AI-Driven Digital Twins for Robots
This research topic focuses on building digital twins of rovers from various objectives either through machine learning techniques, model-based systems engineering, or a combination of both. In the context of digital twins for rovers, we aim to explore the following scenarios:
- Digital-twin enabled techniques to determine whether the behavior of a rover deviates significantly from the expected one.
- Building approaches that can be used for automated refactoring of digital twin models, e.g., for supporting predictive refactoring, using genetic algorithms or any predictive models such as ML.
- Considering how to learn and to transfer such a learning, possibly with uncertainty, to evolve models within the digital twins.
- Utilizing path planners inside the digital twins to determine the right action plan in real-time.
- Building generic digital twins for various types of rovers that can be easily configured for various.
The candidate will address the following activities:
- Establish the state of the art on digital twins, specifically for rovers.
- Define the relevant properties related to the implementation of such digital twins, e.g., reusability, modularity, evolution, uncertainty and transfer learning.
- Define a reference software architecture for designing and implementing such digital twins, maximizing the identified properties, and offering the aforementioned scenarios.
- Explore a generative approach to automate the implementation of digital twins according to the proposed reference architecture.
- Learning innovative digital twin technology
- Apply various AI techniques
- Work with real rovers
- Programming skills
- Familiarity with machine learning frameworks is a plus
- Shaukat Ali
- The research will be conducted in the context of INRIA-Simula Associated Team RESIST. More details about the team can be found at gemoc.org/resist/
The research will be jointly supervised by Benoit Combemale (people.irisa.fr/Benoit.Combemale/) and Djamel Khelladi (people.irisa.fr/Djamel-Eddine.Khelladi/) from Inria, and Shaukat Ali (www.simula.no/people/shaukat). As part of the research, the following physical rovers can be used: 1) Leo Rovers (www.leorover.tech/) at Simula; 2) the Aion Robotics R6 (www.aionrobotics.com/r6) at Inria.
- Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., and others & Nee, A. Y. C. (2021). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 58, 3-21
- Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016-1022
- Romina Eramo, Francis Bordeleau, Benoît Combemale, Mark van den Brand, Manuel Wimmer, Andreas Wortmann: Conceptualizing Digital Twins. IEEE Softw. 39(2): 39-46 (2022)
- Uncertainty-Aware Transfer Learning to Evolve Digital Twins for Industrial Elevators. Xu Qinghua, Shaukat Ali, Tao Yue, Maite Arratibel