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

Created by
  • Haebom

Author

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

Outline

In this paper, we propose CoordField, a novel system for efficiently coordinating heterogeneous unmanned aerial vehicles (UAVs) swarms in complex urban environments. CoordField leverages a large-scale language model (LLM) to translate high-level human commands into executable commands (e.g., patrolling, target tracking) for UAV swarms, and distributes and adaptively allocates UAV movements and task selections through a coordination field mechanism. Through 50 comparative experiments in a 2D simulation environment, we demonstrate that CoordField outperforms existing methods in terms of task coverage, response time, and adaptability to dynamic changes.

Takeaways, Limitations

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Takeaways:
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We present a novel system for efficient coordination of heterogeneous UAV swarms in complex urban environments.
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Present an effective method to translate high-level human commands into executable instructions using LLM.
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Distributed and adaptive task allocation and UAV control possible through the coordination field mechanism.
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The experimental results verify the excellent performance of the proposed system.
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Limitations:
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Currently, it has only been evaluated in a 2D simulation environment, so performance verification in a real environment is required.
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Robustness verification against various types of obstacles and unpredictable situations is required.
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Since it depends on the performance of LLM, there is a possibility that the limitations of LLM may affect the system performance.
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Lack of experimental results on real UAV swarms.
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