用大模型提升无人机路径规划，兼顾语义理解与实时导航
FM-Planner: Foundation Model Guided Path Planning for Autonomous Drone Navigation
- 融合大语言模型与视觉模型进行语义感知与路径推理
- 在仿真与真实场景中验证了规划效果与实时性
- 为无人机自主飞行提供可落地的大模型解决方案
路径规划是无人机自主运行的核心，决定其在复杂环境中的安全高效通行。近年来，大语言模型（LLMs）和视觉语言模型（VLMs）在机器人感知与智能决策方面展现出潜力，但在全局路径规划中的实际应用仍不明确。本文提出基础模型引导的路径规划器（FM-Planner），并开展系统性基准测试与实际验证。首先，在标准仿真场景中评估八种代表性LLM与VLM方法；为实现实时导航，设计集成式LLM-Vision规划框架，结合语义推理与视觉感知；最后通过多配置真实实验部署并验证该规划器。研究结果揭示了基础模型在真实无人机应用中的优势、局限与可行性，为自主飞行提供了可实践的技术方案。项目主页：https://github.com/NTU-ICG/FM-Planner。
原文摘要 · Abstract (English)
Path planning is a critical component in autonomous drone operations, enabling safe and efficient navigation through complex environments. Recent advances in foundation models, particularly large language models (LLMs) and vision-language models (VLMs), have opened new opportunities for enhanced perception and intelligent decision-making in robotics. However, their practical applicability and effectiveness in global path planning remain relatively unexplored. This paper proposes foundation model-guided path planners (FM-Planner) and presents a comprehensive benchmarking study and practical validation for drone path planning. Specifically, we first systematically evaluate eight representative LLM and VLM approaches using standardized simulation scenarios. To enable effective real-time navigation, we then design an integrated LLM-Vision planner that combines semantic reasoning with visual perception. Furthermore, we deploy and validate the proposed path planner through real-world experiments under multiple configurations. Our findings provide valuable insights into the strengths, limitations, and feasibility of deploying foundation models in real-world drone applications and providing practical implementations in autonomous flight. Project site: https://github.com/NTU-ICG/FM-Planner.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。