论文标题
大规模光谱调查的最佳目标分配
Optimal target assignment for massive spectroscopic surveys
论文作者
论文摘要
机器人技术最近有助于宇宙学光谱，以使用机器人纤维定位器自动获得可观察到的宇宙的图。为此，需要分配算法才能将每个机器人纤维定位器分配给与特定观察结果相关的目标。分配过程直接影响机器人纤维定位器的协调，以达到其指定的目标。在本文中，我们建立了一个最佳目标分配方案，该方案同时提供了最快的协调，并伴随着机器人纤维定位器之间的最小碰撞场景。特别是，我们提出了一个成本函数，其最小化在目标分配过程中，这两个引用的要求均考虑在内。应用模拟使用我们的最佳方法表明了收敛速率的提高。我们表明，在完整观察的情况下，我们的算法在二次时时间缩放了解决方案。此外，在监督和混合协调策略中，汇聚时间和碰撞方案的百分比也有所下降。
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.
