LATIDIA · Robótica

LLM-Flock: LLM-Driven Descentralized Multi-Robot Flocking via Influence-Based Consensus with Collision Avoidance

arXiv: 2505.06513v2Tipo de anuncio: reemplazar Resumen: Los modelos de lenguaje grandes (LLM) han demostrado una gran capacidad de razonamiento y planificación, lo que ha llevado a los investigadores a explorar su uso como tomadores de decisiones descentralizados para

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arXiv:2505.06513v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong capabilities in reasoning and planning, prompting researchers to explore their use as decentralized decision-makers for multi-robot formation control. However, directly applying LLMs to such tasks often leads to unstable behavior, in which robots may collapse to the centroid of their positions or diverge entirely, due to hallucinated reasoning and limited coordination awareness. We propose LLM-Flock, a decentralized multi-robot flocking framework that addresses these challenges through two key mechanisms: (1) an influence-based consensus protocol, where robots iteratively refine individually generated formation plans by adopting those of more connected neighbors, driving the team toward a unified plan without centralized control,