  Astrobots are robotic artifacts whose swarms are used in astrophysical
studies to generate the map of the observable universe. These swarms have to be
coordinated with respect to various desired observations. Such coordination are
so complicated that distributed swarm controllers cannot always coordinate
enough astrobots to fulfill the minimum data desired to be obtained in the
course of observations. Thus, a convergence verification is necessary to check
the suitability of a coordination before its execution. However, a formal
verification method does not exist for this purpose. In this paper, we instead
use machine learning to predict the convergence of astrobots swarm. In
particular, we propose a weighted $k$-NN-based algorithm which requires the
initial status of a swarm as well as its observational targets to predict its
convergence. Our algorithm learns to predict based on the coordination data
obtained from previous coordination of the desired swarm. This method first
generates a convergence probability for each astrobot based on a distance
metric. Then, these probabilities are transformed to either a complete or an
incomplete categorical result. The method is applied to two typical swarms
including 116 and 487 astrobots. It turns out that the correct prediction of
successful coordination may be up to 80% of overall predictions. Thus, these
results witness the efficient accuracy of our predictive convergence analysis
strategy.
