Autonomous Digital twinning at the Edge

Framework and methods for autonomous creation of digital twins
Description of the Project: 

Digital twins are increasingly becoming a choice, if not a trend to forecast, operate and manage complex systems. However, all digital twins are derived from human defined models about the environment or the physical asset.  That is, the human defines the digital proxies, the virtualised environment, the technology, etc.  This project questions the potential of machines and/or networks to derive its own digital model and workspace. In particular, the emphasis on systems that are beyond human visual sight and where communication synchronicity is not guaranteed, i.e. working at the edge.  Such situations are common when disasters happen or for remote asset operations and management. This scoping research project aims to develop a new framework that offers far greater safe interactions and trustworthiness in autonomous systems. The intention is to derive novel methodologies that cater for a whole systems or ecosystems approach in developing the next generation of digital twins.  The research will define how robotics and autonomous systems can define their own interpretation of itself and the environment or space they work in. Analogous to inverse kinematics, is how environmental logic can generate the ambient twin capable to process at the edge. 

Resources required: 
Mixed reality system
Project number: 
123407
First Supervisor: 
University: 
Heriot-Watt University
Second Supervisor(s): 
First supervisor university: 
Heriot-Watt University
Essential skills and knowledge: 
IoT, Mechatronics, Software engineering, Systems
Desirable skills and knowledge: 
Cyber-physical systems, reliability engineering, remote sensing, Systems usability assessment,
References: 

Self-Certification and Safety Compliance for Robotics Platforms 

Zaki, O., Flynn, D., Blanche, J., Roe, J., Kong, C. W. L., Mitchell, D., Lim, T., Harper, S. & Robu, V., 4 May 2020, Offshore Technology Conference 2020. 18 p. OTC-30840-MS 

SMASH: One-Shot Model Architecture Search through HyperNetworks Brock, A., Lim, T., Ritchie, J. M. & Weston, N. J., May 2018. 

Qi, Q., Zhao, D., Liao, T.W. and Tao, F., 2018, June. Modeling of cyber-physical systems and digital twin based on edge computing, fog computing and cloud computing towards smart manufacturing. In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers Digital Collection. 

He, Y., Guo, J. and Zheng, X., 2018. From surveillance to digital twin: Challenges and recent advances of signal processing for industrial internet of things. IEEE Signal Processing Magazine, 35(5), pp.120-129. 

Lu, Y., Huang, X., Zhang, K., Maharjan, S. and Zhang, Y., 2020. Communication-efficient Federated Learning and Permissioned Blockchain for Digital Twin Edge Networks. IEEE Internet of Things Journal. 

Boschert, S., Heinrich, C. and Rosen, R., 2018, May. Next generation digital twin. In Proc. TMCE (pp. 209-217). Las Palmas de Gran Canaria, Spain. 

Harper, S., Sivanathan, A., Lim, T., McGibbon, S. & Ritchie, J. M. (2018) Development of a Mixed Reality Game for Simulation Based Education. Proc. 12th European Conf. on Games Based Learning. Ciussi, M. (ed.). Academic Conferences and Publishing International , p. 212-220. 

A novel design engineering review system with searchable content: knowledge engineering via real-time multimodal recording Sivanathan, A., Ritchie, J. M. & Lim, T., 2 Dec 2017, In : Journal of Engineering Design. 28, 10-12, p. 681-708 28 p.