Distributed Map Classification using Local Observations

TL;DR

This paper introduces a scalable, distributed approach for map classification using teams of robots with local observations, leveraging graph decomposition and offline learning to enhance efficiency in large environments.

Contribution

It presents a novel graph decomposition-based offline learning framework enabling scalable, distributed map classification by communicating robots.

Findings

Reduces offline training complexity via graph decomposition.

Enables scalable map classification in large environments.

Validated through extensive simulations.

Abstract

We consider the problem of classifying a map using a team of communicating robots. It is assumed that all robots have localized visual sensing capabilities and can exchange their information with neighboring robots. Using a graph decomposition technique, we proposed an offline learning structure that makes every robot capable of communicating with and fusing information from its neighbors to plan its next move towards the most informative parts of the environment for map classification purposes. The main idea is to decompose a given undirected graph into a union of directed star graphs and train robots w.r.t a bounded number of star graphs. This will significantly reduce the computational cost of offline training and makes learning scalable (independent of the number of robots). Our approach is particularly useful for fast map classification in large environments using a large number of communicating robots. We validate the usefulness of our proposed methodology through extensive simulations.