arXiv cs.AI· Yinghao Zhang, Yalin Liu, Hong-Ning Dai, Yan Zhang, Qubeijian Wang, Bishenghui Tao, Yaohan Li·· 3 小时前AI 评分39
LLM 与优化智能体结合的无人机搜救任务调度方案
A Joint LLM and Optimization Agent Solution for Drone Scheduling in Victim Search Missions
AI 导读
研究者提出一种智能体辅助的无人机调度系统，用于野外的遇难者搜救任务。系统在地面控制站部署 Agent，每个搜索周期由 LLM 推理搜索区域，再由优化器在能量约束下决定无人机派遣方案。仿真结果显示该系统能稳定完成任务，而六个非 LLM 基线均无法完成，直接用 LLM 调度的可靠性也明显更低，源代码已公开。
正文 · 原文
Abstract:Victim search in wilderness areas is time-critical. Drones enable rapid area search, but scheduling them is challenging because victims may continue moving, detection results are uncertain, and effective decisions must combine structured mission information with unstructured contextual clues and domain knowledge. To address this, we propose an agent-aided drone scheduling system. The system employs an agent at the ground control station (GCS) to coordinate multiple drones over successive search cycles. In each cycle, the agent applies a scheduling policy that combines a large language model (LLM) for reasoning about where to search with an optimizer for determining how to dispatch the drones under energy constraints. Simulation results show that the proposed system consistently completes the victim-search mission, whereas none of the six non-LLM baselines does so and direct LLM scheduling is considerably less reliable. The source code is publicly available at this https URL.
| Subjects: | Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET) |
| Cite as: | arXiv:2611.16524 [eess.SY] |
| (or arXiv:2611.16524v1 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16524 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yinghao Zhang [view email]
[v1]
Thu, 12 Nov 2026 09:20:54 UTC (359 KB)
来源：arXiv cs.AI · arxiv.org