Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3131
Publisher URL: https://www.epubli.de/shop/buch/Data-Science-and-Innovation-in-Supply-Chain-Management-Wolfgang-Kersten-9783753123462/106047
Title: Realization and validation of a collaborative automated picking system
Language: English
Authors: Rieder, Mathias 
Verbeet, Richard 
Editor: Kersten, Wolfgang 
Blecker, Thorsten 
Ringle, Christian M.  
Keywords: Logistics;Industry 4.0;Digitalization;Innovation;Supply Chain Management;Artificial Intelligence;Data Science
Issue Date: 23-Sep-2020
Publisher: epubli
Source: Hamburg International Conference of Logistics (HICL) 29: 521-558 (2020)
Part of Series: Proceedings of the Hamburg International Conference of Logistics (HICL) 
Volume number: 29
Abstract (english): 
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.
Conference: Hamburg International Conference of Logistics (HICL) 2020 
URI: http://hdl.handle.net/11420/8021
DOI: 10.15480/882.3131
ISBN: 978-3-753123-46-2
ISSN: 2365-5070
Document Type: Chapter/Article (Proceedings)
License: CC BY-SA 4.0 (Attribution-ShareAlike 4.0) CC BY-SA 4.0 (Attribution-ShareAlike 4.0)
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