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  4. Realization and validation of a collaborative automated picking system
 
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Realization and validation of a collaborative automated picking system

Citation Link: https://doi.org/10.15480/882.3131
Publikationstyp
Conference Paper
Date Issued
2020-09-23
Sprache
English
Author(s)
Rieder, Mathias  
Verbeet, Richard  
Herausgeber*innen
Kersten, Wolfgang  orcid-logo
Blecker, Thorsten  orcid-logo
Ringle, Christian M.  orcid-logo
TORE-DOI
10.15480/882.3131
TORE-URI
http://hdl.handle.net/11420/8021
First published in
Proceedings of the Hamburg International Conference of Logistics (HICL)  
Number in series
29
Start Page
521
End Page
558
Citation
Hamburg International Conference of Logistics (HICL) 29: 521-558 (2020)
Contribution to Conference
Hamburg International Conference of Logistics (HICL) 2020  
Publisher Link
https://www.epubli.de/shop/buch/Data-Science-and-Innovation-in-Supply-Chain-Management-Wolfgang-Kersten-9783753123462/106047
Publisher
epubli
Purpose: A picking system is presented ensuring order fulfilment and enabling trans-formation from manual to automated picking using a continuous learning process. It is based on Machine Learning for object detection and realized by a human-robot collaboration to meet requirements for flexibility and adaptability. A demonstrator is implemented to show cooperation and to evaluate the learning process. Methodology: The collaborative process, system architecture, and an approach for evaluation and workload balancing for order fulfilment and learning of robots during picking have already been introduced. However, a practical application is still miss-ing. A demonstrator is implemented using an agent-based architecture (JADEX) and a physical robot (UR5e) with a camera for object detection and first empirical data are evaluated. Findings: Single components of the demonstrator are already developed, but a pending task is to implement their interaction to analyze overall system perfor-mance. This work focuses on human-robot-interaction (Emergency Call), automated generation of images extended by feedback information, and training of algorithms for object detection. Requirements of human-machine interface, technical evalua-tion of image recording, and effort of algorithm training are discussed. Originality: Many approaches for automated picking assume a static range of ob-jects. However, this approach considers a changing range as well as a concept for transformation of manual to automated picking enabled by human-robot coopera-tion and automated image recording while enabling reliable order fulfilment.
Subjects
Logistics
Industry 4.0
Digitalization
Innovation
Supply Chain Management
Artificial Intelligence
Data Science
DDC Class
330: Wirtschaft
380: Handel, Kommunikation, Verkehr
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by-sa/4.0/
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