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arXiv 2603.08038eess.SYcs.SY
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具有高效通信的分布式协调算法用于具有动态通信链接和处理延迟的开放多智能体系统

Distributed Coordination Algorithms with Efficient Communication for Open Multi-Agent Systems with Dynamic Communication Links and Processing Delays: Extended Version

KTH(皇家理工学院)

Jiaqi Hu, Karl H. Johansson, Apostolos I. Rikos

AI总结:

本文提出三种高效通信的分布式算法,用于解决开放多智能体系统中动态通信链接和处理延迟下的量化平均一致性问题,展示了其有限时间收敛和鲁棒性。

摘要:

本文聚焦于开放多智能体系统中动态有向通信链接中活跃节点的分布式量化平均一致性问题。我们提出了三种通信高效的分布式算法,适用于不同场景。我们的第一个算法在有限的网络开放性(即当活跃集最终稳定时)下解决量化平均问题。第二个算法扩展了上述方法,适用于节点遭受任意有界处理延迟的情况。第三个算法在无限开放的多智能体网络中运行,具有动态通信链接(即有连续的节点到达和离开),计算包含活跃和历史上活跃节点的平均值。我们分析了这些算法的操作,建立了其正确性,并提出了新的必要和充分的拓扑条件,确保其有限时间收敛。在分布式传感器融合用于环境监测的数值模拟中,展示了快速有限时间收敛和在不同网络规模、离开/到达率和处理延迟下的鲁棒性。最后,证明了我们提出的算法在现有文献中的算法中表现优异。

英文摘要:

In this paper we focus on the distributed quantized average consensus problem in open multi-agent systems consisting of dynamic directed communication links among active nodes. We propose three communication-efficient distributed algorithms designed for different scenarios. Our first algorithm solves the quantized averaging problem over the currently active node set under finite network openness (i.e., when the active set eventually stabilizes). Our second algorithm extends the aforementioned approach for the case where nodes suffer from arbitrary bounded processing delays. Our third algorithm operates over indefinitely open multi-agent networks with dynamic communication links (i.e., with continuous node arrivals and departures), computing the average that incorporates both active and historically active nodes. We analyze our algorithms' operation, establish their correctness, and present novel necessary and sufficient topological conditions ensuring their finite-time convergence. Numerical simulations on distributed sensor fusion for environmental monitoring demonstrate fast finite-time convergence and robustness across varying network sizes, departure/arrival rates, and processing delays. Finally, it is shown that our proposed algorithms compare favorably to algorithms in the existing literature.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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