FM-Planner: Foundation Model Guided Path Planning for Autonomous Drone Navigation
This work addresses the challenge of enhancing drone navigation with foundation models, offering insights for robotics researchers, but it is incremental as it builds on existing models without introducing a new paradigm.
The paper tackled the problem of applying foundation models like LLMs and VLMs to global path planning for autonomous drones, evaluating eight models in simulations and validating an integrated LLM-Vision planner in real-world experiments to assess feasibility and provide practical implementations.
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.