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.

翻译：自组织机器人网络中发生的故障可能严重扰乱其拓扑结构，导致网络子集断开。在大型自组织机器人网络中，实时进行最优拓扑合成通常需要大量计算资源和时间。只有在任意故障发生后拓扑可恢复的概率超过其不可恢复的概率时，才应执行拓扑重计算。我们将此问题建模为二元分类问题。接着，我们开发了一种基于贝叶斯高斯混合模型的双通路数据驱动模型，通过两种不同的故障前预测通路和故障后预测通路来预测典型问题的解。通过整合这些通路的预测结果，与文献中现有最优策略相比，实验结果明确表明我们的模型在解决拓扑（不可）恢复性预测问题上的成功。