Rieder, MathiasMathiasRiederVerbeet, RichardRichardVerbeet2020-12-012020-12-012020-09-23Hamburg International Conference of Logistics (HICL) 29: 521-558 (2020)http://hdl.handle.net/11420/8021Purpose: 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.enhttps://creativecommons.org/licenses/by-sa/4.0/LogisticsIndustry 4.0DigitalizationInnovationSupply Chain ManagementArtificial IntelligenceData ScienceWirtschaftHandel, Kommunikation, VerkehrConference Paper10.15480/882.3131https://www.epubli.de/shop/buch/Data-Science-and-Innovation-in-Supply-Chain-Management-Wolfgang-Kersten-9783753123462/10604710.15480/882.3131Kersten, WolfgangWolfgangKerstenBlecker, ThorstenThorstenBleckerRingle, Christian M.Christian M.RingleConference Paper