Analytical and Machine Learning Methods for the Complete Safe Coordination of Astrobot Swarms

The recent generations of massive spectroscopic surveys aim at the ray collection from a multitude of cosmological targets in the course of observations. For this purpose, astrobots are used to change the configuration of optical fibers from one observation to another in relatively short periods of time instead of tedious manual replacements. The dense formations of astrobots on focal planes enhance the number of the potential targets to be observed. However, the safe coordination of astrobots swarms is challenging. The more astrobots are coordinated, the more data are sent to a spectrograph, thereby the higher the resolution of a resulted survey will be. However, traditional collision-avoidance coordination strategies often give rise to the partial convergence of astrobots swarms. Thus, this thesis focuses on the solutions to the complete safe coordination of astrobots, particularly in the case of Sloan Digital Sky Survey V. We increase coordination convergence rates not only by directly improving the state-of-the-art coordination solution but also by optimizing target-to-astrobot assignments. Namely, we propose an optimal assignment scheme which minimizes both the likelihood of collisions between astrobots and the effort demanded to preform coordination in terms of the required time to perform coordination. We also propose a cooperative coordination method in which, given particular settings of astrobots and/or targets, each astrobot stops at its goal point when its other neighboring peers have already reached theirs, as well. So, we derive a localized completeness condition that, if sufficed, generates the trajectories which completely coordinate an astrobots swarm in a guaranteed manner. We also propose a logic-based formally-verifiable supervisory coordination technique whose behavior is always safe and complete without any need to simulation-based validations. Finally, we employ machine learning tools to train models to predict the feasibility of complete coordination only according to initial and final configurations of astrobots and their targets' projected locations on their focal plane. These models contribute to the identification of those target-astrobot pairings which do not fulfill the completeness condition. So, one may re-plan such ill-posed assignments before getting involved in potentially-pointless completeness checking simulations. Our simulated and experimental tests manifest the efficiency of the developed methods quoted above.