Decentralized Gaussian Process Classification and an Application in Subsea Robotics
This work addresses the challenge of uncertain acoustic communication for AUV teams, which is incremental as it builds on existing decentralized classification approaches.
The paper tackles the problem of real-time mapping of acoustic communication success probabilities for teams of autonomous underwater vehicles (AUVs) by developing a decentralized Gaussian process classification method, with experimental validation on real data showing its effectiveness in underwater environments.
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.