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

arXiv:2505.06513 · cs.RO · Submitted 2025-05-10 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: We're looking at the paper: "LLM-Flock: LLM-Driven Decentralized Multi-Robot Flocking via Influence-Based Consensus with Collision Avoidance".

Dev: The gist The proposed framework integrates LLM-driven local planning with an influence-based consensus protocol to enable decentralized multi-robot flocking without centralized coordination or retraining.

Rosa: First, who's behind it and why it matters.

Paper summary: Rosa: We just talked about how LLM-Flock tackles the instability issues by using an influence-based consensus protocol with local planning from individual LLMs. The main thesis is that you can use these models as decentralized decision makers for multi-robot formation control.

Dev: They found that directly applying LLMs to this kind of task often leads to robots collapsing into the centroid or just diverging because of hallucinated reasoning and a lack of coordination awareness in the system.

Taro: That instability is a major concern for real-world deployment, especially when we think about safety cases. If the robot group can't maintain their shape under stress, that’s a huge failure mode.

Rosa: The solution they propose is to have each robot generate its own local plan toward the desired formation using its own LLM first. Then they iteratively refine those plans through this consensus process where robots adopt the plans of more connected neighbors.

Dev: That refinement happens because the influence score, which measures a robot's communication centrality—defined as the number of connected neighbors divided by the total number of robots—tells each robot how much weight to put on a neighbor's plan when deciding its own.

Taro: I’m wondering about that influence score. How does that prevent one bad decision from cascading through the whole flock? Is there a way for a truly bad plan to stop spreading?

Dev: The idea is that by basing the adoption on this calculated influence, you’re driving the team toward a unified plan without needing some kind of centralized control dictating every single move. It’s decentralized coordination.

Rosa: After adopting a new plan, each robot dynamically assigns itself one goal position from that adopted set based on proximity to ensure they transition smoothly during the update.

Taro: So while they’re planning their next step based on this consensus, is there any explicit mechanism for collision avoidance built in here beyond the general structure?

Dev: Yes, they use a two-layer collision avoidance strategy. This pairs LLM-based urgency reasoning with a programmatic collision checker and a re-prompting mechanism if things get dangerous. It’s like having the LLM reason about immediate danger while a hard rule checks for physical overlaps.

Rosa: That combination is what makes it more robust than just using the LLM alone for planning, as they address those logical inconsistencies that plague pure LLM applications in this context.

Taro: So the paper is essentially arguing that you can get flexible reasoning from an LLM and make it work in a multi-robot setting by layering a structured consensus protocol on top of it.

Dev: Exactly. It’s about mitigating the inherent weaknesses of using raw, unstructured LLM output for tasks that require precise, predictable coordination among many agents.

Rosa: So we've covered the basics: it uses local LLM generation, influence-based consensus for alignment, and a two-layer collision avoidance system to keep the robots safe and coordinated.

Taro: That sets up a good question for what comes next—how does this actually scale up to, say, dozens of robots instead of just a handful?

Dev: That’s the next thing we need to probe. The paper focuses on demonstrating that these improvements hold across five different models, which suggests the framework is quite generalizable across various LLM architectures.

Rosa: Right. And it moves beyond just proving it works in simulation by validating it with physical Crazyflie drones in real-world experiments. That adds another layer of confidence to the results.

Conclusion: Rosa: So, looking at the whole paper, LLM-Flock is a framework that successfully demonstrates how you can combine local LLM planning with an influence-based decentralized consensus mechanism to achieve multi-robot flocking without needing centralized coordination or retraining.

Dev: The authors are Peihan Li and Lifeng Zhou who are pointing toward the potential of using language models as decentralized decision makers for robots that need to interpret high-level instructions and generate formation strategies on the fly.

Taro: It shows that you can get flexible reasoning from an LLM and make it work in a multi-robot setting by layering a structured consensus protocol on top of it to mitigate inconsistencies.

Rosa: This means we’re looking at a path where robots can interpret complex, high-level goals and generate formation strategies on the fly in dynamic environments without needing constant retraining for every new scenario.

Dev: The core implication is that this approach offers a way to achieve scalable multi-robot collaboration by leveraging the reasoning capabilities of LLMs while keeping the coordination local and decentralized.

Taro: It’s less about replacing traditional control methods entirely and more about finding a hybrid way where the language model handles the complex, flexible planning part, and a simpler mechanism handles the stability part.

Rosa: So it’s a step toward building systems that are more adaptable to unexpected situations because they can reason about what’s happening right around them while still staying loosely coordinated with their teammates.

Dev: That seems like a practical direction for real-time control engineers—focusing on how to integrate these LLM capabilities into existing real-time loop constraints reliably.

Taro: It definitely suggests that the future involves testing this in environments where the failures are more extreme, pushing it beyond just simple formation maintenance.

Rosa: That’s what we're hearing about. We’ll keep an eye on how these models handle those tougher scenarios as researchers continue to build on this work.

