论文标题
学习临时机器人网络的最佳拓扑
Learning Optimal Topology for Ad-hoc Robot Networks
论文作者
论文摘要
在本文中，我们合成了一种数据驱动的方法来预测临时机器人网络的最佳拓扑。从技术上讲，这个问题是一个多任务分类问题。但是，我们将其分为一类可以更有效解决的多类分类问题。为此，我们首先创建了一种算法，以创建与机器人网络各种配置相关的地面最佳拓扑。该算法结合了我们的学习模型成功地学习的复杂最佳标准集合。该模型是一个堆叠的集合，其输出是特定机器人的拓扑预测。每个堆叠的集合实例构成了三个低级估计器，它们的输出将通过高级增强搅拌器汇总。将我们的模型应用于10个机器人的网络，在预测与引用网络各种配置相对应的最佳拓扑的预测中显示了超过80％的精度。
In this paper, we synthesize a data-driven method to predict the optimal topology of an ad-hoc robot network. This problem is technically a multi-task classification problem. However, we divide it into a class of multi-class classification problems that can be more efficiently solved. For this purpose, we first compose an algorithm to create ground-truth optimal topologies associated with various configurations of a robot network. This algorithm incorporates a complex collection of optimality criteria that our learning model successfully manages to learn. This model is an stacked ensemble whose output is the topology prediction for a particular robot. Each stacked ensemble instance constitutes three low-level estimators whose outputs will be aggregated by a high-level boosting blender. Applying our model to a network of 10 robots displays over 80% accuracy in the prediction of optimal topologies corresponding to various configurations of the cited network.
