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.

机器人技术最近为宇宙光谱学做出了贡献，以便使用机器人纤维定位​​器自动获取可观察到的宇宙图。为此，需要一种分配算法来将每个机械手光纤定位器分配给与特定观察值关联的目标。分配过程直接影响机械手光纤定位器达到其分配目标的协调。在本文中，我们建立了一个最佳的目标分配方案，该方案可以在机器人光纤定位器之间同时提供最快的协调和最小的冲突情况。特别是，我们提出了一种成本函数，通过该函数可以在目标分配过程中将两个引用的需求最小化。应用的仿真表明使用我们的最佳方法可以提高收敛速度。我们证明了在充分观察的情况下，我们的算法在二次时间内扩展了解决方案。另外，在监督和混合协调策略中，收敛时间和冲突场景的百分比也减少了。