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Realization and validation of a collaborative automated picking system

In: Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 29

Author

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  • Rieder, Mathias
  • Verbeet, Richard

Abstract

Purpose: A picking system is presented ensuring order fulfilment and enabling transformation 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 missing. 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 performance. 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 evaluation of image recording, and effort of algorithm training are discussed. Originality: Many approaches for automated picking assume a static range of objects. However, this approach considers a changing range as well as a concept for transformation of manual to automated picking enabled by human-robot cooperation and automated image recording while enabling reliable order fulfilment.

Suggested Citation

  • Rieder, Mathias & Verbeet, Richard, 2020. "Realization and validation of a collaborative automated picking system," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Lo, volume 29, pages 521-558, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:228932
    DOI: 10.15480/882.3131
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    References listed on IDEAS

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    1. Rieder, Mathias & Verbeet, Richard, 2019. "Robot-human-learning for robotic picking processes," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg Int, volume 27, pages 87-114, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    2. Azadeh, K. & de Koster, M.B.M. & Roy, D., 2017. "Robotized Warehouse Systems: Developments and Research Opportunities," ERIM Report Series Research in Management ERS-2017-009-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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    Cited by:

    1. Rieder, Mathias & Breitmayer, Marius, 2022. "Object detection in picking: Handling variety of a warehouse's articles," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 67-90, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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