How to safely navigate many robots using dynamic velocity fields?
MA-DV2F: A Multi-Agent Navigation Framework using Dynamic Velocity Vector Field
November 12, 2024
https://arxiv.org/pdf/2411.06404This paper introduces MA-DV2F, a framework for coordinating multiple vehicles navigating to targets without collisions. It creates separate, dynamically updated velocity vector fields for each vehicle, indicating ideal speed and direction at each map point. These fields consider both target attraction and repulsion from obstacles/other vehicles. This decentralized approach simplifies the problem and enables parallel processing.
Key points for LLM-based multi-agent systems:
- Decentralized control: The independent velocity fields simplify complex interactions, mirroring the independent nature of LLMs in multi-agent scenarios.
- Dynamic adaptation: The fields update based on real-time agent positions, similar to how LLMs can adapt responses based on evolving dialogue context.
- Potential for self-supervised learning: The generated velocity fields can train a GNN, demonstrating a novel approach to training multi-agent systems with minimal labeled data, which is crucial for LLMs.
- Scalability: MA-DV2F scales well to large numbers of agents, which is a critical challenge in LLM-based multi-agent applications.
- Continuous action and state spaces: The framework operates in continuous space, which aligns well with the nature of LLM outputs and the complexities of real-world scenarios.