  In this paper we describe a machine learning based framework for spacecraft
swarm trajectory planning. In particular, we focus on coordinating motions of
multi-spacecraft in formation flying through passive relative orbit(PRO)
transfers. Accounting for spacecraft dynamics while avoiding collisions between
the agents makes spacecraft swarm trajectory planning difficult. Centralized
approaches can be used to solve this problem, but are computationally demanding
and scale poorly with the number of agents in the swarm. As a result,
centralized algorithms are ill-suited for real time trajectory planning on
board small spacecraft (e.g. CubeSats) comprising the swarm. In our approach a
neural network is used to approximate solutions of a centralized method. The
necessary training data is generated using a centralized convex optimization
framework through which several instances of the n=10 spacecraft swarm
trajectory planning problem are solved. We are interested in answering the
following questions which will give insight on the potential utility of deep
learning-based approaches to the multi-spacecraft motion planning problem: 1)
Can neural networks produce feasible trajectories that satisfy safety
constraints (e.g. collision avoidance) and low in fuel cost? 2) Can a neural
network trained using n spacecraft data be used to solve problems for
spacecraft swarms of differing size?
