Decentralized Gaussian Process Classification and an Application in Subsea Robotics

TL;DR

This paper introduces a decentralized Gaussian process classification method for real-time mapping of communication success probabilities among autonomous underwater vehicles, enhancing coordination in challenging acoustic environments.

Contribution

It presents a novel data sharing policy for decentralized Gaussian process classification, validated with real underwater acoustic communication data.

Findings

Effective communication success probability mapping in real-time

Validated approach with real AUV data

Improved coordination in underwater robotics

Abstract

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.