A Distributed Multi-Robot Framework for Exploration, Information Acquisition and Consensus

TL;DR

This paper presents a novel distributed multi-robot framework that integrates local planning, global coordination, and mapping into a single factor graph, enabling efficient, asynchronous inference with Gaussian Belief Propagation for collaborative exploration.

Contribution

It introduces the first distributed GBP multi-robot solver that unifies various task aspects for intelligent collaboration, improving over separate, isolated solutions.

Findings

First demonstration of distributed GBP enabling collaborative robot behavior.

Efficient asynchronous inference for multi-aspect coordination.

Successful exploration and mapping in large spaces.

Abstract

The distributed coordination of robot teams performing complex tasks is challenging to formulate. The different aspects of a complete task such as local planning for obstacle avoidance, global goal coordination and collaborative mapping are often solved separately, when clearly each of these should influence the others for the most efficient behaviour. In this paper we use the example application of distributed information acquisition as a robot team explores a large space to show that we can formulate the whole problem as a single factor graph with multiple connected layers representing each aspect. We use Gaussian Belief Propagation (GBP) as the inference mechanism, which permits parallel, on-demand or asynchronous computation for efficiency when different aspects are more or less important. This is the first time that a distributed GBP multi-robot solver has been proven to enable intelligent collaborative behaviour rather than just guiding robots to individual, selfish goals. We encourage the reader to view our demos at https://aalpatya.github.io/gbpstack