Jean-Paul Kneib

Florian Cabot, Yves Revaz, Jean-Paul Kneib et al.

VIRUP is a new C++ open source software that provides an interactive virtual reality environment to navigate through large scientific astrophysical datasets obtained from both observations and simulations. It is tailored to visualize terabytes of data, rendering at 90 frames per second in order to ensure an optimal immersion experience. While VIRUP has initially been designed to work with gaming virtual reality headsets, it supports different modern immersive systems like 3D screens, 180 deg. domes or 360 deg. panorama. VIRUP is scriptable thanks to the Python language, a feature that allows to immerse visitors through pre-selected scenes or to pre-render sequences to create movies. A companion video (https://www.youtube.com/watch?v=KJJXbcf8kxA) to the last SDSS 2020 release as well as a 21 minute long documentary, The Archaeology of Light, https://go.epfl.ch/ArchaeologyofLight have been both 100% produced using VIRUP.

Matin Macktoobian, Ricardo Araújo, Loïc Grossen et al.

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.

Conor Sayres, José R. Sánchez-Gallego, Michael R. Blanton et al.

Robotic fiber positioner (RFP) arrays are becoming heavily adopted in wide field massively multiplexed spectroscopic survey instruments. RFP arrays decrease nightly operational overheads through rapid reconfiguration between fields and exposures. In comparison to similar instruments, SDSS-V has selected a very dense RFP packing scheme where any point in a field is typically accessible to three or more robots. This design provides flexibility in target assignment. However, the task of collision-less trajectory planning is especially challenging. We present two multi-agent distributed control strategies that are highly efficient and computationally inexpensive for determining collision-free paths for RFPs in heavily overlapping workspaces. We demonstrate that a reconfiguration path between two arbitrary robot configurations can be efficiently found if "folded" state, in which all robot arms are retracted and aligned in a lattice-like orientation, is inserted between the initial and final states. Although developed for SDSS-V, the approach we describe is generic and so applicable to a wide range of RFP designs and layouts. Robotic fiber positioner technology continues to advance rapidly, and in the near future ultra-densely packed RFP designs may be feasible. Our algorithms are especially capable in routing paths in very crowded environments, where we see efficient results even in regimes significantly more crowded than the SDSS-V RFP design.

Matin Macktoobian, Francesco Basciani, Denis Gillet et al.

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.

Matin Macktoobian, Denis Gillet, Jean-Paul Kneib

Robotic fiber positioners play a vital role in the generation of massive spectroscopic surveys. The more complete a positioners set is coordinated, the more information its corresponding spectrograph receives during an observation. The complete coordination problem of positioners sets is studied in this paper. We first define the local and the global completeness problems and determine their relationship. We then propose a new artificial potential field according to which the convergences of a positioner and its neighboring positioners are cooperatively taken into account. We also discover the required condition for a complete coordination. We finally explain how the modifications of some of the parameters of a positioners set may resolve its incompleteness coordination scenarios. We verify our accomplishments using simulations.

Matin Macktoobian, Denis Gillet, Jean-Paul Kneib

Robotics have recently contributed to cosmological spectroscopy to automatically obtain the map of the observable universe using robotic fiber positioners. For this purpose, an assignment algorithm is required to assign each robotic fiber positioner to a target associated with a particular observation. The assignment process directly impacts on the coordination of robotic fiber positioners to reach their assigned targets. In this paper, we establish an optimal target assignment scheme which simultaneously provides the fastest coordination accompanied with the minimum of colliding scenarios between robotic fiber positioners. In particular, we propose a cost function by whose minimization both of the cited requirements are taken into account in the course of a target assignment process. The applied simulations manifest the improvement of convergence rates using our optimal approach. We show that our algorithm scales the solution in quadratic time in the case of full observations. Additionally, the convergence time and the percentage of the colliding scenarios are also decreased in both supervisory and hybrid coordination strategies.