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

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, and (2) a two-layer collision avoidance strategy that pairs LLM-based urgency reasoning with a programmatic collision checker and re-prompting mechanism. We evaluate LLM-Flock across five state-of-the-art LLMs, including both closed-source (GPT-5, Claude 4.5) and open-source (Llama3.1-405b, Qwen-Max, DeepSeek-R1) models, demonstrating notable improvements in formation stability and convergence over prior LLM-based methods. Real-world experiments with Crazyflie drones further validate the framework's applicability to physical multi-robot systems.