DANCeRS: A Distributed Algorithm for Negotiating Consensus in Robot Swarms with Gaussian Belief Propagation

TL;DR

DANCeRS introduces a scalable, distributed algorithm using Gaussian Belief Propagation for achieving consensus in robot swarms across both discrete and continuous decision spaces, enhancing robustness and efficiency.

Contribution

The paper presents a unified, distributed framework for consensus in robot swarms utilizing Gaussian Belief Propagation, applicable to both shape formation and decision-making tasks.

Findings

Demonstrates scalability and robustness in dynamic environments.

Effective in path planning and collision avoidance for shape formation.

Achieves consensus on discrete decisions efficiently.

Abstract

Robot swarms require cohesive collective behaviour to address diverse challenges, including shape formation and decision-making. Existing approaches often treat consensus in discrete and continuous decision spaces as distinct problems. We present DANCeRS, a unified, distributed algorithm leveraging Gaussian Belief Propagation (GBP) to achieve consensus in both domains. By representing a swarm as a factor graph our method ensures scalability and robustness in dynamic environments, relying on purely peer-to-peer message passing. We demonstrate the effectiveness of our general framework through two applications where agents in a swarm must achieve consensus on global behaviour whilst relying on local communication. In the first, robots must perform path planning and collision avoidance to create shape formations. In the second, we show how the same framework can be used by a group of robots to form a consensus over a set of discrete decisions. Experimental results highlight our method's scalability and efficiency compared to recent approaches to these problems making it a promising solution for multi-robot systems requiring distributed consensus. We encourage the reader to see the supplementary video demo.