发表机构
GRASP Lab, University of Pennsylvania; Department of Computer Science, University of Southern California（宾夕法尼亚大学GRASP实验室; 南加州大学计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多机器人在受限环境中编队导航问题，提出EFLUX框架，基于几何和大语言模型，对变形与重新配置动作联合推理，经闭环管道转化为航点，实验证明其能实现安全弹性导航，减少死锁与导航失败。
AI 中文摘要
在受限或杂乱环境中运行的多机器人团队必须调整编队几何形状和群组拓扑以穿越复杂障碍物，这需要变形和重新配置两种互补行为。现有方法存在不足，我们提出EFLUX，一种基于几何的智能语言模型代理框架，用于自动和弹性多机器人编队导航。它提取结构化场景表示，通过大语言模型对变形和重新配置动作联合推理，并通过闭环生成、验证和校正管道转化为可执行的每个机器人的航点。仿真和硬件实验表明，EFLUX能在受限环境中实现安全、连续和弹性的编队导航，减少死锁和导航失败。
英文摘要
Multi-robot teams operating in confined or cluttered environments must adapt both their formation geometry and group topology to navigate through complex obstacles. This adaptation requires two complementary behaviors: deformation, where the team continuously reshapes its geometry while remaining connected, and reconfiguration, where robots split into subgroups or merge back into a single formation. Existing methods often model these behaviors independently, connect them through handcrafted rules, or lack explicit geometric criteria for determining when each behavior should be invoked. However, challenging environments may require online changes in formation shape, connectivity, and effective team composition, making decoupled or rule-based approaches prone to suboptimal trajectories and deadlock. We propose EFLUX, a geometry-grounded LLM agentic framework for automatic and elastic multi-robot formation navigation. EFLUX extracts a structured scene representation and uses an LLM to reason jointly over both deformation actions, such as scaling and shearing, and reconfiguration actions, such as splitting and merging. These strategies are then translated into executable per-robot waypoints through a closed-loop generation, verification, and correction pipeline. Simulation and hardware experiments show that EFLUX enables safe, continuous, and elastic formation navigation in constrained environments, reducing deadlock and navigation failures compared with baselines while maintaining coherent multi-robot coordination.
