arXiv 2505.000912025-07-21cs.ROcs.AI

CoordField：低空城市场景中面向智能体无人机任务分配的协调场

CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios

Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology（工程科学系，创新工程学院，澳门科学技术大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University（应用信息与应用数学博士学院，奥布达大学）；School of Artificial Intelligence, Anhui University（人工智能学院，安徽大学）；State Key Laboratory for Management and Control of Complex Systems（复杂系统管理与控制国家重点实验室）

Tengchao Zhang, Yonglin Tian, Fei Lin, Jun Huang, Patrik P. Süli, Qinghua Ni, Rui Qin, Xiao Wang, Fei-Yue Wang

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AI总结：

本文提出 CoordField 协调场智能体系统，利用大语言模型解析指令并结合协调场机制引导异构无人机集群在低空城市场景中实现去中心化、自适应的任务分配，实验证明其在任务覆盖率、响应时间和动态适应性上表现优越。

摘要：

随着异构无人机集群在城市环境中执行复杂任务的需求日益增长，系统设计目前面临重大挑战，包括高效的语义理解、灵活的任务规划，以及根据不断变化的环境条件和持续变化的任务需求动态调整协调策略的能力。为解决现有方法的局限性，本文提出了 CoordField，一种用于在复杂城市场景中协调异构无人机集群的协调场智能体系统。在该系统中，大语言模型（LLMs）负责解释高层人类指令，并将其转换为无人机集群可执行的命令，例如巡逻和目标跟踪。随后，提出了一种协调场机制来引导无人机运动和任务选择，从而实现突发任务的去中心化和自适应分配。在二维仿真空间中，针对不同模型共进行了 50 轮对比测试以评估其性能。实验结果表明，所提出的系统在任务覆盖率、响应时间和对动态变化的适应性方面均取得了优越的性能。

英文摘要：

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.

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。