A constraint programming framework for preliminary mission analysis: Applications for constellation-servicing active debris removal

Constraint Programming is a classical artificial intelligence paradigm characterised by its flexibility for the modelling of complex problems. In the field of space operations, this approach has been successfully used for mission planning and scheduling. This manuscript proposes a framework that leverages the strengths of Constraint Programming for the preliminary analysis of space missions, introducing some modifications to tailor it to the application at hand. Specifically, it uses constraint propagation and search techniques to thoroughly explore the configuration space of a mission in an efficient manner. Consequently, it is able to quantify the performance of precomputed mission choices with respect to the mission requirements, as well as generate new ones that optimise such performance. The proposed methodology has been particularised for two application cases involving active debris removal missions for large constellations in low Earth orbit, namely, a chaser case and a mothership case. The chaser case considers a servicing satellite that rendezvouses with the failed satellites of the constellation and directly transports them to a disposal orbit. The mothership case comprises a servicing satellite that installs deorbiting kits in each of the failed satellites, except for the one removed in the last place. This way, the servicing satellite will only transport this object, while the deorbiting kits will carry out the disposal of the rest of them. This methodology has been successfully used to evaluate a preliminary mission analysis of both application cases developed under ESA’s Sunrise project.