A novel teaching and training system for industrial applications based on augmented reality

Kuu-Young Young, Shu Ling Cheng*, Chun Hsu Ko, Yu Hsuan Su, Qiao Fei Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Along with the high demand on automation, more industrial robot manipulators are being introduced to factories. In addition to their manufacturing, it is also very crucial on the deployment and training of the robots. It thus solicits the development of new types of manipulative devices and methods for tackling these tasks of high complexity. To achieve effective task execution, this paper proposes a novel AR-based teaching and training system, along with several assistive strategies. The technique of augmented reality (AR), which combines virtual objects with real scenes, is used for providing visual immersion. The assistive strategies, including that of collision-free path planning, occlusion effect removal, and visual guidance, are designed to ease the user from the load during the complicated teaching process. We conduct a series of experiments for performance evaluation. Questionnaires based on the system usability scale are also administered to obtain feedback from users.

Keywords

  • assistive strategy
  • augmented reality
  • industrial robot manipulator
  • Teaching and training system

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