Experimental evaluation of complete safe coordination of astrobots for Sloan Digital Sky Survey V

Abstract

The data throughput of massive spectroscopic surveys in the course of each observation is directly coordinated with the number of optical fibers which reach their target. In this paper, we evaluate the safety and the performance of the astrobots coordination in SDSS-V by conducting various experimental and simulated tests. We illustrate that our strategy provides a complete coordination condition which depends on the operational characteristics of astrobots, their configurations, and their targets. Namely, a coordination method based on the notion of cooperative artificial potential fields is used to generate safe and complete trajectories for astrobots. Optimal target assignment further improves the performance of the used algorithm in terms of faster convergences and less oscillatory movements. Both random targets and galaxy catalog targets are employed to observe the coordination success of the algorithm in various target distributions. The proposed method is capable of handling all potential collisions in the course of coordination. Once the completeness condition is fulfilled according to initial configuration of astrobots and their targets, the algorithm reaches full convergence of astrobots. Should one assign targets to astrobots using efficient strategies, convergence time as well as the number of oscillations decrease in the course of coordination. Rare incomplete scenarios are simply resolved by trivial modifications of astrobots swarms’ parameters.

Astrobotics: Swarm Robotics for Astrophysical Studies

“…This strategy successfully yields a completeness condition which can be locally evaluated based on the specifications of astrobots in various states, e.g., their initial and target spots. In particular, it is also formally shown that if all of the neighborhoods are completely coordinated, then the coordination of the whole astrobots swarm associated with those neighborhoods is globally complete [17], [18]. Thus, the nonlinear hybrid control discussed in the previous section is substituted with the paradigm of cooperative nonlinear hybrid (CNH) control as depicted in Figure 5a.…”

Learning convergence prediction of astrobots in multi-object spectrographs

“…We wish to predict the success of this path generator without having to run it, which will provide a method for assessments for target selection. This is useful because the complete path generator [6,7] may require some time longer than the available time between two consecutive observations to run, which makes it computationally expensive for path generations. So, we are interested in using those which do not generally fulfill completeness, should they reach a minimum satisfactory convergence rate.…”

“…Thus, reaching a minimum of convergence rate in a swarm is an important requirement. This complicated swarm control problem is already solved using artificial potential fields [6,7,8] and supervisory control [9]. These methods can check the completeness of a typical swarm configuration.…”