journal article·2022Open access

Design of Teaching System of Industrial Robots Using Mixed Reality燭echnology

Guwei Li, Yun Yang, Zhou Li, Jingchun Fan

Computers, Materials & Continua· Volume 73· Issue 1

Abstract

Traditional teaching and learning about industrial robots uses abstract instructions, which are difficult for students to understand. Meanwhile, there are safety issues associated with the use of practical training equipment. To address these problems, this paper developed an instructional system based on mixed-reality (MR) technology for teaching about industrial robots. The Siasun T6A-series robots were taken as a case study, and the Microsoft MR device HoloLens-2 was used as the instructional platform. First, the parameters of the robots were analyzed based on their structural drawings. Then, the robot modules were decomposed, and 1:1 three-dimensional (3D) digital reproductions were created in Maya. Next, a library of digital models of the robot components was established, and a 3D spatial operation interface for the virtual instructional system was created in Unity. Subsequently, a C# code framework was established to satisfy the requirements of interactive functions and data transmission, and the data were saved in JSON format. In this way, a key technique that facilitates the understanding of spatial structures and a variety of human-machine interactions were realized. Finally, an instructional system based on HoloLens-2 was established for understanding the structures and principles of robots. The results showed that the instructional system developed in this study provides realistic 3D visualizations and a natural, efficient approach for human-machine interactions. This system could effectively improve the efficiency of knowledge transfer and the student’s motivation to learn.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Augmented Reality Applications · Virtual Reality Applications and Impacts · Teleoperation and Haptic Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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