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Ardissone E, Ulrich S, Kirchheim A (2023). Design and Evaluation of an Automatic Decision System for Gripper Selection in Order Picking. Logistics Journal : Proceedings, Vol. 2023. (urn:nbn:de:0009-14-58233)

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%0 Journal Article
%T Design and Evaluation of an Automatic Decision System for Gripper Selection in Order Picking
%A Ardissone, Eleonora
%A Ulrich, Stephan
%A Kirchheim, Alice
%J Logistics Journal : Proceedings
%D 2023
%V 2023
%N 1
%@ 2192-9084
%F ardissone2023
%X Grasping objects poses a challenge in the automation of order picking due to the diverse types of objects and complex real-world scenarios, necessitating the selection of an appropriate gripper for each object type. Existing gripper selection methods focus on generalized gripper selection systems able to grasp a large variety of objects in non-cluttered scenarios for handling in industrial applications. Within this paper, a knowledge-based gripper selection method for e-grocery items in cluttered scenarios is implemented as a binary decision tree. The results are validated through empirical tests, demonstrating an overall accuracy of 90.7 %. As the percentage of true negatives is 81.6 %, it is necessary to combine grasping principles to reduce the percentage of True Negatives in the future.
%L 620
%K Automatisierung
%K Kommissionierung
%K E-Grocery
%K Greiferauswahl
%K automation
%K order picking
%K gripper selection
%R 10.2195/lj_proc_ardissone_en_202310_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-58233
%U http://dx.doi.org/10.2195/lj_proc_ardissone_en_202310_01
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Bibtex

@Article{ardissone2023,
  author = 	"Ardissone, Eleonora
		and Ulrich, Stephan
		and Kirchheim, Alice",
  title = 	"Design and Evaluation of an Automatic Decision System for Gripper Selection in Order Picking",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2023",
  volume = 	"2023",
  number = 	"1",
  keywords = 	"Automatisierung; Kommissionierung; E-Grocery; Greiferauswahl; automation; order picking; gripper selection",
  abstract = 	"Grasping objects poses a challenge in the automation of order picking due to the diverse types of objects and complex real-world scenarios, necessitating the selection of an appropriate gripper for each object type. Existing gripper selection methods focus on generalized gripper selection systems able to grasp a large variety of objects in non-cluttered scenarios for handling in industrial applications. Within this paper, a knowledge-based gripper selection method for e-grocery items in cluttered scenarios is implemented as a binary decision tree. The results are validated through empirical tests, demonstrating an overall accuracy of 90.7 {\%}. As the percentage of true negatives is 81.6 {\%}, it is necessary to combine grasping principles to reduce the percentage of True Negatives in the future.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_proc_ardissone_en_202310_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-58233"
}
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RIS

TY  - JOUR
AU  - Ardissone, Eleonora
AU  - Ulrich, Stephan
AU  - Kirchheim, Alice
PY  - 2023
DA  - 2023//
TI  - Design and Evaluation of an Automatic Decision System for Gripper Selection in Order Picking
JO  - Logistics Journal : Proceedings
VL  - 2023
IS  - 1
KW  - Automatisierung
KW  - Kommissionierung
KW  - E-Grocery
KW  - Greiferauswahl
KW  - automation
KW  - order picking
KW  - gripper selection
AB  - Grasping objects poses a challenge in the automation of order picking due to the diverse types of objects and complex real-world scenarios, necessitating the selection of an appropriate gripper for each object type. Existing gripper selection methods focus on generalized gripper selection systems able to grasp a large variety of objects in non-cluttered scenarios for handling in industrial applications. Within this paper, a knowledge-based gripper selection method for e-grocery items in cluttered scenarios is implemented as a binary decision tree. The results are validated through empirical tests, demonstrating an overall accuracy of 90.7 %. As the percentage of true negatives is 81.6 %, it is necessary to combine grasping principles to reduce the percentage of True Negatives in the future.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-58233
DO  - 10.2195/lj_proc_ardissone_en_202310_01
ID  - ardissone2023
ER  -
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Wordbib

