Bellome, AndreaSanchez, Joan-PauRico Álvarez, Jose IgnacioAfsa, HadrienKemble, StephenFelicetti, Leonard2022-01-072022-01-072021-12-29Bellome A, Sanchez Cuartielles J-P, Rico Alvarez JI, et al., (2021) An automatic process for sample return missions based on dynamic programming optimization. In: AIAA SciTech 2022 Forum, 3-7 January 2022, San Diego and Virtual Event, Paper number AIAA 2022-1477https://doi.org/10.2514/6.2022-1477https://dspace.lib.cranfield.ac.uk/handle/1826/17384This work describes a methodology to design sample return missions and rendezvous trajectories options towards cometary objects. These are visited through a succession of fly-bys with Solar System planets, on an overall Multiple Gravity Assist (MGA) transfer. The method is based upon dynamic programming in conjunction to a specific MGA trajectory optimization model to investigate sample return mission scenarios. The model implemented is based on evaluation of grids of transfers between successive planets. The grid is obtained with Lambert arc transfer for a range of departure dates at one planet and range of time of flight to the next planet. For each successive planet in the sequence, discontinuities between incoming and outgoing Lambert arcs arise, which are in part compensated by the fly-by of the planet and, if required, an additional Δv maneuver is added on the given leg of a planet-to-planet transfer. The solutions identified are validated by re-optimizing the complete MGA trajectories as sequences of swing-bys, Deep Space Maneuvers and Lambert arcs transfers. A procedure for discontinuities removal using position constraints is also presented. Mission scenarios towards Saturn are used to validate the accuracy of proposed methods. Trajectory design for novel sample return options and rendezvous are explored for objects among Jupiter Family Comets (JFCs), as well as for never explored targets and orbital regions, as highly inclined Centaurs objects.enAttribution-NonCommercial 4.0 InternationalAn automatic process for sample return missions based on dynamic programming optimizationConference paper978-1-62410-631-6