大规模光谱测量的最佳目标分配
Optimal target assignment for massive spectroscopic surveys
摘要：
机器人技术最近为宇宙光谱学做出了贡献，使用机器人光纤定位器自动获得可观测宇宙的地图。为此，需要一种分配算法来将每个机器人光纤定位器分配给与特定观察相关联的目标。分配过程直接影响机器人光纤定位器的协调，以达到其分配的目标。在本文中，我们建立了一个最佳的目标分配方案，同时提供了最快的协调伴随着最少的碰撞场景之间的机器人纤维定位器。特别是，我们提出了一个成本函数，其最小化的引用的要求都考虑到在目标分配过程中。应用模拟表明，使用我们的最佳方法的收敛速度的改善。我们表明，我们的算法规模的解决方案在二次时间的情况下，充分的观察。此外，收敛时间和碰撞的情况下的百分比也减少了监督和混合协调策略。
英文摘要：
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.
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