Decentralized Gaussian Process Classification and an Application in Subsea Robotics
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Abstract (arXiv)
Teams of cooperating autonomous underwater vehicles (AUVs) rely on acoustic communication for coordination, yet this communication medium is constrained by limited range, multi-path effects, and low bandwidth. One way to address the uncertainty associated with acoustic communication is to learn the communication environment in real-time. We address the challenge of a team of robots building a map of the probability of communication success from one location to another in real-time. This is a decentralized classification problem -- communication events are either successful or unsuccessful -- where AUVs share a subset of their communication measurements to build the map. The main contribution of this work is a rigorously derived data sharing policy that selects measurements to be shared among AUVs. We experimentally validate our proposed sharing policy using real acoustic communication data collected from teams of Virginia Tech 690 AUVs, demonstrating its effectiveness in underwater environments.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 19 Nov 2025.
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References · 8
- 2016GPflow: A Gaussian process library using TensorFlowcited by 139
- 2021Personalized Federated Learning with Gaussian Processescited by 76
- 2020Convergence of Sparse Variational Inference in Gaussian Processes Regressioncited by 53
- 2020Bayesian Few-Shot Classification with One-vs-Each P'olya-Gamma Augmented Gaussian Processescited by 32
- 2016Monte Carlo Markov Chain Algorithms for Sampling Strongly Rayleigh Distributions and Determinantal Point Processescited by 24
- 2018Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentationcited by 17
- 2022PropEM-L: Radio Propagation Environment Modeling and Learning for Communication-Aware Multi-Robot Explorationcited by 2
- 2024Prediction of Acoustic Communication Performance for AUVs using Gaussian Process Classificationcited by 1
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