FM-Planner：基于基础模型引导的自主无人机导航路径规划
FM-Planner: Foundation Model Guided Path Planning for Autonomous Drone Navigation
AI总结：
本文提出FM-Planner，利用基础模型（LLM和VLM）引导无人机路径规划，通过仿真基准测试和真实实验验证其可行性，为自主导航提供实际实现。
摘要：
路径规划是自主无人机运行中的关键组成部分，能够确保其在复杂环境中安全高效地导航。近年来，基础模型（尤其是大型语言模型（LLM）和视觉语言模型（VLM））的进步为机器人领域的增强感知和智能决策开辟了新的机遇。然而，这些模型在全局路径规划中的实际适用性和有效性仍未得到充分探索。本文提出了基础模型引导的路径规划器（FM-Planner），并针对无人机路径规划进行了全面的基准测试研究和实际验证。具体而言，我们首先使用标准化仿真场景系统评估了八种具有代表性的LLM和VLM方法。为了实现有效的实时导航，我们随后设计了一种集成的LLM-视觉规划器，将语义推理与视觉感知相结合。此外，我们通过多种配置下的真实世界实验部署并验证了所提出的路径规划器。我们的研究结果为在真实无人机应用中部署基础模型的优势、局限性和可行性提供了宝贵的见解，并为自主飞行提供了实际实现方案。项目网站：https://github.com/NTU-ICG/FM-Planner。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
