Faults occurring in ad-hoc robot networks may fatally perturb their topologies leading to disconnection of subsets of those networks. Optimal topology synthesis is generally resource-intensive and time-consuming to be done in real time for large ad-hoc robot networks. One should only perform topology re-computations if the probability of topology recoverability after the occurrence of any fault surpasses that of its irrecoverability. We formulate this problem as a binary classification problem. Then, we develop a two-pathway data-driven model based on Bayesian Gaussian mixture models that predicts the solution to a typical problem by two different pre-fault and post-fault prediction pathways. The results, obtained by the integration of the predictions of those pathways, clearly indicate the success of our model in solving the topology (ir)recoverability prediction problem compared to the best of current strategies found in the literature.

通过基于贝叶斯高斯混合模型的两条途径的数据驱动模型，我们将自组织机器人网络的拓扑(不可)恢复性预测问题解决为一个二元分类问题，并成功地预测了典型问题的解决方案。

自组织机器人网络拓扑恢复能力预测：一种基于数据驱动的容错方法