Technology Category
Constraint Satisfaction and Optimization: Distributed CSP/OptimizationIntelligent Robots: Multi-Robot SystemsMultiagent Systems: Distributed Problem Solving
DANCeRS: A Distributed Algorithm for Negotiating Consensus in Robot Swarms with Gaussian Belief Propagation
🤖 AI Summary
Multi-robot swarms lack a unified distributed consensus framework applicable to both discrete and continuous decision spaces. Method: This paper proposes a factor-graph-based general modeling and inference approach, unifying Gaussian Belief Propagation (GBP) for consensus in both discrete and continuous domains for the first time. It introduces a fully decentralized, peer-to-peer message-passing protocol relying solely on local communication, enabling integrated solutions for shape formation, path planning, and collaborative decision-making. Contribution/Results: The method eliminates centralization, significantly improving scalability and robustness in dynamic environments. Experiments demonstrate faster convergence and higher solution accuracy compared to state-of-the-art distributed consensus methods, particularly in large-scale swarm tasks.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Unified distributed consensus for robot swarms
Achieving consensus in discrete and continuous domains
Scalable peer-to-peer coordination without central control
Innovation
Methods, ideas, or system contributions that make the work stand out.
Distributed algorithm using Gaussian Belief Propagation
Unified consensus in discrete and continuous domains
Peer-to-peer message passing on factor graph
