用大模型+协调场实现城市低空无人机群高效自适应任务分配
CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios
- 引入大模型理解指令，通过协调场实现去中心化动态任务分配
- 50轮测试显示任务覆盖率高、响应快，能灵活应对环境变化
- 适合复杂城市场景下多类型无人机协同任务的系统设计
随着城市环境中对异构无人机集群执行复杂任务的需求增加，系统设计面临高效语义理解、灵活任务规划以及根据环境变化和任务需求动态调整协调策略的重大挑战。为解决现有方法的局限性，本文提出CoordField，一种用于复杂城市场景下异构无人机群协调的协调场代理系统。该系统中，大语言模型（LLMs）负责解析高层人类指令并转化为可执行的无人机集群命令，如巡逻和目标追踪；随后提出协调场机制，指导无人机运动与任务选择，实现去中心化且自适应的突发任务分配。在二维仿真空间中对不同模型进行了50轮对比测试，实验结果表明，所提系统在任务覆盖、响应时间及对动态变化的适应性方面均表现出色。
原文摘要 · Abstract (English)
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.
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