SyncSBC: 同期自律制御のための分散型群行動予測
SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control
本論文では、各エージェントが局所的な知覚のみから群全体の行動を分類し、分散合意により群の意思決定を同期させる手法SyncSBCを提案し、実ロボットでの異常検知と行動変化の自律調整を実証した。
著者: Varun Raveendra, Connor Mattson, Daniel S. Brown
分類: cs.RO, cs.AI
原文アブストラクト
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.