DANCeRS：一种基于高斯信念传播的机器人群体协商共识分布式算法
DANCeRS: A Distributed Algorithm for Negotiating Consensus in Robot Swarms with Gaussian Belief Propagation
AI总结：
针对现有方法将机器人群体离散与连续决策空间共识视为不同问题的局限，提出统一分布式算法DANCeRS，通过因子图表示群体并利用高斯信念传播实现双域共识，经两个应用验证其可扩展性和效率优于近期方法。
摘要：
机器人群体需要凝聚的集体行为来应对包括形状形成和决策在内的各种挑战。现有方法常将离散和连续决策空间中的共识视为不同问题。我们提出DANCeRS，这是一种统一的分布式算法，利用高斯信念传播（GBP）在两个领域均实现共识。通过将群体表示为因子图，我们的方法仅依赖点对点消息传递，确保了在动态环境中的可扩展性和鲁棒性。我们通过两个应用展示了通用框架的有效性：群体中的智能体必须在依赖本地通信的同时就全局行为达成共识。第一个应用中，机器人需执行路径规划和避障以形成形状；第二个应用中，我们展示同一框架如何用于群体智能体就一组离散决策达成共识。实验结果表明，与针对这些问题的近期方法相比，我们的方法具有更好的可扩展性和效率，使其成为需要分布式共识的多机器人系统的有前景解决方案。我们鼓励读者查看补充视频演示。
英文摘要：
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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