Multimodal immersive digital twin platform for cyber–physical robot fleets in nuclear environments

Baniqued, Paul Dominick E.; Bremner, Paul; Sandison, Melissa; Harper, Samuel; Agrawal, Subham; Bolarinwa, Joseph; Blanche, Jamie; Jiang, Zhengyi; Johnson, Thomas; Mitchell, Daniel; Jose Lopez Pulgarin, Erwin; West, Andrew; Willis, Melissa; Yao, Kanzhong; Flynn, David; Giuliani, Manuel; Groves, Keir; Lennox, Barry; Watson, Simon

Abstract

The nuclear energy sector can benefit from mobile robots for remote inspection and handling, reducing human exposure to radiation. Advances in cyber–physical systems have improved robotic platforms in this sector through digital twin (DT) technology. DTs enhance situational awareness for robot operators, crucial for safety in the nuclear energy sector, and their value is anticipated to increase with the growing complexity of cyber–physical systems. The primary motivation of this work is to rapidly develop and evaluate a robot fleet interface that accounts for these benefits in the context of nuclear environments. Here, we introduce a multimodal immersive DT platform for cyber–physical robot fleets based on the ROS-Unity 3D framework. The system design enables fleet monitoring and management by integrating building information models, mission parameters, robot sensor data, and multimodal user interaction through traditional and virtual reality interfaces. A modified heuristic evaluation approach, which accounts for the positive and negative aspects of the interface, was introduced to accelerate the iterative design process of our DT platform. Robot operators from leading nuclear research institutions (Sellafield Ltd. and the Japan Atomic Energy Agency) performed a simulated robot inspection mission while providing valuable insights into the design elements of the cyber–physical system. The three usability themes that emerged and inspired our design recommendations for future developers include increasing the interface's flexibility, considering each robot's individuality, and adapting the platform to expand sensor visualization capabilities.

• Journal Article Type: Article
• Acceptance Date: Mar 7, 2024
• Online Publication Date: Apr 11, 2024
• Publication Date: Aug 1, 2024
• Deposit Date: Sep 10, 2024
• Publicly Available Date: Sep 10, 2024
• Print ISSN: 1556-4959
• Electronic ISSN: 1556-4967
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 41
• Issue: 5
• Pages: 1521-1540
• DOI: https://doi.org/10.1002/rob.22329
• Public URL: https://uwe-repository.worktribe.com/output/11877206
• Publisher URL: https://onlinelibrary.wiley.com/