Experimental evaluation of complete safe coordination of astrobots for Sloan Digital Sky Survey V
December 19, 2020 Β· Declared Dead Β· π Experimental astronomy (Print)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Matin Macktoobian, Ricardo AraΓΊjo, LoΓ―c Grossen, Luzius Kronig, Mohamed Bouri, Denis Gillet, Jean-Paul Kneib
arXiv ID
2012.10656
Category
astro-ph.IM
Cross-listed
cs.RO
Citations
3
Venue
Experimental astronomy (Print)
Last Checked
1 month ago
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β astro-ph.IM
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
π
π
Old Age
Star-galaxy Classification Using Deep Convolutional Neural Networks
R.I.P.
π»
Ghosted
CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
R.I.P.
π»
Ghosted
Non-negative Matrix Factorization: Robust Extraction of Extended Structures
R.I.P.
π
404 Not Found
Deep Recurrent Neural Networks for Supernovae Classification
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted