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The manual assignment of objects to printed assembly diagrams by human operators is a labor-intensive and time-consuming task in manufacturing. Here, we present a system leveraging cutting-edge technologies to improve the efficiency of manual pick-and-place operations. Our system projects assembly diagrams onto a worktable, using deep learning and computer vision to identify objects based on their CAD representation. AR technology is then used to visualize the objects’ precise positioning in these projected plans, enabling the human operator to interact seamlessly with them.

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Accelerating Manual Pick-and-Place Operations with AR-Projected CAD Plans and AI-Assisted Object Recognition

  • 会议论文

  • 发表日期：

    2025年

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  • Raphael Seliger,
  • Matthias Micheler,
  • Sebnem Gül-Ficici,
  • Ulrich Göhner

摘要

The manual assignment of objects to printed assembly diagrams by human operators is a labor-intensive and time-consuming task in manufacturing. Here, we present a system leveraging cutting-edge technologies to improve the efficiency of manual pick-and-place operations. Our system projects assembly diagrams onto a worktable, using deep learning and computer vision to identify objects based on their CAD representation. AR technology is then used to visualize the objects’ precise positioning in these projected plans, enabling the human operator to interact seamlessly with them.