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A scalable wearable AR system for manual order picking based on warehouse floor-related navigation

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Abstract

In manual order picking systems, a paper list specifying the name, location, and amount of each item is often given, and this would exert mental pressure on pickers while finding target shelf bins in high-density picking environments. Augmented reality (AR) can provide a friendly alternative to improve the manual order picking performance by conveying picking information into visual instruction. Nevertheless, there is still no systematic consensus to deploy the pick-by-AR associated with actual warehouse workplaces. To establish a spatial correspondence between the visual guidance with the actual workplace, the multi-marker-based global map about the warehouse floor is established in advance. Instead of traditional single marker-based pick-by-AR methods, the warehouse floor-related map can provide an accurate and continuous navigation performance for intuitive AR guidance, allowing the picker to move freely on the warehouse floor without limiting to some certain locations. Besides, a systematic pick-by-AR solution is available by integrating the proposed method to a lightweight wearable AR device, and this easy-to-deploy pick-by-AR solution can alleviate the worker’s mental effort while doing picking action. Finally, the pick-by-AR system described in the paper is deployed in the automobile assembly line, and experimental results illustrate that the proposed method can improve the picking efficiency while reducing the picking errors compared with the current paper-based order picking.

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Acknowledgments

Many thanks to Siyao Zheng and Zhen Liu for discussing the method, and providing the experimental conditions for this study.

Funding

This work received financial supports from the Beijing Natural Science Foundation (3204050), the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems (VRLAB2020B05), and the Fundamental Research Funds for the Central Universities (2019RC26).

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Authors and Affiliations

  1. School of Automation, Beijing University of Posts and Telecommunications, Beijing, China

    Wei Fang

  2. School of Mechanical Engineering and Automation, Beihang University, Beijing, China

    Zewu An

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  1. Wei Fang
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  2. Zewu An
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Correspondence to Wei Fang.

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Fang, W., An, Z. A scalable wearable AR system for manual order picking based on warehouse floor-related navigation. Int J Adv Manuf Technol 109, 2023–2037 (2020). https://doi.org/10.1007/s00170-020-05771-3

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  • DOI: https://doi.org/10.1007/s00170-020-05771-3

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