PhD Researcher

Yuan Liu

Project based at

Lead Partner Organisation

Augmented Reality in Collaborative Robotics

Start: 1st May 2023

Expected end date:1st November 2026

This project explores how Augmented Reality (AR) can enhance human decision making in human-robot collaborative (HRC) tasks. In dynamic and complex manufacturing environments, human decision-making plays a crucial role in overall performance. AR offers several benefits, including:

• Integrating human decision-making considerations in the early design stages of HRC.
• Providing real-time visualisation, interactive feedback, and an intuitive user interface.
• Enabling design based on real-world manufacturing environments.

By leveraging AR, human workers and decision-makers can better understand how collaborative robots impact their workspace, workflows, and safety. A human-centered AR design approach can improve the acceptance of collaborative robots and foster innovative ideas to enhance human decision-making support. This, in turn, increases the effectiveness, efficiency, and safety of human-robot collaboration.

Expected Outcomes

The project aims to develop:

1. A framework for understanding human behavior, particularly decision-making, in manufacturing tasks.
2. An AR-driven, human-centered design approach for HRC.
3. Guidelines for designing AR interfaces that optimise human-robot interaction.

Supervisory Team

• Principal Supervisor: Professor Glenda Caldwell
• Associate Supervisors: Professor Markus Rittenbruch, Associate Professor Muge Fialho Leandro Alves Teixeira, Dr Alan Burden
• External Supervisor: Dr Matthias Guertler

Publications

First Author

• Liu, Y., Caldwell, G., Rittenbruch, M., Belek Fialho Teixeira, M., Burden, A., & Guertler, M. (2024). What affects human decision making in human–robot Collaboration?: A scoping review. Robotics, 13(2), 30. doi:10.3390/robotics13020030

Other Publications

• Burden, A. G., Liu, Y., Vermeulen, J., (2024). Designing Effective AR Interfaces for Human-Robot Interaction: A Scoping Review and Practical Guidelines for Robot Pick-and-Place Tasks. In Proceedings of The 36th Australian Conference on Human-Computer Interaction (OZCHI ’24). ACM, New York, NY, USA, 16 pages