CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios
This addresses efficient task allocation for UAV swarms in low-altitude urban scenarios, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of coordinating heterogeneous UAV swarms in complex urban environments by proposing CoordField, a system that uses large language models for instruction interpretation and a coordination field mechanism for decentralized task allocation, achieving superior performance in task coverage, response time, and adaptability in 50 rounds of comparative testing.
With the increasing demand for heterogeneous Unmanned Aerial Vehicle (UAV) swarms to perform complex tasks in urban environments, system design now faces major challenges, including efficient semantic understanding, flexible task planning, and the ability to dynamically adjust coordination strategies in response to evolving environmental conditions and continuously changing task requirements. To address the limitations of existing methods, this paper proposes CoordField, a coordination field agent system for coordinating heterogeneous drone swarms in complex urban scenarios. In this system, large language models (LLMs) is responsible for interpreting high-level human instructions and converting them into executable commands for the UAV swarms, such as patrol and target tracking. Subsequently, a Coordination field mechanism is proposed to guide UAV motion and task selection, enabling decentralized and adaptive allocation of emergent tasks. A total of 50 rounds of comparative testing were conducted across different models in a 2D simulation space to evaluate their performance. Experimental results demonstrate that the proposed system achieves superior performance in terms of task coverage, response time, and adaptability to dynamic changes.