Space Trajectory Design Using Artificial Intelligence

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Recent research has shown that artificial intelligence can be used to aid the design of space trajectories. Instead of solving a complex and time-consuming optimisation problem, machine learning can be trained to provide an estimate of the cost of a transfer instantaneously. This can be used in preliminary mission design, or in multi-target missions, where the fast computation of a high number of trajectories is necessary. Currently this relies on a database of pre-computed optimal transfers, that can be used to train an artificial neural network.This PhD project will delve into machine learning and reinforcement learning for space trajectory design, with the main aim to create a framework where an agent would automatically learn about optimality of solutions iteratively, taking away the need to perform any optimisation at all. It will also expand to include the selection of possible mission targets (bodies or orbits), and systems design of the spacecraft itself - very often, the design of the trajectory relies on propulsion system, and vice-versa, the selection of propulsion system relies on the trajectory. Integrated mission-system design will be tackled with machine learning.Applications include multi-body missions, where thousands to millions of possible trajectories have to be evaluated, such as interplanetary multi-asteroid rendezvouses, and multiple active debris removal missions. However, fast trajectory optimisation is also used in the preliminary phases of the mission design, where the satellite systems (and propulsion system in particular) are not frozen, and shall be selected and optimised together with the trajectory itself.The ideal candidate will have a background in computer science, artificial intelligence, machine learning, with a strong interest for mathematical modelling and space systems. Alternatively, the candidate can have a background in space systems engineering, with a strong interest (and preferably experience) in artificial intelligence.