Large-scale object selection and trajectory planning for multi-target space debris removal missions

Abstract

Upcoming active space debris removal missions will most likely attempt to remove several objects per mission. The design of such missions involves the selection of the objects to be removed, as well as the optimisation of the visit sequence and the orbital transfers interconnecting them. In this work a branch-and-bound-based algorithm is presented for the preliminary design of multi-target space debris removal missions. The proposed algorithm comprises two different levels. The upper level, modelled as an Integer Linear Programming problem, deals with the combinatorial complexity of the problem. The lower level, modelled as a Mixed Integer Nonlinear Programming problem, encapsulates the orbital dynamics. Throughout the problem resolution, the upper level selects promising subsets of a pool of candidate objects of space debris, so that a removed threat value is maximised. Each of these subsets is passed through to the lower level, which ensures that there is a feasible trajectory that allows to rendezvous, in a specific sequence, with each and every object in the subset, while prescribed mission duration and Δv constraints are fulfilled. This framework is able to exploit the structure of the problem so that instances with large pools of candidate objects can be efficiently solved while achieving the certificates of optimality that branch-and-bound methods provide.