用自然语言指挥无人机完成从短程巡逻到远程调度的全流程任务
Next-Generation LLM for UAV: From Natural Language to Autonomous Flight
- 通过自然语言解析、路径规划与控制平台实现多尺度无人机任务自动化
- 在三类真实场景中验证了跨尺度任务执行可行性，涵盖巡检、投递与中继飞行
- 提出五级自动化分级体系，为未来智能飞行系统提供演进蓝图
随着大语言模型（LLM）在自动化领域的快速发展，其在无人飞行器（UAV）操作中的应用日益受到关注。当前研究主要局限于小型无人机的局部功能，如玩具无人机的路径规划，缺乏对中长距离、真实运行环境下的中大型无人机系统的全面探索。更大规模的无人机平台面临严格的机场起降要求、复杂的监管框架以及更高的任务期望。本文提出下一代无人机大语言模型系统（NeLV），构建了一个从自然语言指令到多尺度无人机任务执行的完整自动化路线图。该系统包含五个关键技术组件：(i) LLM-as-Parser 负责指令理解，(ii) Route Planner 确定兴趣点（POI），(iii) Path Planner 生成航路点，(iv) Control Platform 实现可执行轨迹，(v) UAV 监控。通过三个代表性应用场景验证了系统的可行性：多无人机巡逻、多兴趣点投递、多跳中继转移。此外，我们建立了一个五级自动化分类体系，从当前的 LLM-as-Parser（Level 1）逐步演进至全自主的 LLM-as-Autopilot（Level 5），明确各阶段的技术需求与研究挑战。
原文摘要 · Abstract (English)
With the rapid advancement of Large Language Models (LLMs), their capabilities in various automation domains, particularly Unmanned Aerial Vehicle (UAV) operations, have garnered increasing attention. Current research remains predominantly constrained to small-scale UAV applications, with most studies focusing on isolated components such as path planning for toy drones, while lacking comprehensive investigation of medium- and long-range UAV systems in real-world operational contexts. Larger UAV platforms introduce distinct challenges, including stringent requirements for airport-based take-off and landing procedures, adherence to complex regulatory frameworks, and specialized operational capabilities with elevated mission expectations. This position paper presents the Next-Generation LLM for UAV (NeLV) system -- a comprehensive demonstration and automation roadmap for integrating LLMs into multi-scale UAV operations. The NeLV system processes natural language instructions to orchestrate short-, medium-, and long-range UAV missions through five key technical components: (i) LLM-as-Parser for instruction interpretation, (ii) Route Planner for Points of Interest (POI) determination, (iii) Path Planner for waypoint generation, (iv) Control Platform for executable trajectory implementation, and (v) UAV monitoring. We demonstrate the system's feasibility through three representative use cases spanning different operational scales: multi-UAV patrol, multi-POI delivery, and multi-hop relocation. Beyond the current implementation, we establish a five-level automation taxonomy that charts the evolution from current LLM-as-Parser capabilities (Level 1) to fully autonomous LLM-as-Autopilot systems (Level 5), identifying technical prerequisites and research challenges at each stage.
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