SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control

2026-08-06

AI Summary

This work addresses the challenge of accurately inferring global swarm behavior from purely local sensing in decentralized robotic systems, where achieving coordination and fault response is inherently difficult. The paper proposes SyncSBC (Synchronous Swarm Behavior Classification), a novel approach that, for the first time, integrates lightweight machine learning models with a distributed consensus mechanism under fully decentralized conditions to enable high-accuracy, low-latency recognition of collective behaviors and synchronized decision-making. SyncSBC supports real-time anomaly detection and autonomous behavioral switching, thereby enhancing collaborative robustness and responsiveness in resource-constrained settings. The method’s efficacy is experimentally validated on a physical multi-robot platform, demonstrating significant improvements in both system resilience and reaction efficiency without reliance on centralized control or global information.

Abstract

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.

Problem

• swarm behavior prediction
• decentralized control
• local perception
• behavior classification
• anomaly detection

Innovation

• decentralized swarm control
• behavior classification
• distributed consensus
• local perception
• autonomous coordination