UAV-VLN: End-to-End Vision Language guided Navigation for UAVs

• URL: http://arxiv.org/abs/2504.21432v1
• Date: Wed, 30 Apr 2025 08:40:47 GMT
• Title: UAV-VLN: End-to-End Vision Language guided Navigation for UAVs
• Authors: Pranav Saxena, Nishant Raghuvanshi, Neena Goveas,
• Abstract summary: A core challenge in AI-guided autonomy is enabling agents to navigate realistically and effectively in previously unseen environments.<n>We propose UAV-VLN, a novel end-to-end Vision-Language Navigation framework for Unmanned Aerial Vehicles (UAVs)<n>Our system interprets free-form natural language instructions, grounds them into visual observations, and plans feasible aerial trajectories in diverse environments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A core challenge in AI-guided autonomy is enabling agents to navigate realistically and effectively in previously unseen environments based on natural language commands. We propose UAV-VLN, a novel end-to-end Vision-Language Navigation (VLN) framework for Unmanned Aerial Vehicles (UAVs) that seamlessly integrates Large Language Models (LLMs) with visual perception to facilitate human-interactive navigation. Our system interprets free-form natural language instructions, grounds them into visual observations, and plans feasible aerial trajectories in diverse environments. UAV-VLN leverages the common-sense reasoning capabilities of LLMs to parse high-level semantic goals, while a vision model detects and localizes semantically relevant objects in the environment. By fusing these modalities, the UAV can reason about spatial relationships, disambiguate references in human instructions, and plan context-aware behaviors with minimal task-specific supervision. To ensure robust and interpretable decision-making, the framework includes a cross-modal grounding mechanism that aligns linguistic intent with visual context. We evaluate UAV-VLN across diverse indoor and outdoor navigation scenarios, demonstrating its ability to generalize to novel instructions and environments with minimal task-specific training. Our results show significant improvements in instruction-following accuracy and trajectory efficiency, highlighting the potential of LLM-driven vision-language interfaces for safe, intuitive, and generalizable UAV autonomy.

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