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ISI

PT Journal
AU Ardissone, E
   Ulrich, S
   Kirchheim, A
TI Design and Evaluation of an Automatic Decision System for Gripper Selection in Order Picking
SO Logistics Journal : Proceedings
PY 2023
VL 2023
IS 1
DI 10.2195/lj_proc_ardissone_en_202310_01
DE Automatisierung; Kommissionierung; E-Grocery; Greiferauswahl; automation; order picking; gripper selection
AB Grasping objects poses a challenge in the automation of order picking due to the diverse types of objects and complex real-world scenarios, necessitating the selection of an appropriate gripper for each object type. Existing gripper selection methods focus on generalized gripper selection systems able to grasp a large variety of objects in non-cluttered scenarios for handling in industrial applications. Within this paper, a knowledge-based gripper selection method for e-grocery items in cluttered scenarios is implemented as a binary decision tree. The results are validated through empirical tests, demonstrating an overall accuracy of 90.7 %. As the percentage of true negatives is 81.6 %, it is necessary to combine grasping principles to reduce the percentage of True Negatives in the future.
ER
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Mods

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  <titleInfo>
    <title>Design and Evaluation of an Automatic Decision System for Gripper Selection in Order Picking</title>
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    <namePart type="given">Alice</namePart>
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  <abstract>Grasping objects poses a challenge in the automation of order picking due to the diverse types of objects and complex real-world scenarios, necessitating the selection of an appropriate gripper for each object type. Existing gripper selection methods focus on generalized gripper selection systems able to grasp a large variety of objects in non-cluttered scenarios for handling in industrial applications. Within this paper, a knowledge-based gripper selection method for e-grocery items in cluttered scenarios is implemented as a binary decision tree. The results are validated through empirical tests, demonstrating an overall accuracy of 90.7 %. As the percentage of true negatives is 81.6 %, it is necessary to combine grasping principles to reduce the percentage of True Negatives in the future.</abstract>
  <subject>
    <topic>Automatisierung</topic>
    <topic>Kommissionierung</topic>
    <topic>E-Grocery</topic>
    <topic>Greiferauswahl</topic>
    <topic>automation</topic>
    <topic>order picking</topic>
    <topic>e-grocery</topic>
    <topic>gripper selection</topic>
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  <identifier type="uri">http://nbn-resolving.de/urn:nbn:de:0009-14-58233</identifier>
  <identifier type="citekey">ardissone2023</identifier>
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Full Metadata

Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2023, Iss. 1
Title

Design and Evaluation of an Automatic Decision System for Gripper Selection in Order Picking (eng)

Design und Evaluation eines automatischen Entscheidungssystems für die Greiferauswahl bei der Kommissionierung (ger)
Author Eleonora Ardissone, Stephan Ulrich, Alice Kirchheim
Language eng
Abstract Grasping objects poses a challenge in the automation of order picking due to the diverse types of objects and complex real-world scenarios, necessitating the selection of an appropriate gripper for each object type. Existing gripper selection methods focus on generalized gripper selection systems able to grasp a large variety of objects in non-cluttered scenarios for handling in industrial applications. Within this paper, a knowledge-based gripper selection method for e-grocery items in cluttered scenarios is implemented as a binary decision tree. The results are validated through empirical tests, demonstrating an overall accuracy of 90.7 %. As the percentage of true negatives is 81.6 %, it is necessary to combine grasping principles to reduce the percentage of True Negatives in the future. Das Greifen von Objekten stellt aufgrund der vielfältigen Objektarten und komplexen realen Szenarien eine Herausforderung in der Automatisierung des Kommissionierens dar und erfordert die Wahl eines passenden Greifers für die jeweilige Objektart. Vorhandene Methoden zur Greiferauswahl konzentrieren sich auf Greifsysteme, die in der Lage sind, eine breite Vielfalt von Objekten in geordneten industriellen Anwendungen zu greifen. Innerhalb dieser Arbeit wird daher eine wissensbasierte Me-thode zur Greiferauswahl für E-Grocery Artikel in unordentlichen Szenarien als binärer Entscheidungsbaum implementiert. Die Ergebnisse werden durch empirische Tests validiert und zeigen eine Gesamtgenauigkeit von 90,7 %. Da der Prozentsatz der echten Negative bei 81,6 % liegt, ist es erforder-lich, Greifprinzipien zu kombinieren, um zukünftig den Prozentsatz der echten Negativwerte zu reduzieren.
Subject Automatisierung, Kommissionierung, E-Grocery, Greiferauswahl, automation, order picking, e-grocery, gripper selection
DDC 620
Rights cc-by
URN: urn:nbn:de:0009-14-58233
DOI https://doi.org/10.2195/lj_proc_ardissone_en_202310_01