Minimal AR: visual asset optimization for the authoring of augmented reality work instructions in manufacturing

This work investigates the possibility of using a novel minimal AR authoring approach to optimize the visual assets used in augmented reality (AR) interfaces to convey work instructions in manufacturing. In the literature, there are no widely supported guidelines for the optimal choice of visual assets (e.g., CAD models, drawings, and videos). Therefore, to avoid the risk of having AR technical documentation based only on the author's preference, our work proposes a novel authoring approach that enforces the minimal amount of information to accomplish a task. Minimal AR was tested through a simulated AR LEGO-based assembly task. The performance (completion time, mental workload, errors) of 40 users was evaluated with 4 combinations of visual assets in 4 tasks with an increasing amount of information needed. The main result is that visual assets with an excess of information do not significantly increase performance. Therefore, the location of a specified object should be minimally authored by an auxiliary model (e.g., a circle and an arrow). For identifying an object within a couple, color coding is preferred to using additional visual assets. If more than two objects must be identified, a drawing visual asset is also needed. Only when the orientation of a selected object must be conveyed, animated product models are required. These insights could be helpful for an optimal design of AR work instructions in a wide range of industrial fields.

Automated summary

This study investigates the use of minimal visual assets in augmented reality interfaces for work instructions in manufacturing. The results show that excessive information in visual assets does not significantly improve performance. Different approaches, such as auxiliary models, color coding, drawings, and animated product models, are suggested based on the specific task requirements in a wide range of industrial fields.