让机器人集群在无中心控制下实时识别集体行为并同步决策。
SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control
- 基于分布式共识与机器学习，实现完全去中心化的集群行为分类。
- 分类准确率高，同步延迟低，适合真实机器人部署。
- 可检测异常行为并自动协调群体动作，适用于实际场景。
机器人集群利用大量感知能力有限的独立智能体，在无需中央控制的情况下产生复杂的涌现行为。然而，现有研究较少探讨智能体如何仅通过局部感知推断群体行为，而这一能力对故障检测和行为变化识别至关重要。本文提出同步集群行为分类（SyncSBC），融合机器学习与分布式一致性机制，实现完全去中心化的集群行为分类与决策同步。实验表明，SyncSBC具备高分类准确率与低同步延迟，适合现实部署。我们还在真实机器人上验证了两项应用：使用SyncSBC的集群能精准识别个体异常行为，并自主协调群体行为变更。视频、代码与补充实验见https://sites.google.com/view/sync-sbc/home。
原文摘要 · Abstract (English)
Robot swarms utilize many independent limited-sensing agents to produce complex emergent behaviors without requiring centralized control. However, little research explores how agents can infer swarm-level behavior from purely local perception, a capability critical for detecting faults and behavior changes. In this paper, we introduce Synchronized Swarm Behavior Classification (SyncSBC), which combines improvements in machine learning and distributed consensus to classify collective swarm behavior and synchronize swarm decision-making in an entirely decentralized manner. We show that SyncSBC achieves high classification accuracy and low synchronization delay, making it suitable for real-world deployment. Finally, we use SyncSBC to demonstrate two promising swarm applications on real robots where we show that swarms utilizing SyncSBC can accurately identify anomalies in robot behavior and autonomously coordinate collective changes in swarm behavior. Videos, code and supplemental experiments are available at https://sites.google.com/view/sync-sbc/home.
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