Astrobot swarms are used to capture astronomical signals to generate the map of the observable universe for the purpose of dark energy studies. The convergence of each swarm in the course of its coordination has to surpass a particular threshold to yield a satisfactory map. The current coordination methods do not always reach desired convergence rates. Moreover, these methods are so complicated that one cannot formally verify their results without resource-demanding simulations. Thus we use support vector machines to train a model that can predict the convergence of a swarm based on the data of previous coordination of that swarm. Given a fixed parity, i.e., the rotation direction of the outer arm of an astrobot, corresponding to a swarm, our algorithm reaches a better predictive performance compared to the state of the art. Additionally, we revise our algorithm to solve a more generalized convergence prediction problem according to which the parities of astrobots may differ. We present the prediction results of a generalized scenario, associated with a 487-astrobot swarm, which are interestingly efficient and collision free given the excessive complexity of this scenario compared to the constrained one.

中文翻译：

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多目标光谱仪中天文机器人的学习收敛预测

Astrobot群用于捕获天文信号，以生成可观察宇宙的地图，以进行暗能量研究。每个群在其协调过程中的收敛必须超过特定的阈值才能产生令人满意的地图。当前的协调方法并不总是达到期望的收敛速度。此外，这些方法是如此复杂，以至于没有资源需求的模拟就无法正式验证其结果。因此，我们使用支持向量机来训练一个模型，该模型可以基于该群体先前的协调数据来预测群体的收敛性。给定一个固定的奇偶性，即，一个太空人的外臂的旋转方向（对应于一个群），与现有技术相比，我们的算法具有更好的预测性能。此外，我们修改了算法，以解决更普遍的收敛预测问题，根据该问题，天文机器人的奇偶性可能会有所不同。我们给出了与487天文机器人群相关联的广义场景的预测结果，鉴于这种场景与受约束的场景相比过于复杂，该结果有趣且高效且无碰撞。