用高斯信念传播统一解决机器人集群的连续与离散共识问题
DANCeRS: A Distributed Algorithm for Negotiating Consensus in Robot Swarms with Gaussian Belief Propagation
- 基于因子图建模，通过纯对等消息传递实现分布式共识
- 在路径规划与离散决策任务中均验证了高效性与可扩展性
- 适合需要动态协作的多机器人系统，如编队与集体决策
机器人集群需具备一致的集体行为以应对形状形成和决策等挑战。现有方法常将连续与离散决策空间的共识视为不同问题。本文提出DANCeRS，一种基于高斯信念传播（GBP）的统一分布式算法，在两类决策空间中均实现共识。通过将集群建模为因子图，该方法在动态环境中具备可扩展性和鲁棒性，仅依赖对等消息传递。我们在两个应用场景中验证了该框架的有效性：一是机器人协同路径规划与避障以实现形状形成；二是群体在离散决策上达成一致。实验结果表明，相比近期方法，本方案在效率与可扩展性上表现更优，是多机器人系统实现分布式共识的有力候选。建议观看附录视频演示。
原文摘要 · Abstract (English)
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.
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