  Astrobot swarms are used to capture astronomical signals to generate the map
of the observable universe for the purpose of dark energy studies. The
convergence of each swarm in the course of its coordination has to surpass a
particular threshold to yield a satisfactory map. The current coordination
methods do not always reach desired convergence rates. Moreover, these methods
are so complicated that one cannot formally verify their results without
resource-demanding simulations. Thus, we use support vector machines to train a
model which can predict the convergence of a swarm based on the data of
previous coordination of that swarm. Given a fixed parity, i.e., the rotation
direction of the outer arm of an astrobot, corresponding to a swarm, our
algorithm reaches a better predictive performance compared to the state of the
art. Additionally, we revise our algorithm to solve a more generalized
convergence prediction problem according to which the parities of astrobots may
differ. We present the prediction results of a generalized scenario, associated
with a 487-astrobot swarm, which are interestingly efficient and collision-free
given the excessive complexity of this scenario compared to the constrained
one.
