TACOS：多无人机系统的任务无关协调器
TACOS: Task Agnostic COordinator of a multi-drone System
AI总结：
TACOS通过大型语言模型实现多无人机系统的高层自然语言控制，整合自然语言接口、智能协调器和自主代理，实现灵活的任务分配与实时协调。
摘要：
当单一飞行员负责管理多无人机系统时，任务可能需要不同层次的自主性，从对单个无人机的直接控制，到群体层面的协调，再到完全自主的群体行为以完成高级任务。启用这种灵活的交互需要一个支持多种共享自主模式的框架。随着语言模型在推理和规划方面持续改进，它们为这类系统提供了天然的基础，通过直观的语言接口减少飞行员的工作负担，通过任务委托实现高级任务分配。在本文中，我们提出了TACOS（多无人机系统的任务无关协调器），这是一个统一的框架，通过大型语言模型（LLMs）实现多无人机系统的高层自然语言控制。TACOS将三种关键能力整合到单一架构中：一种一对一的自然语言接口用于直观的用户交互，一个智能协调器用于将用户意图转化为结构化任务计划，以及一个自主代理用于执行计划并与现实世界交互。TACOS允许LLM与可执行API库交互，将语义推理与实时多机器人协调结合起来。我们在一个现实世界多无人机系统上展示了该系统，并通过消融研究评估了每个模块的贡献。
英文摘要：
When a single pilot is responsible for managing a multi-drone system, the task may demand varying levels of autonomy, from direct control of individual UAVs, to group-level coordination, to fully autonomous swarm behaviors for accomplishing high-level tasks. Enabling such flexible interaction requires a framework that supports multiple modes of shared autonomy. As language models continue to improve in reasoning and planning, they provide a natural foundation for such systems, reducing pilot workload by enabling high-level task delegation through intuitive, language-based interfaces. In this paper we present TACOS (Task-Agnostic COordinator of a multi-drone System), a unified framework that enables high-level natural language control of multi-UAV systems through Large Language Models (LLMs). TACOS integrates three key capabilities into a single architecture: a one-to-many natural language interface for intuitive user interaction, an intelligent coordinator for translating user intent into structured task plans, and an autonomous agent that executes plans interacting with the real world. TACOS allows a LLM to interact with a library of executable APIs, bridging semantic reasoning with real-time multi-robot coordination. We demonstrate the system on a real-world multi-drone system, and conduct an ablation study to assess the contribution of each module.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
