  The space sector is experiencing a flourishing growth and evidence is
mounting that the near future will be characterized by a large amount of
deep-space missions. In the last decade, CubeSats have granted affordable
access to space due to their reduced manufacturing costs compared to
traditional missions. At the present-day, most miniaturized spacecraft have
thus far been deployed into near-Earth orbits, but soon a multitude of
interplanetary CubeSats will be employed for deep-space missions as well.
Nevertheless, the current paradigm for deep-space missions strongly relies on
ground-based operations. Although reliable, this approach will rapidly cause
saturation of ground slots, thereby hampering the current momentum in space
exploration. At the actual pace, human-in-the-loop, flight-related operations
for deep-space missions will soon become unsustainable.
  Self-driving spacecraft are challenging the current paradigm under which
spacecraft are piloted in interplanetary space. They are intended as machines
capable of traveling in deep space and autonomously reaching their destination.
In EXTREMA, these systems are used to engineer ballistic capture (BC), thereby
proving the effectiveness of autonomy in a complex scenario. The key is to
accomplish low-thrust orbits culminating in BC. For this, a bundle of BC orbits
named ballistic capture corridor (BCC) can be targeted far away from a planet.
To achieve BC at Mars without any a priori instruction, an inexpensive and
accurate method to construct BCC directly on board is required. Therefore,
granting spacecraft the capability to manipulate stable sets in order to
self-compute a BCC is crucial. The goal of the paper is to numerically
synthesize a corridor exploiting the polynomial chaos expansion (PCE)
technique, thereby applying a suited uncertainty propagation technique to BC
orbit propagation.
