Abstract
Autonomous systems increasingly are integrated into larger, connected, and hybrid (Human-Machine) systems of systems, making them complex systems - which are hard to design and predicting emergent behaviour is difficult. These issues are faced increasingly across civil and military applications, both in the UK and NATO. A holistic approach is needed to fully quantify them. Working as a partnership between industry and academia has provided greater freedom to apply innovative technologies in the context of relevant use cases. This paper presents some tools and methods we have used in our research and development to support this approach and address the challenges of deploying autonomous systems in the future. We discuss the use of simulations and how they can support every step of the process, from academic experiments to digital twins; where the right level of fidelity is needed at different times to give maximum benefit. The use of a common simulation platform to align control design exploration with human factors research is discussed, enabling questions of human-machine teaming and trust. We highlight how foundational research on: architecture and modelling, network topology, decision making processes and human interactions impact on the overall development of a system. Included are our lessons identified from this partnership.
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Acknowledgments
This material was funded and delivered in partnership between the Thales Group and the University of Bristol, and with the support of the UK Engineering and Physical Sciences Research Council Grant Award EP/R004757/1 entitled ‘Thales-Bristol Partnership in Hybrid Autonomous Systems Engineering (T-B PHASE)’. We would like to thank Professor Jan Noyes (UoB) for her support as well as the rest of the team.
We would also like to honour the memory of Angus Johnson who we sadly lost during the preparation of this paper, he was the driving force for the T-B Phase project and is greatly missed.
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Barden, E. et al. (2022). Academic and Industrial Partnerships in the Research and Development of Hybrid Autonomous Systems: Challenges, Tools and Methods. In: Mazal, J., et al. Modelling and Simulation for Autonomous Systems. MESAS 2021. Lecture Notes in Computer Science, vol 13207. Springer, Cham. https://doi.org/10.1007/978-3-030-98260-7_31
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DOI: https://doi.org/10.1007/978-3-030-98260-7_31
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