SyncSBC：用于同步自主控制的去中心化群体行为预测
SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control
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中文总结 AI 辅助
SyncSBC 是一种去中心化方法，结合机器学习与分布式共识实现群体行为分类及决策同步，准确率高、延迟低，可用于机器人异常识别与行为协同。
中文摘要 AI 辅助
机器人群体利用众多独立的感知受限智能体，无需集中控制即可产生复杂的涌现行为。然而，鲜有研究探索智能体如何仅通过局部感知推断群体层面的行为，这一能力对检测故障和行为变化至关重要。本文提出同步群体行为分类（SyncSBC），它结合机器学习与分布式共识的改进技术，以完全去中心化的方式对群体的集体行为进行分类并同步群体决策。研究表明，SyncSBC 能实现高分类准确率与低同步延迟，适合实际部署。最后，我们在真实机器人上展示了 SyncSBC 的两个有前景的群体应用：利用 SyncSBC 的群体可准确识别机器人行为异常，并自主协调群体行为的集体变化。视频、代码及补充实验可在该 https URL 获取。
英文摘要
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.
发表机构
- Kahlert School of Computing, University of Utah（犹他大学卡勒特计算学院）
机构由 AI 辅助整理，请以论文原文为准。
