Zhang, Tengchao (Macau University of Science and Technology), Tian, Yonglin (Institute of Automation, Chinese Academy of Sciences), Lin, Fei (Macau University of Science and Technology), Huang, Jun (Macau University of Science and Technology), Süli, Patrik P. (Obuda University, Budapest, Hungary), Qinghua, Ni (Macau University of Science and Technology), Qin, Rui (Institute of Automation, Chinese Academy of Sciences), WANG, XIAO (Anhui University), Wang, Fei-Yue (Institute of Automation, Chinese Academy of Sciences)

CoordField: Coordination Field for Agentic UAV Task Allocation in Low-Altitude Urban Scenarios

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Abstract

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.