TACOS: Task Agnostic COordinator of a multi-drone System
This addresses the challenge of intuitive human-drone interaction for operators in fields like surveillance or delivery, though it appears incremental as it builds on existing LLM capabilities for robotics.
The paper tackles the problem of enabling flexible, high-level natural language control for multi-drone systems managed by a single pilot, resulting in a unified framework called TACOS that integrates natural language interfaces, task planning, and autonomous execution to reduce pilot workload.
When a single pilot is responsible for managing a multi-drone system, the task demands 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 in real-world multi-drone system and conduct an ablation study to assess the contribution of each module.