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3 images: 1) a trajectory summary, 2) a motion primitive approach to trajectory design, and 3) maneuvers designed by a trained reinforcement learning agent

Our future in space involves miniaturized satellites for low-cost and rapid access to space, frequent and sustained operations in cislunar space, agile and autonomous spacecraft that can plan paths with little or no dependence on an analyst,听on-orbit servicing for sustainability, in-space assembly of critical infrastructure, formations for multi-point measurements, and spacecraft visiting the farthest reaches of our solar system. Across these architectures, form factors, and destinations is a common thread: spacecraft operating in complex, multi-body systems where trajectory analysis, design, and prediction听can be considered as a critical enabling and/or enhancing technology.听

Inspired by this future, the Bosanac group听focuses听on developing new strategies for spacecraft trajectory analysis, design, and prediction within chaotic multi-body gravitational systems.听

To achieve this goal, we use interdisciplinary techniques such as dynamical systems theory, data mining, machine learning, and path-planning. By developing these strategies, we aim to:

  1. Enable new missions with new spacecraft form factors, architectures, and objectives via innovative trajectories that mitigate the impact of technology gaps and operational limitations, and听
  2. Map transport pathways in support of knowledge discovery and space situational awareness

Image credits:

  • Left:听Bonasera, S; Bosanac, N, 'Applying Data Mining Techniques to Higher-Dimensional Poincar茅 Maps in the Circular Restricted Three-Body Problem,' November 2021, Vol. 133, No. 51,听Celestial Mechanics and Dynamical Astronomy, DOI:听.
  • Center:听Smith, T.R.; Bosanac, N,听鈥楳otion Primitive Approach to Spacecraft Trajectory Design in a Multi-Body System,鈥� September 2023, Vol. 70, No. 34, The Journal of Astronautical Sciences, DOI:听
  • Right:听Bonasera, S; Bosanac, N; Sullivan, C; Elliott, I; Ahmed, N; McMahon, J, 'Designing Sun-Earth L2 Halo Orbit Stationkeeping Maneuvers via Reinforcement Learning,' February 2023, Vol. 46, No. 2, Journal of Guidance, Control, and Dynamics, DOI:听