Selection framework of visualization methods in designing AR industrial task-support systems

Abstract

An Augmented reality (AR) task-support system incorporates head-mounted displays (HMDs) to improve human performance in maintenance, assembly, and disassembly tasks by intuitively showing the working procedure to the operator. To achieve the intended AR capability and to enable the operator to work efficiently, a system designer must understand the characteristics of AR and design the information to be shown operators for each subtask, taking into account the working situation represented by the procedure, operator performance skills, workspace, devices, and operating objects (i.e., AR information design). Especially, in the AR information design, the designer needs AR expertize to select the suitable visualization method among the many extant methods. This is difficult for designers (manual writers) to perform AR information design in factories in the future. However, at present, only a few studies have been conducted to select the suitable visualization method for each subtask. This study proposes to define 31 subtask types as criteria for decomposing tasks in AR information design. Furthermore, we classify and define 42 visualization methods in terms of AR information design. Finally, to select the suitable visualization method for each subtask, we construct a selection framework consisting of three selection categories representing the working status and 17 selection conditions. The evaluation experiments of the proposed framework could output an average of 4.3 suitable visualization method candidates, including at least one suitable visualization method selected by AR experts in the subtask working situation. By incorporating the proposed framework into a website and combining it with existing HMDs and services for AR application development, it is expected that designers will be able to develop appropriate AR task-support systems.

Topics & Concepts