  Given a set of celestial bodies, the problem of finding an optimal sequence
of swing-bys, deep space manoeuvres (DSM) and transfer arcs connecting the
elements of the set is combinatorial in nature. The number of possible paths
grows exponentially with the number of celestial bodies. Therefore, the design
of an optimal multiple gravity assist (MGA) trajectory is a NP-hard mixed
combinatorial-continuous problem. Its automated solution would greatly improve
the design of future space missions, allowing the assessment of a large number
of alternative mission options in a short time. This work proposes to formulate
the complete automated design of a multiple gravity assist trajectory as an
autonomous planning and scheduling problem. The resulting scheduled plan will
provide the optimal planetary sequence and a good estimation of the set of
associated optimal trajectories. The trajectory model consists of a sequence of
celestial bodies connected by twodimensional transfer arcs containing one DSM.
For each transfer arc, the position of the planet and the spacecraft, at the
time of arrival, are matched by varying the pericentre of the preceding
swing-by, or the magnitude of the launch excess velocity, for the first arc.
For each departure date, this model generates a full tree of possible transfers
from the departure to the destination planet. Each leaf of the tree represents
a planetary encounter and a possible way to reach that planet. An algorithm
inspired by Ant Colony Optimization (ACO) is devised to explore the space of
possible plans. The ants explore the tree from departure to destination adding
one node at the time: every time an ant is at a node, a probability function is
used to select a feasible direction. This approach to automatic trajectory
planning is applied to the design of optimal transfers to Saturn and among the
Galilean moons of Jupiter.
