A survey on artificial intelligence trends in spacecraft guidance dynamics and control

Abstract

The rapid developments of artificial intelligence in the last decade are influencing aerospace engineering to a great extent and research in this context is proliferating. We share our observations on the recent developments in the area of spacecraft guidance dynamics and control, giving selected examples on success stories that have been motivated by mission designs. Our focus is on evolutionary optimisation, tree searches and machine learning, including deep learning and reinforcement learning as the key technologies and drivers for current and future research in the field. From a high-level perspective, we survey various scenarios for which these approaches have been successfully applied or are under strong scientific investigation. Whenever possible, we highlight the relations and synergies that can be obtained by combining different techniques and projects towards future domains for which newly emerging artificial intelligence techniques are expected to become game changers.

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Correspondence to Dario Izzo.

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Dario Izzo graduated as a doctor of aeronautical engineering from the University Sapienza of Rome (Italy). He then took his second master in Satellite Platforms at the University of Cranfield in the United Kingdom and completed his Ph.D. in mathematical modelling at the University Sapienza of Rome where he lectured classical mechanics and space flight mechanics. Dario Izzo later joined the European Space Agency and became the scientific coordinator of its Advanced Concepts Team. He devised and managed the Global Trajectory Optimization Competitions events, the ESAs Summer of Code in Space and the Kelvins innovation and competition platform for space problems. He published more than 170 papers in international journals and conferences making key contributions to the understanding of flight mechanics and spacecraft control and pioneering techniques based on evolutionary and machine learning approaches. Dario Izzo received the Humies Gold Medal and led the team winning the 8th edition of the Global Trajectory Optimization Competition.

Marcus Martens graduated from the University of Paderborn (Germany) with a master degree in computer science. He joined the European Space Agency as a Young Graduate Trainee in artificial intelligence where he worked on multi-objective optimization of spacecraft trajectories. He was part of the winning team of the 8th edition of the Global Trajectory Optimization Competition (GTOC) and received a HUMIES gold medal for developing algorithms achieving human competitive results in trajectory design. The Delft University of Technology awarded him a Ph.D. for his thesis on information propagation in complex networks. After his time at the network architectures and services group in Delft (Netherlands), Marcus rejoined the European Space Agency, where works as a research follow in the Advanced Concepts Team. While his main focus is on applied artificial intelligence and evolutionary optimization, Marcus has worked together with experts from different fields and authored works related to neuroscience, cyber-security and gaming.

Binfeng Pan received his Ph.D. degree in aerospace engineering from Northwestern Polytechnical University, China, in 2010. He is an associate professor at School of Astronautics, Northwestern Polytechnical University. His research interests are in the area of trajecotry optimizations, computational guidance and control, and applications of AI in aerospace engineering. He is the principal investigator (PI) or co-PI of several research grants on the aforementioned topics from the National Natural Science Foundation of China (NSFC), and the Chinese industry.

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Izzo, D., Märtens, M. & Pan, B. A survey on artificial intelligence trends in spacecraft guidance dynamics and control. Astrodyn 3, 287–299 (2019). https://doi.org/10.1007/s42064-018-0053-6

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Keywords

  • guidance
  • control
  • AI
  • deep learning
  • machine learning
  • evolutionary computing
  • genetic algorithms
  • interplanetary