Edward Tomanek-Volynets

Email: e.tomanek-volynets.1@research.gla.ac.uk

ORCID iDhttps://orcid.org/0009-0006-2927-5632

Research title: Space Trajectory Design Using Artificial Intelligence

Research Summary

Biography

I graduated with a MEng in Aeronautical Engineering from the University of Glasgow in 2023, with a final year dissertation on optimisation of low-thrust space trajectories. I currently work in the Space and Exploration Technology group of the James Watt School of Engineering, pursuing a PhD in Aerospace Engineering, supervised by Dr. Matteo Ceriotti and Professor Colin McInnes. In addition to my research, I have been involved in delivering a number of undergraduate engineering courses since 2021.

In 2025, I spent time as a visiting researcher in the European Space Agency's Advanced Concepts Team (ACT), working on mission analysis and design. Specific details of our project can be found here.

Current research

My work explores the use of machine learning to quickly optimise spacecraft trajectories in missions with a large number of destinations. This is a combinatorial problem which is extremely (often prohibitively) time-consuming to design using conventional methods, but very useful in contexts such as space debris removal (the necessity of which is becoming quite urgent); so the possibility of training approximation models to learn properties of good solutions is being explored.

My research uses reinforcement learning to tackle this "sequence design" problem; an agent is trained to obtain near-optimal solutions without the need for resource-intensive iterative optimisation. A further problem within multi-target mission design is the calculation of costs of mission legs between individual pairs of orbits (many of which need to be calculated to select the sequence). The use of neural network approximators instead of full trajectory optimisers for these leg costs has become commonplace due to their evaluation speed, but there is still room for improvement on the accuracy, training time and versatility of these tools, so I am also performing research on these techniques.

I am open to collaboration so if you are performing research that links in with my topic, please do not hesitate to send an email.

Broader research interests

Whilst the main focus of my PhD work is on multi-target mission design and machine learning, I have broader interests more generally in high-efficiency space trajectory optimisation/optimal control methods, and high specific impulse or propellantless thrust control.

Publications

List by: Type | Date

Jump to: 2025 | 2024
Number of items: 4.

2025

Tomanek-Volynets, Edward L. ORCID logoORCID: https://orcid.org/0009-0006-2927-5632, McInnes, Colin R. ORCID logoORCID: https://orcid.org/0000-0003-0988-8854 and Ceriotti, Matteo ORCID logoORCID: https://orcid.org/0000-0001-6819-7178 (2025) Transfer learning for sample-efficient training of space trajectory cost approximators. IEEE Transactions on Aerospace and Electronic Systems, (doi: 10.1109/TAES.2025.3610417) (Early Online Publication)

Tomanek-Volynets, Edward ORCID logoORCID: https://orcid.org/0009-0006-2927-5632 and Ceriotti, Matteo ORCID logoORCID: https://orcid.org/0000-0001-6819-7178 (2025) The pointer network for reward maximisation in multi-target space mission sequence selection. Advances in Space Research, 75(12), pp. 8687-8706. (doi: 10.1016/j.asr.2025.04.045)

Tomanek-Volynets, Edward ORCID logoORCID: https://orcid.org/0009-0006-2927-5632, Ceriotti, Matteo ORCID logoORCID: https://orcid.org/0000-0001-6819-7178 and McInnes, Colin ORCID logoORCID: https://orcid.org/0000-0003-0988-8854 (2025) Solar Sail Trajectory Cost Estimation with Transfer Learning. In: 7th International Symposium on Space Sailing (ISSS 2025), Delft, The Netherlands, 30 Jun - 04 Jul 2025, (Accepted for Publication)

2024

Tomanek-Volynets, Edward ORCID logoORCID: https://orcid.org/0009-0006-2927-5632, Ceriotti, Matteo ORCID logoORCID: https://orcid.org/0000-0001-6819-7178 and McInnes, Colin R. ORCID logoORCID: https://orcid.org/0000-0003-0988-8854 (2024) Multi-target Space Mission Sequence Optimization with Deep Reinforcement Learning. In: 75th International Astronautical Congress (IAC), Milan, Italy, 14-18 Oct 2024,

This list was generated on Wed Oct 22 23:00:38 2025 BST.
Number of items: 4.

Articles

Tomanek-Volynets, Edward L. ORCID logoORCID: https://orcid.org/0009-0006-2927-5632, McInnes, Colin R. ORCID logoORCID: https://orcid.org/0000-0003-0988-8854 and Ceriotti, Matteo ORCID logoORCID: https://orcid.org/0000-0001-6819-7178 (2025) Transfer learning for sample-efficient training of space trajectory cost approximators. IEEE Transactions on Aerospace and Electronic Systems, (doi: 10.1109/TAES.2025.3610417) (Early Online Publication)

Tomanek-Volynets, Edward ORCID logoORCID: https://orcid.org/0009-0006-2927-5632 and Ceriotti, Matteo ORCID logoORCID: https://orcid.org/0000-0001-6819-7178 (2025) The pointer network for reward maximisation in multi-target space mission sequence selection. Advances in Space Research, 75(12), pp. 8687-8706. (doi: 10.1016/j.asr.2025.04.045)

Conference Proceedings

Tomanek-Volynets, Edward ORCID logoORCID: https://orcid.org/0009-0006-2927-5632, Ceriotti, Matteo ORCID logoORCID: https://orcid.org/0000-0001-6819-7178 and McInnes, Colin ORCID logoORCID: https://orcid.org/0000-0003-0988-8854 (2025) Solar Sail Trajectory Cost Estimation with Transfer Learning. In: 7th International Symposium on Space Sailing (ISSS 2025), Delft, The Netherlands, 30 Jun - 04 Jul 2025, (Accepted for Publication)

Tomanek-Volynets, Edward ORCID logoORCID: https://orcid.org/0009-0006-2927-5632, Ceriotti, Matteo ORCID logoORCID: https://orcid.org/0000-0001-6819-7178 and McInnes, Colin R. ORCID logoORCID: https://orcid.org/0000-0003-0988-8854 (2024) Multi-target Space Mission Sequence Optimization with Deep Reinforcement Learning. In: 75th International Astronautical Congress (IAC), Milan, Italy, 14-18 Oct 2024,

This list was generated on Wed Oct 22 23:00:38 2025 BST.

Grants