Drexel University

cs.RO

Submitted: 2025-05-10

Updated: 2026-10-19

Code: https://github.com/Zhourobotics/LLM-Flock

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 67/100

The gist: The gist The proposed framework integrates LLM-driven local planning with an influence-based consensus protocol to enable decentralized multi-robot flocking without centralized coordination or

Key concepts

Influence-based Consensus Protocol: This mechanism allows robots to agree on a common formation plan without a single leader. Robots iteratively adopt the plans of their neighbors based on how central those neighbors are in the network. This process ensures the entire team converges toward one unified plan dynamically, relying only on local communication.
LLM-Driven Local Planning: Each robot uses its onboard LLM to independently generate a formation plan based on its local environment and constraints. The LLM is given specific instructions about the desired shape and distance requirements. This allows for flexible, context-aware planning tailored to each robot's immediate situation.
Two-Layer Collision Avoidance Strategy: Safety is ensured by combining urgency reasoning from the LLM with a programmatic collision checker. The LLM provides high-level urgency, while the programmatic checker verifies physical safety. A re-prompting mechanism allows the system to correct trajectory planning if an immediate collision risk is detected.

Terminology

Adaptability — 1× in this paper · explained in 1 episode
Large Language Models (LLMs) — 1× in this paper · explained in 8 episodes

Summary

The gist The proposed framework integrates LLM-driven local planning with an influence-based consensus protocol to enable decentralized multi-robot flocking without centralized coordination or retraining.

How it works

Individual Plan Generation: Each robot independently queries its onboard Large Language Model (LLM) to generate a candidate formation plan based on local objectives and environmental constraints. The system message explicitly describes the objective of forming a specified geometric formation, the inter-robot distance requirements, the safe distance constraint, the maximum allowed movement per round, and the communication range. After receiving this system message, each robot initiates its mission by sending an initial plan generation user message to its LLM. This user message instructs the LLM to propose a full set of target positions for all robots that satisfy the specified formation constraints. The LLM is also required to assign the robot’s own role by indicating which specific position it should take within the formation. The output format is explicitly defined to ensure accurate and consistent information extraction across all robots.
Influence-based Decentralized Plan Consensus: After individual plan generation, robots negotiate and align on a common formation plan through an influence-based consensus process. The notion of influence quantifies a robot’s communication centrality within the network, defined as Ii(t) = Ni(t)/N. At each consensus round, every robot broadcasts its current formation plan Pi and computed influence score Ii(t) to its neighboring robots Ni(t) Ii(t) and Pk!= Pi, robot i adopts the neighbor’s plan Pi <- Pk Ii(t) and Pk!= Pi, robot i adopts the neighbor’s plan Pi <- Pk. After adopting a new plan, each robot dynamically assigns itself a goal position from the adopted plan based on proximity. This nearest-available-goal assignment ensures a smooth transition during plan updates, minimizes unnecessary movement, and promotes stability throughout the formation process.
Coordinated Motion Execution: Following the influence-based consensus process, each robot proceeds with motion execution while continuously adjusting its trajectory based on local observations. During each update cycle, the robot queries its LLM using a position update user message that encodes its current state and nearby environmental information. Using this updated local information, the LLM determines the robot’s next movement step toward its assigned goal gi. The LLM reasons about the robot’s next waypoint wi(t) in R squared toward its assigned goal gi, taking into account the updated neighbor configuration and formation constraints.

Evaluation

The framework was evaluated through comprehensive simulations involving both state-of-the-art closed-source LLMs (e.g., o3-mini, Claude 3.5) and open-source models (e.g., Llama3.1, Qwen-Max, DeepSeek) and validated on a physical team of Crazyflie drones. The results show notable improvements in stability, convergence, and adaptability over previous LLM-based methods. In quantitative simulation evaluation, the Procrustes shape error was used as a metric to measure the geometric difference between the actual robot formation X and the target formation Y after optimal alignment. Stronger reasoning models such as OpenAI o3-mini and DeepSeek-R1 achieved faster convergence and consistently formed desired formations across all tasks.

Limitations

Key limitations include the computational demands of modern language models, which can be too large for lightweight robot platforms or lack sufficient reasoning ability for onboard inference. Furthermore, LLM outputs remain susceptible to inconsistencies under partial or ambiguous observations of neighboring states. The overall performance inherently depends on the zero-shot planning accuracy of the underlying language model.

Conclusion

LLM-Flock successfully demonstrates that combining local LLM-based planning with an influence-based decentralized consensus mechanism enables scalable multi-robot flocking without centralized coordination or retraining. This approach mitigates inconsistencies in unstructured LLM reasoning and demonstrates the potential of combining language models with lightweight coordination for multi-robot collaboration.

Failure Analysis

Failures can occur due to incorrect plan generation, where the proposed formation geometry is invalid or poorly structured. Additionally, flawed motion execution can impact stability when LLMs generate actions that violate intended trajectories.

Improvements for AI systems

Improved AI system can maintain stable flocking formations through structured plan negotiation by integrating LLM-driven local planning with an influence-based consensus protocol. This mitigates issues where independently operating LLM-based robots frequently collapse toward a centroid or diverge due to misaligned individual plans.
The system can achieve more robust coordination by replacing unstructured LLM outputs with a structured interaction protocol, specifically by having each robot iteratively refine their plans through a decentralized consensus protocol that accounts for their influence on neighboring robots. This prevents the propagation of hallucinated reasoning, logical inconsistencies, and limited coordination awareness.
The system can ensure reliable execution of movement steps by using LLMs to reason about physical constraints during motion execution. For instance, the LLM can calculate a Direction Vector and scale it by the maximum speed while simultaneously performing Constraint Validation against neighbor positions to avoid overshooting goals, as demonstrated in Figure 14.