12, Nr. 1, 2018, s. 78-83.

Publikation: Bidrag til tidsskriftKonferenceartikelForskningpeer review

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AU - Wolniakowski, Adam

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AU - Kramberger, Aljaz

AU - Chrysostomou, Dimitrios-Chrysostomos

AU - Madsen, Ole

AU - Miatliuk, Konstantsin

AU - Petersen, Henrik Gordon

AU - Hagelskjær, Frederik

AU - Buch, Anders Glent

AU - Ude, Aleš

AU - Krüger, Norbert

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AB - The gripper finger design is a recurring problem in many robotic grasping platforms used in industry. The task of switching the gripper configuration to accommodate for a new batch of objects typically requires engineering expertise, and is a lengthy and costly iterative trial-and-error process. One of the open challenges is the need for the gripper to compensate for uncertainties inherent to the workcell, e.g. due to errors in calibration, inaccurate pose estimation from the vision system, or object deformation. In this paper, we present an analysis of gripper uncertainty compensating capabilities in a sample industrial object grasping scenario for a finger that was designed using an automated simulation-based geometry optimization method [1, 2]. We test the developed gripper with a set of grasps subjected to structured perturbation in a simulation environment and in the real-world setting. We provide a comparison of the data obtained by using both of these approaches. We argue that the strong correspondence observed in results validates the use of dynamic simulation for the gripper finger design and optimization.

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