LAPF: LLM-Agent-Based Path Finder for UAV Navigation
A novel framework known as LAPF (LLM-Agent-Based Path Finder) has been introduced for the navigation of autonomous UAVs in expansive outdoor urban settings. This framework, outlined in a paper on arXiv (2608.15175), combines modules for perception, memory, planning, and action within a closed-loop cognitive architecture, enhancing LLM-assisted navigation. It tackles the shortcomings of current optimization, machine learning, and reinforcement learning methods, which typically depend on fixed models or training for specific tasks, thus hindering adaptability and generalization. While recent LLM-assisted techniques provide reasoning abilities, they fall short in agentic features like memory and planning. LAPF seeks to address these issues. The paper also serves as a cross-announcement, suggesting prior presentations. This research is pertinent to the increasing use of UAVs in intricate outdoor scenarios that require intelligent adaptive decision-making.
Key facts
- LAPF is an LLM-Agent-Based Path Finder framework for autonomous UAV navigation.
- The framework integrates perception, memory, planning, and action modules.
- It is designed for town-scale outdoor environments.
- The paper is available on arXiv with identifier 2608.15175.
- The announcement type is 'cross'.
- Existing approaches include optimization-based, ML, and RL methods.
- LLM-assisted approaches lack agentic functionality like memory and planning.
- The research addresses generalization and adaptability in uncertain scenarios.
Entities
Institutions
- arXiv