CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912494348402688 |
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| author | Zhang, Tengchao Tian, Yonglin Lin, Fei Huang, Jun Süli, Patrik P. Ni, Qinghua Qin, Rui Wang, Xiao Wang, Fei-Yue |
| author_facet | Zhang, Tengchao Tian, Yonglin Lin, Fei Huang, Jun Süli, Patrik P. Ni, Qinghua Qin, Rui Wang, Xiao Wang, Fei-Yue |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00091 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios Zhang, Tengchao Tian, Yonglin Lin, Fei Huang, Jun Süli, Patrik P. Ni, Qinghua Qin, Rui Wang, Xiao Wang, Fei-Yue Robotics Artificial Intelligence 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. |
| title | CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2505.00091 |