LAPF: LLM-Agent-Based Path Finder Using the UAVScenes Dataset
Uncrewed aerial vehicles (UAVs) are increasingly deployed for autonomous navigation in complex outdoor environments, where dynamic conditions and mission requirements require intelligent adaptive decision-making. Existing optimization-based, Machine Learning (ML), and Reinforcement Learning (RL) approaches often rely on predefined models or task-specific training, limiting their generalization and adaptability in uncertain scenarios. Recent Large Language Model (LLM)-assisted approaches offer promising reasoning capabilities but remain constrained by limited agentic functionality, including insufficient memory, planning, and tool interaction mechanisms.This paper proposes an LLM-Agent-Bas...
| Authors | Yousef Emami, Mohammadhossein Homaei, Hao Zhou et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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