# Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant Approach

arXiv: https://arxiv.org/abs/2310.20024 · HTML: https://mlsanity.com/paper/2310.20024/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 58 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Bayesian Deep Learning and a Probabilistic Perspective of Generalization](https://mlsanity.com/paper/2002.08791/) (2020)
- [AI-IMU Dead-Reckoning](https://mlsanity.com/paper/1904.06064/) (2019)
- [Bayesian Gaussian mixture model for robotic policy imitation](https://mlsanity.com/paper/1904.10716/) (2019)
- [Learning Optimal Topology for Ad-hoc Robot Networks](https://mlsanity.com/paper/2201.12900/) (2022)
- [Meta Navigation Functions: Adaptive Associations for Coordination of Multi-Agent Systems](https://mlsanity.com/paper/2210.01314/) (2022)

## Abstract (arXiv)

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.