How can LLMs enable safe, fast, multi-robot navigation?
LIVENET: Robust, Minimally Invasive Multi-Robot Control for Safe and Live Navigation in Constrained Environments
December 9, 2024
https://arxiv.org/pdf/2412.04659This paper introduces LIVENET, a decentralized neural network controller for navigating multiple robots in confined spaces, like doorways and intersections, without collisions or deadlocks. It emphasizes simultaneous safety and liveness, mimicking human-like yielding behavior.
Key points for LLM-based multi-agent systems:
- Decentralized control: Each agent operates independently based on local observations, mirroring the distributed nature of many LLM agents.
- Safety and liveness guarantees: LIVENET formally addresses both collision avoidance (safety) and progress towards goals (liveness), crucial for robust multi-agent applications.
- Minimally invasive control: Agents adjust speed rather than trajectory for conflict resolution, promoting smooth and efficient interactions in collaborative LLM agent scenarios.
- Robustness: LIVENET handles variations in environment and agent configurations, a desirable trait for LLM agents deployed in diverse, dynamic contexts.
- Potential for end-to-end training: The neural network architecture could be adapted for end-to-end training with LLMs, potentially enabling more complex and adaptive behaviors in multi-agent settings.
- Relevance of CBFs: The use of Control Barrier Functions (CBFs) provides a mathematically sound framework for ensuring safety and liveness, applicable to various LLM agent control problems.